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Generative Design to Reduce Embodied GHG Emissions of High-Rise Buildings
by
Julian Zaraza
A thesis submitted in conformity with the requirements for the degree of Master of Applied Science in Civil Engineering
Department of Civil and Mineral Engineering
University of Toronto
© Copyright by Julian Zaraza 2020
ii
Generative Design to Reduce Embodied GHG Emissions of High-
Rise Buildings
Julian Zaraza
Master of Applied Science in Civil Engineering
Department of Civil and Mineral Engineering
University of Toronto
2020
Abstract
Although countries have reduced their total greenhouse gas emissions by improving energy and
transportation policies, the contribution of the building sector has been widely overlooked.
Embodied emissions (EE) are particularly important since they are released upfront rather than
over the lifespan of buildings, making them critical for accomplishing the 2030 Canadian
emission reduction targets. Accordingly, this study developed a tool to reduce EE at the
conceptual stage of high-rise residential buildings. The tool also incorporates goals and
constraints that are inherent to conceptual building design, such as maximizing site use, views,
and complying with building codes. In a case study, it was able to achieve a 7% reduction in EE
when compared to a sub-optimal solution. This research elucidated the potential of using
generative design in early-stage design, proposed novel systems for the generation and
evaluation of design alternatives, and delivered GenGHG, a ready-to-use, open-source tool for
conceptual building design.
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Acknowledgments
The author would like to thank Prof. Daniel Posen and Prof. Brenda McCabe of the Civil &
Mineral Engineering Department at the University of Toronto for their insightful and critical
contributions to this research. Also, for the opportunity of being under their supervision and their
support in the development of an unconventional research project with a major focus on
technology.
Additionally, the author acknowledges the support of EllisDon, BASF Canada and WSP, as well
as matching funds from the Natural Sciences Research Council of Canada (CRDPJ 508960) and
the Ontario Centres of Excellence (TargetGHG 27943).
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Table of Contents
Acknowledgments.......................................................................................................................... iii
Table of Contents ........................................................................................................................... iv
List of Tables ................................................................................................................................. vi
List of Figures ............................................................................................................................... vii
List of Appendices ...........................................................................................................................x
Chapter 1 Motivation .......................................................................................................................1
1.1 Introduction ..........................................................................................................................1
1.2 Background ..........................................................................................................................4
1.2.1 Embodied Emissions (EE) in High-Rise Residential Buildings ..............................4
1.2.2 LCA in Conceptual Design Stages ..........................................................................6
1.2.3 Generative Design (GD) ..........................................................................................7
1.3 Scope and Objectives ...........................................................................................................8
Chapter 2 Methods .........................................................................................................................11
Research methods......................................................................................................................11
2.1 Context Analysis ................................................................................................................11
2.2 Generative Design Tool .....................................................................................................14
2.3 Design Generation .............................................................................................................15
2.4 Design Evaluation ..............................................................................................................20
2.4.1 Embodied Emissions (EE) .....................................................................................22
2.4.2 Floor Area Ratio (FAR) .........................................................................................28
2.4.3 Architectural Score (ARCH)..................................................................................29
2.5 Design Constraints .............................................................................................................30
Chapter 3 Results and Discussion ..................................................................................................33
Results .......................................................................................................................................33
3.1 Context Analysis ................................................................................................................33
v
3.2 Generative Design Tool (GenGHG) ..................................................................................36
3.3 Demonstration in a Case Study ..........................................................................................40
3.4 Design Alternatives for a Case Study ................................................................................41
3.5 Analysis and Discussion of Results ...................................................................................44
3.6 Limitations and Future Work .............................................................................................48
Chapter 4 Conclusions ...................................................................................................................52
References ......................................................................................................................................54
Appendices .....................................................................................................................................63
vi
List of Tables
Table 1. Building LCA tools as of June 2020, adapted from [82] and [14]. .................................. 9
Table 2. Summary of ready to use commercial GD applications for conceptual building design as
of January 2020 ............................................................................................................................. 10
Table 3. Generative design tool inputs ......................................................................................... 15
Table 4. Conceptual design assumptions for design generation ................................................... 16
Table 5. Geometry system steps for design generation ................................................................ 17
Table 6. Ranges of the geometry system variable parameters for design optimization ............... 19
Table 7. Resources used for documenting goals and constraints. ................................................. 21
Table 8. Conceptual design assumptions and scope for design evaluation .................................. 21
Table 9. Specifications and data sources used for the estimation of EE. ...................................... 24
Table 10. Consolidated LCA factors per material ........................................................................ 25
Table 11. Design constraints used for design evaluation. ............................................................. 31
vii
List of Figures
Fig. 1. Proposed buildings according to year of completion and structural material in the City of
Toronto. Data extracted in 2019 from the Council of Tall Buildings and Urban Habitat (CTBUH)
public database [18]. Composite structures are those that entail a mixed use of concrete and steel
structural members for both the vertical and lateral resisting system. ............................................ 2
Fig. 2. High-level research methods which align with the structure of the following sections. .. 11
Fig. 3. 3D visualizations of the nine case studies under analysis plotted according to their height
and gross residential area. All the case studies are located within the boundaries of the downtown
and central waterfront area as defined in the city of Toronto ‘How Does the City Grow?’ report
[47]. ............................................................................................................................................... 12
Fig. 4. Building sections. The tower section contains mostly residential units. The podium
section is commonly used for amenities and common-use services such as cleaning and
mechanical rooms. The underground section is mostly for parking and storage.......................... 13
Fig. 5. GD system data flow diagram containing the methods and outputs in squared boxes, and
the optimization steps and the platforms used in rounded boxes. The data flow arrows indicate
the direction of the information being passed between steps and software platforms.................. 14
Fig. 6. Flow diagram describing the geometry system steps and inputs. The steps are listed in
Table 5. The calculated values, i.e. subprocesses are described in equations 1, 2, 3, and 4. The
ranges of the system parameters (variables) are listed in Table 6. ............................................... 18
Fig. 7. Design evaluation metrics, goals, and constraints. The data sources for each are listed in
Table 7 and Table 8....................................................................................................................... 20
Fig. 8. High-level system scope for the estimation of EE. The stages are defined per the ISO
14040 standard [93]. The construction stage includes excavation of underground levels,
installation of structural and envelope elements, and transportation of construction materials and
excess soil. Fig. 9 expands on the details of the system scope. ................................................... 22
Fig. 9. Detailed system scope for the estimation of EE. Only structural and envelope
components are included. The façade ratios were extracted from the context analysis. The
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construction stage includes excavation and installation. The estimation of on-site fuel use is
based on a model proposed in [94]. The installation includes concrete pumping and hoisting of
façade components, data sources are shown in Table 9. ............................................................... 23
Fig. 10. Data flow diagram for the estimation of EE. .................................................................. 26
Fig. 11. Scope for the estimation of the floor area ratio (FAR). Hallways and structural
components area are both included in the calculation of the total residential floor area. ............. 28
Fig. 12. Illustration of the calculation process for the unobstructed views and irregularity scores.
....................................................................................................................................................... 30
Fig. 13. Box-and-whisker plot showing EE per building section in case studies. Normalized by
square meters if floor space in (1) and by cubic meters of building space in (2). Results based on
a sample of 9 buildings in Toronto. Whiskers plotted for maximum 1.5 IQR (interquartile range
....................................................................................................................................................... 34
Fig. 14. Sankey diagram showing the average distribution of EE seen in case studies. Results
based on a sample of 9 buildings in Toronto. 1 Gg (gigagram) is equivalent to 1 kt (one thousand
metric tonnes)................................................................................................................................ 35
Fig. 15. GenGHG’s visual programming flow. Each group of codes has a specific functionality
within the major steps: inputs, design generation, design evaluation, outputs, and visualization.
This functionality is stated in the tittle of the group. Objects in the 3D visualization can be
hidden or shown according to the user’s preference. A high-resolution snapshot of the code flow
is available in the following link: https://julianzaraza.page.link/genghgcapture .......................... 37
Fig. 16. GenGHG’s user interface in Refinery (Autodesk®). The create study window with the
optimization setup (left) includes the objectives to be optimized, inputs to varied, and constraints
for outputs. The generation settings for the NSGA-II can be configured at the end of the window.
The design exploration window includes 3D visualizations for all the generated solutions in
addition to a parallel coordinates plot showing the ranges of the parameters varied and a
dedicated view for the outputs of the selected alternative. ........................................................... 38
Fig. 17. 3D (A) and plan (B) visualizations of the selected site (highlighted in red). ................. 41
ix
Fig. 18. Parallel coordinates diagram showing the ranges of a subset of metrics for the randomly
generated design alternatives. ....................................................................................................... 42
Fig. 19. Design exploration results including randomly generated solutions (DS) and sample of
non-dominated solutions (NDS). A1, A2, and A3 solutions are described in ##. ........................ 43
Fig. 20. 3D visualizations of the subset of non-dominated solutions (NDS) for the selected site.
....................................................................................................................................................... 44
Fig. 21. Comparison between best and worst performing alternatives while maintain the same
GRA. ............................................................................................................................................. 45
Fig. 22. Façade area and floor plate area versus EE. ................................................................... 46
x
List of Appendices
Appendix 1 .................................................................................................................................... 63
Appendix 2 .................................................................................................................................... 63
Appendix 3 .................................................................................................................................... 64
Appendix 4 .................................................................................................................................... 65
Appendix 5 .................................................................................................................................... 66
Appendix 6 .................................................................................................................................... 66
Appendix 7 .................................................................................................................................... 67
Appendix 8 .................................................................................................................................... 67
Appendix 9 .................................................................................................................................... 67
Appendix 10 .................................................................................................................................. 68
Appendix 11 .................................................................................................................................. 68
Appendix 12 .................................................................................................................................. 68
Appendix 13 .................................................................................................................................. 69
Appendix 14 .................................................................................................................................. 69
Appendix 15 .................................................................................................................................. 69
Appendix 16 .................................................................................................................................. 70
Appendix 17 .................................................................................................................................. 70
Appendix 18 .................................................................................................................................. 70
Appendix 19 .................................................................................................................................. 71
Appendix 20 .................................................................................................................................. 72
xi
Appendix 21 .................................................................................................................................. 73
Appendix 22 .................................................................................................................................. 74
1
Chapter 1 Motivation
1.1 Introduction
Environmental impacts such as eutrophication, acidification, ozone depletion, smog formation,
and climate change have created the need to regulate anthropogenic activities. Special attention
has been placed on greenhouse gases (GHGs) since their accumulation in the atmosphere is the
primary cause of climate change [1]. As a worldwide strategic response, 195 countries have
committed to reducing yearly emissions [2]. Canada pledged to decrease its GHG emissions by
30% below 2005 levels by 2030 [3]. Although Canada only generates 1.6% of global GHG
emissions [4], it stands among the top per capita emitters [5].
The construction and operation of buildings are important contributors to global GHG emissions,
representing nearly 39% of global CO2 emissions [6]. Building life cycle GHG emissions come
from three main sources: embodied emissions, which result from the production, transportation,
and installation of construction materials; energy consumption during daily operation; and
decommissioning. The operation of buildings accounts for 19% and 11.5% of global [7] and
Canadian [8] GHG emissions. Thus, reducing operational energy has been an important goal for
the building industry over the last decade [9]. In developed economies, although building
operations have historically been the largest contributor to GHG emissions, embodied emissions
(EE) have been gaining in importance. This is due to reductions in operational emissions (OE)
achieved through the decline in the carbon intensity of electricity and improvements in building
energy systems [10–12]. In Ontario, the carbon intensity of the electricity grid decreased from
175 to 58 gCO2-eq/kWh over the period from 2010 to 2018 [12]. Further, the majority of EE are
emitted upfront while OE are produced over the lifespan of the building (i.e. 50 years) which
makes the reduction of EE critical considering the 2030 target of the Canadian emissions
reduction plan. This target falls in line with the Paris agreement which seeks to achieve a global
temperature rise below 2 degrees Celsius above pre-industrial levels over the upcoming century
[2].
2
The share of EE in the life cycle emissions of a building ranges between 9% and 50% in North
American projects with a 60-year lifespan, according to a 2013 study [13]. This variation is
associated with factors such as construction materials, structure type, functionality, energy
performance, and lifespan considerations [14]. A larger EE share is typical of high-rise buildings
[15] (i.e. those taller than 50 m [16]) given their reliance on carbon-intensive materials such as
concrete, steel, and glass, whose production accounted for 11% of the global CO2 emissions in
2019 [6]. In the North American context, 73% of such high-rise projects use concrete as the
main structural material [17]; in Toronto, Canada, the proportion is 95% (see Fig. 1) [18].
Concrete is the preferred alternative due to its cost-effectiveness, well-established building
practices, and local availability in comparison to other high-strength materials.
Fig. 1. Proposed buildings according to year of completion and structural material in the City
of Toronto. Data extracted in 2019 from the Council of Tall Buildings and Urban Habitat
(CTBUH) public database [18]. Composite structures are those that entail a mixed use of
concrete and steel structural members for both the vertical and lateral resisting system.
High-rise residential towers in Toronto are characterized by a high window to wall ratio (above
60%) which involves extensive use of aluminum-framed glazed façades [19]. These produce
70% higher EE rates per m2 of façade area when compared to traditional steel-framed exterior
walls according to a recent study based on North American data [20]. Despite the considerable
difference in EE, highly glazed façades are likely to continue being the preferred option:
developers meet the increasing demand for new residential spaces by decreasing the average
3
floor area per apartment while incorporating translucent facades [21,22]. Although the size is
smaller, potential owners are willing to compensate living space for added visual range and
building aesthetics [23, p. 53]. Thus, highly glazed concrete-framed buildings are expected to
stay as the standard high-rise residential building type in Toronto, and ways to reduce their EE
need to be developed.
The traditional method for reducing EE in new building designs involves the use of life cycle
assessment (LCA) tools for the selection of less carbon-intensive materials relative to industry
standard baselines [24–26]. In industry practice, this strategy is typically applied in late design
stages when material specifications are well defined [27]. While the process can reduce EE, it is
limited in that the building design is largely fixed. In contrast, considering EE in early design
stages allows designers to evaluate diverse building shapes, ultimately enabling the use of design
optimization strategies. This approach is more relevant for projects with limited construction
material options such as Toronto’s high-rise condominiums, 72% of which are located in the
waterfront and downtown core [28] where highly glazed concrete buildings are the predominant
choice [29,30]. Hence, this study focuses on the reduction of EE through building shape
optimization rather than material selection.
With the development of the genetic algorithm (GA) in 1975 [31], numerous studies have
proposed computer-aided strategies for building design optimization. However, EE have been
overlooked due to their novelty and the prevalence of other metrics, such as cost, floor area,
structural efficiency, and energy performance [32–34]. Further, the strategies proposed are
disconnected from current architectural designs, as they rely on orthogonal, box-like, building
simplifications [32,35–38]. In other studies, the outputs are unconstrained building shapes that
are not structurally feasible with current construction technologies [39,40]. The generation of
more complex, structurally feasible, and closer to reality, building shapes has been expedited by
visual programming interfaces, such as Dynamo [41] and Grasshopper [42], due to their ability
to execute generative design studies within a controlled design space [43]. Generative design
(GD) is a computational technique that automatically creates, evaluates, and filters large numbers
of design alternatives [36,44–46]. To allow the exploration of the design space based on
aesthetics and geometry-dependent metrics (e.g., views, areas, material use) GD-able interfaces
are equipped with comprehensive modules for visualization and geometric calculations. This
4
study relies on GD because of the multidisciplinary and aesthetics-driven nature of early stage
building design [47].
Toronto’s population is expected to grow by 36% over the period from 2019 to 2041 [22, p. 6],
which will drive the demand for new residential spaces. The downtown and waterfront are likely
to support intensive high-rise residential development [22, p. 7], accounting for 37% of the units
currently in the development pipeline (i.e. 107,315 out of 290,039 units) [48]. The city faces
unprecedented expected demand for new buildings and the carbon-intensive construction
materials that they require, lacks tools that consider EE in the early design stages, and must cope
with the disconnect between optimization strategies and current architectural design practices.
Accordingly, this study proposes a GD tool that can minimize EE while integrating industry
standard goals, constraints, and key conceptual design parameters. The intended users are the
developers of highly glazed tall residential concrete buildings in downtown Toronto, Canada.
Although the specific geographical scope, the modular nature of the proposed system enables its
use on other global contexts if data sources are properly adjusted.
1.2 Background
1.2.1 Embodied Emissions (EE) in High-Rise Residential Buildings
GHG emissions are commonly quantified using the 100-year global warming potential metric
(GWP100) which is expressed in kg of CO2 equivalent (kgCO2-eq). GWP100 represents the time-
integrated global mean radiative forcing of 1 kg of any GHG relative to that of 1 kg of CO2 [49].
This study uses GWP100 for measuring all building-generated GHG emissions, specifically EE.
The EE of North American concrete-framed buildings have been found to range between 150
and 600 kgCO2-eq per m2 of floor area, a three-fold variation range [50]. Likewise, the
contribution of EE to the total life cycle GHG emissions of buildings has been found to be highly
variable as LCA results depend on time-sensitive factors, such as the carbon intensity of the
electricity grid [12]; the efficiency of supply chains for the manufacturing and transportation of
construction materials; and the energy performance of buildings which is driven by local
regulations [51]. An example of this is illustrated in a 2020 study, where the total GHG
emissions of 235 buildings were assessed, concluding that building life cycle related GHG
emissions have been decreasing due to new energy standards for building operations [52]. The
5
study determined that the contribution of EE approaches to 33% considering contemporary
building practices (i.e. 2015 to 2020) and to 50% in low energy consumption buildings with a
50-year lifespan consideration. In ‘net-zero’ buildings the EE share was close to 90%. Overall,
residential buildings were found to be 30% less carbon-intensive than office buildings when
considering current energy standards.
In terms of building components, structural elements account for the highest proportion of EE:
between 60 and 90% in concrete and steel-framed buildings [53,54]. Therefore, many studies
have determined the contribution of the most popular structural materials, i.e. concrete and steel,
to the total GHG impact EE of high-rise buildings; two studies, [55] and [56], compared the
environmental performance of reinforced concrete (RC), steel, and composite-based structural
systems on different high-rise building scenarios. In the first study, 36 scenarios were proposed
by varying structural attributes, such as height, structural material, and floor and outrigger
system. In the second study, 96 scenarios were considered, resulting from varying the same
parameters as in [55] but adding more complexity to the types of structural systems. The results
from both studies suggest that RC-based structures are less carbon and energy intensive than
their counterparts: 43-88% in [55], and 25-30% in [56].
Timber structures excel in comparison to concrete and steel ones; cross laminated timber
structures were found less EE intensive by 9-48% [57] and 56-72% [58] than their concrete-
framed counterparts, corresponding to 18 to 120 fewer kgCO2-eq per m2. Although the majority of
timber buildings do not exceed the 50 m height threshold [59], studies have determined that
timber towers of over 100 meters are structurally feasible if the appropriate technologies are
developed [60]. However, the timber industry is still far from reaching this point due to the
technological challenges involved and the slow adoption of novel design strategies in building
codes [59]. In contrast, concrete will likely keep its high market share in residential projects due
to the highly competitive market that it is part of, and the long-standing standards and building
codes that support its use. Almost three-quarters of North American high-rise projects use
concrete as the main structural material [17]; in Toronto, Canada, the proportion is 95% [18].
After structural members, the building envelope components account for the second largest share
of EE in buildings [61]. Window wall (WW) systems have been extensively used as the preferred
façade assembly in tall residential buildings. However, WW systems produce one of the highest
6
EE per façade area in comparison to other envelope assemblies, corresponding to a EE intensity
of 152 kgCO2-eq /m2 for a double glazed aluminum window in comparison to 92 kgCO2-eq /m
2 for
an exterior steel stud wall with metal cladding and EPS as continuous insulation, according to a
recent study with Toronto-specific data [19]. Aluminum mullions are the main contributor to the
high EE rates seen in WW systems because of the large energy requirements during the
production of aluminum alloys [62]. Consequently, aluminum has been found to have
substantially higher EE than other framing materials such as carbon steel and timber [63].
However, aluminum is the material of preference for WW frames in high-rise buildings [64]
because of its favorable cost to performance ratio [65]; it has been classified as one of the
commercially available materials with the highest compressive strength to density ratio,
durability, resistance of corrosion, and ductility [65].
Although the contribution of the envelope to the building total EE (~12%) is lower than that of
structural components (~70%) [66], the scale of high-rise buildings makes the consideration of
the building envelope relevant as minimal design changes may significantly impact total EE.
Further, the production of aluminum alone accounted for 1.5% of global CO2 emissions in 2019
[6,67]; a recent study assessed the global life cycle GHG emissions of primary aluminum
production to be 1.2 Gt CO2-eq [62]. The scope of the present study comprises structural and
façade components exclusively, representing close to ~80% of the total EE of high-rise
buildings. The remaining ~20% is associated to interior finishes and mechanical, electrical, and
plumbing elements, according to a 2018 study on a 17-storey building [66].
1.2.2 LCA in Conceptual Design Stages
In building design, the most influential decisions are made during the conceptual phase, where
parameters such as building shape and functionality are defined. These parameters are heavily
connected to stakeholders goals and designers perspectives. A benchmarking study of the
conceptual design phase of high-rises [47], based on case studies and surveys with US-based
industry professionals, found that aesthetics, site views, and area efficiency were the only
parameters considered as crucial for building conceptualization. Other metrics, including
structural performance, energy efficiency, and budget constraints, were not considered as
important despite their influence on project feasibility. The study concluded that designs are
7
being selected based on a very limited set of metrics, as multiple criteria are very difficult to
consider early in the design process.
Most building LCA tools are not used during these early stages, even though some include
modules for conceptual design [14,68,69]. LCA in building design has been limited to informing
professionals about the impacts of their material choices once the project is already defined [70],
leading to a narrow space for improvement.
describes publicly available building LCA software and their early stage modules. Tools such as
Athena’s Impact Estimator® and OneClick LCA® include modules for early stage estimation of
EE but lack advanced visualization capabilities that are critical in this phase. Architects prioritize
visualization as most of them already use 3D and 2D modeling software to represent their early
design ideas parallel to hand drawings [47]. Tally® includes integrations with industry standard
software, but it is only viable for detailed design stages, often requiring Building Information
Modeling (BIM) files with a high Level of Development (LOD) [14]. Further, the creation of
conceptual floor plans, elevations and sections are tasks that are still far from being connected to
LCA software, hampering its implementation on conceptual design processes. In short, early
stage integration of EE with industry standard workflows is lacking in LCA tools.
Further, the business-as-usual building design method makes it difficult to incorporate EE. The
challenge is that it involves multidisciplinary multi-objective problems that are difficult to solve
through conventional design practices [44]. Also, past studies have highlighted bounded-
rationality as a potential issue when defining a project because of the cognitive limits and
computational constraints involved in collecting and processing large amounts of data [71,72],
leading to poor-performing results [73,74]. Studies have concluded that potentially higher-
performing designs are being left unconsidered due to the narrow exploration of the design space
early in the design process [35,75]. Generative design (GD) represents a promising approach for
solving these problems through the automated evaluation of large numbers of design alternatives.
1.2.3 Generative Design (GD)
Generative design (GD) systems enable novel design exploration scenarios, ultimately providing
a set of high-performing solutions that comply with objectives and constraints previously set by
8
the designer. Consequently, GD systems can help planners and designers weigh different
objectives and assess tradeoffs [46,76,77].
The application of GD during early project stages has been shown to enhance design efficiency
[78]. However, previous studies using generative design have been limited to the optimization of
steel structures and the energy efficiency of buildings [32,35,73,79–81]. Thus, this study uses a
GD system for incorporating and minimizing EE at the conceptual design stage of high-rise
residential projects.
Currently, there are two methods for implementing GD. The first develops the system using
visual programming interfaces, such as Dynamo [41] and Grasshopper [42]; the second relies on
third-party applications that can perform GD. Table 2 lists ready-to-use GD applications publicly
available as of June 2020 and compares their functionalities with the tool that is proposed in this
study.
Most of these applications started with urban level analysis to find sites for potential
development, and gradually incorporated analysis modules for single buildings, ranging from site
feasibility to interior space planning. Highly sophisticated tools such as GenDes, Giraffe,
Covetool, and Kreo can perform multidisciplinary assessments, including the calculation of
environmental metrics related to the LEED® standard [82], solar radiation, and energy modeling.
Despite their comprehensive scopes, whole building EE have been overlooked by commercial
tools. Moreover, all these applications involve subscription costs, which hinder their rapid
adoption in a software saturated industry. In response, the present study relies on Dynamo to
build the generative design system. Dynamo is an opensource platform that can be seamlessly
connected with business as usual software for building design, such as Revit.
1.3 Scope and Objectives
The main objective of this research is to develop a tool that allows industry practitioners to
weigh design trade-offs when seeking to reduce embodied GHG emissions in high-rise
residential Toronto-based buildings. Additionally, this research aims to: determine the sources of
material quantity consumption in Toronto’s high-rise residential projects; identify the conceptual
design objectives that are critical during conceptual design; and assess the impact of the City of
Toronto’s tall building regulations on embodied GHG emissions.
9
Table 1. Building LCA tools as of June 2020, adapted from [83] and [14].
Name Developer Early-stage module
BEAT® 2002 Danish Building Research Institute (SBI), Denmark ⬚ -
BEES® 4.0 National Institute of Standards and Technology, U.S.A. ⬚ -
CCaLC Tool® University of Manchester, U.K ⬚ -
EC3 Building Transparency, U.S.A. ⬚ -
Environmental Status Model Association of the Environmental Status of Buildings, Sweden ⬚ -
ESCALE® University of Savoie, France ⬚ -
LEGEPs University of Karlsruhe, Germany ⬚ -
PAPOOSE® TRIBU, France ⬚ -
Tally® KT Innovations, thinkstep, and Autodesk, U.S.A. ⬚ -
TEAM™ Ecobilan, France ⬚ -
ATHENA™ ATHENA Sustainable Material Institute, Canada ✔ Impact Estimator
BeCost® VTT, Finland ✔ BeCost
CCEA Institution of Civil Engineers, U.K. ✔ Carbon Calculator
EDGE® International Finance Corporation (IFC), U.S.A. ✔ EDGE app
Envest 2 Building Research Establishment (BRE), U.K. ✔ Envest 2 - IMPACT DB
OneClick LCA® BIONOVA LTD, Finland ✔ Early Design Optimization
No early-stage module ⬚ / ✔ Includes early-stage module
10
Table 2. Summary of ready to use commercial GD applications for conceptual building design as of January 2020
Name (webpage) Developer (country) Released Scope Functionalities
SB UL FA ZA SP WA SO ST EA EE
Ratio.City (ratio.city) Ratio.City (Canada) 2017 ✔ ✔ ✔ ✔ ⬚ ⬚ ⬚ ⬚ ⬚ ⬚
Digital Blue Foam (digitalbluefoam.com) Digital Blue Foam
(Singapore) 2019 ✔ ✔ ✔ ✔ ⬚ ⬚ ⬚ ⬚ ⬚ ⬚
Hypar (hypar.io) Hypar (U.S.A.) 2018 ✔ ⬚ ✔ ✔ ✔ ⬚ ⬚ ⬚ ⬚ ⬚
Unitize (matterlab.co) Matterlab (United Kingdom) 2019 ✔ ⬚ ✔ ✔ ✔ ⬚ ⬚ ⬚ ⬚ ⬚
Archistar (archistar.ai) Archistar (Australia) 2017 ✔ ✔ ✔ ✔ ✔ ⬚ ⬚ ⬚ ⬚ ⬚
ADITAZZ Design Synthesis and Site Fit
(aditazz.com) Aditazz (U.S.A.) 2016 ✔ ⬚ ✔ ✔ ✔ ⬚ ⬚ ⬚ ✔ ⬚
TestFit (testfit.io) TestFit Inc (U.S.A.) 2016 ✔ ✔ ✔ ✔ ✔ ⬚ ✔ ✔ ⬚ ⬚
Kreo Modular, Design and Planning
(kreo.net) Kreo (United Kingdom) 2017 ✔ ⬚ ✔ ✔ ✔ ✔ ✔ ✔ ⬚ ⬚
Cove Tool (cove.tools) Cove Tool (U.S.A.) 2017 ✔ ✔ ✔ ✔ ⬚ ⬚ ✔ ⬚ ✔ ⭘
Giraffe (giraffe.build) Giraffe Technology
(Australia) 2018 ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ⬚
GenDes (gendes.sidewalklabs.com) SideWalk Labs - Alphabet
(U.S.A./Canada) 2018 ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ⬚
GenGHG: The present study's proposed
tool J. Zaraza et al. (Canada) 2020 ✔ ⬚ ✔ ✔ ⬚ ✔ ⬚ ✔ ⬚ ✔
SB: single building, UL: urban level, FA: architectural feasibility analyses, ZA: zoning analyses (per local zoning by-laws), SP: interior space planning, WA: wind analyses
(simplified for conceptual design), SO: solar analyses, ST: structural analysis (for main structural members), EA: environmental analyses (including energy modeling and
LEED® points), EE: estimation of embodied emissions. ⭘: Envelope components only
11
Chapter 2 Methods
Research methods
The structure of the methodology is underlined in Fig. 2. The main contribution of this study lies
in the generative design tool section, which describes the steps, systems, and software used for
the development of the tool. The context analysis comprises the reasoning behind the selected
tool features, while the last steps involve the validation of tool-generated design alternatives
using a case study.
Fig. 2. High-level research methods which align with the structure of the following sections.
2.1 Context Analysis
Using the literature and case studies, the present study follows an exploratory approach for
understanding the current high-rise conceptual design process and the physical characteristics of
high-rise residential construction in Toronto, Canada. The context analysis included an
exploration of the decision-making process involved in conceptual building design, which was
used for documenting design goals and constraints. These are detailed in section 3.4.
Additionally, nine high-rise residential concrete-framed case studies located in downtown
Toronto were analyzed to understand the typical contribution of different building components to
total embodied GHG emissions. All buildings are illustrated in Fig. 3 and were under
construction in September 2019.
12
Fig. 3. 3D visualizations of the nine case studies analyzed plotted according to their height and
gross residential area. All the case studies are located within the boundaries of the downtown
and central waterfront area as defined in the city of Toronto ‘How Does the City Grow?’ report
[48].
The estimation of EE was limited to façade and structural components (excluding the
foundation) and was structured according to the three building sections that characterize high-
rise residential construction: tower, podium, and underground. The sections are illustrated in Fig.
4 and were identified using a sample of 154 high-rise Toronto-based projects extracted from the
Council of Tall Buildings and Urban Habitat (CTBUH) database [17] and 3D visualizations from
urbantoronto.ca [30]. As of September 2019, the 154 projects investigated for the identification
of building sections were either proposed or under construction and over 100 meters tall.
The EE were calculated by multiplying material quantities by their respective GHG emission
factor, in kgCO2-eq per element-specific unit, i.e., m3 for concrete, Mg for rebar, and m2 for
façade components. Table 6 lists these inputs. The GHG factors were extracted from Ecoinvent
v3.5 [84], a life cycle inventory database, and from environmental product declarations (EPDs)
found through third party tools such as EC3 [85] and OneClick LCA [86]. Although most of the
m2
13
sources are specific for Ontario’s context, there are uncertainties involved when using EPDs
even if local data are available due to changes in reference flow, system boundaries,
manufacturing time, and carbon intensity of the electricity grid [87]. Hence, the results of this
section are presented to identify global trends regarding the contribution of building sections and
components to total GHG emissions rather than finding exact values.
The estimation of the material quantities was conducted by developing a 3D model of each case
study and performing automated material takeoffs. The projects were modeled in Revit 2019.2
[88] with a LOD 200 which involves the representation of building elements with an
approximate shape, size, location, and orientation, as defined in the BIM FORUM 2018 standard
[89]. The data source was architectural plans, which were extracted from the City of Toronto
development projects database [90]. Reference structural drawings were provided by industry
partners. These were used during the modeling process along with on-site pictures extracted from
urbantoronto.ca [30].
Fig. 4. Building sections. The tower section contains mostly residential units. The podium
section is commonly used for amenities and common-use services such as cleaning and
mechanical rooms. The underground section is for parking and storage.
14
2.2 Generative Design Tool
The structure of GD systems reflects the three key steps: design generation, design evaluation,
and optimization [36]. In the present study, the first two steps are performed using Dynamo
(v2.6) [41], a visual programming interface for design automation, and the optimization step is
conducted in Refinery (v0.60.2) which relies on the genetic algorithm NSGA-II. Fig. 5
illustrates the data flow through the proposed GD system.
The inputs for design generation are listed on Table 3. These are passed to a geometry system
that acts as a 3D building generator. The geometry system is structured based on the three typical
high-rise building sections and on a set of conceptual design assumptions listed in section 3.3.
During the design evaluation, multidisciplinary metrics are calculated for each building
alternative as shown in section 3.4. Then, the optimization process assigns a score to each
solution using a set of goals and constraints, which are documented based on the sources detailed
in section 3.4. The non-dominated sorting genetic algorithm II (NSGA-II) iteratively adapts the
variable inputs ultimately producing a set of near-optimal designs.
Fig. 5. GD system data flow diagram containing the methods and outputs in squared boxes, and
the optimization steps and the platforms used in rounded boxes. The data flow arrows indicate
the direction of the information being passed between steps and software platforms.
15
Table 3. Generative design tool inputs
Name Abbreviation Description
Project site conditions -
Includes the urban context of the project,
which is imported from the city of Toronto
3D Massing database
Project site boundary SB Imported from Revit using vertex coordinates
Tower number of floors TNF Variable
Podium number of floors PNF Variable
Tower ceiling height TCH Variable or fixed by the user
Podium ceiling height PCH Variable or fixed by the user
Underground ceiling height UCH Variable or fixed by the user
Geometry system variables - Listed in Table 5
Design constraints - Listed in Table 11
The tool inputs comprise variables and data required to generate and evaluate design alternatives.
The project site conditions refer to urban context information which includes the location of the
building and 3D models of neighboring buildings, landmarks, and parks. These are imported into
Dynamo following the steps described in section 2.5.
2.3 Design Generation
In generative design studies, geometry systems are a set of sequential steps that can generate
design alternatives once executed. The steps are preprogrammed in Dynamo using its built-in
geometry functions. Once the design alternatives are generated, search algorithms, such as the
NSGA-II [91], can be used for performing the design optimization [77]. Geometry systems
previously proposed are limited in that they rely on orthogonal, square-like models, and in that
they consider only alternatives for entire buildings and not their major sections [32,35–38,92]
leaving out a major defining characteristic of high-rise residential development.
16
This study proposes a novel geometry system for the generation of realistic, modern-looking, tall
residential building shapes. describes the steps of the system as it creates the three building
sections under analysis. The steps are a series of geometric operations that through translation,
intersection, subdivision, and extrusion, are able to generate a building shape in one iteration.
The steps are the result of several experimental tests for achieving a desired level of aesthetics
and geometric variability within the shapes produced, and are based on the conceptual design
assumptions listed in Table 4. These assumptions are critical to frame the structure of the
geometry system. These include architectural and geometric considerations that dictate the
sections that are fixed and the ones that vary. For instance, it is provided that the tower shape
may vary in geometry as long as it does so within the site boundary. In contrast, the podium and
underground section are assumed to preserve the same shape of the site, as this is common
practice in downtown Toronto high-rise buildings since developers are bounded to maximize lot
use. This was observed on the 154 projects analyzed in section 3.1.
Table 4. Conceptual design assumptions for design generation
Type Description
Architectural The tower section contains 100% of the residential units.
Architectural The podium section contains retail outlets and amenities.
Architectural The underground section contains parking and storage, provided in
accordance with zoning by-laws.
Geometric The tower shape can vary within the site boundary.
Geometric The podium and underground sections preserve the same shape of the site
boundary.
Many of these constraints are connected to the site boundary which represents the perimeter of
the building lot delimitating public and private space, its geometry is imported using Revit. Fig.
6 shows the system’s flow diagram and its data inputs which are classified into user dependent
(constants) and system parameters (variables), e.g., project-specific data, such as ceiling heights,
are fixed and inputted by the user while the geometry system parameters, shown in Table 6, are
not. Only the geometry system parameters are varied by the search algorithm as it explores the
design space through its generate-evolve-evaluate iterative process [31,93]
17
Table 5. Geometry system steps for design generation
Step Inputs Description Visualization Step Inputs Description Visualization
1 SB
(a) Import the site
boundary (SB) polygon
from Revit
6 b1
(h) Break lines at a portion of the distance (b1) from
the centroid and create
points
(j) Connect the newly created perimeter points
and create a polygon
2 -
(b) Offset the site
boundary 10 m
(c) Create a bounding box using the offset shape
7 -
(k) Intersect the site
boundary and the newly created polygon
3 x1,
y1
(d) Move the bounding
box using the centroid of
the SB as origin point and
a vector v(x1,y1)
8 f1
(m) Create a Dynamo surface from the
intersected area
(n) Fillet* (f1) the
perimeter of the surface using a fillet radius of 5m
4 r1
(e) Rotate the bounding
box r1 degrees with the
moved centroid as the center of rotation
9
TTH,
x2,y2,
r2,n2,b2,f2
(p) Duplicate the
intersected area at the
Tower total height (TTH)
(q) Repeat 1 to 8 on the top surface (suffix number: 2)
5 n1
(f) Break the bounding box
perimeter in n1 equally
spaced points (g) Connect the points
with the centroid of the SB
10 PTH,
UTH
(t) Create a solid by
joining the top and bottom
surfaces
(u) Extrude the SB twice to form the P and U sections
using their heights (-TH)
An animation of the geometry system as it performs design iterations is available here: https://imgur.com/zyUgavk. *Fillet is the geometric operation for rounding the corners of a polygon with a circle.
(a)
(c)
(b)
(d)
(g)
(k)
(q)
(r)
(h)
(j)
b1
(p)
(m)
(t)
(u)
(e)
(f)
18
Start Steps 1-8 Steps 9-10.a.
Calculate UNF
Step 10.b. End
SB
TTH PTH UTH
TNF TCH PCH UCH
Building heights
x1,y1,r1,
n1,b1,f1
x2,y2,r2,
n2,b2,f2Zoning By-laws
User dependent (constants)
System parameters (variables)
Subprocess
Process
External data
Prefixes:T: Tower, P: Podium, U: UndergroundSuffixes:-NF: Number of floors-CH: Ceiling height-TH: Total height
PNF
Fig. 6. Flow diagram describing the geometry system steps and inputs. The steps are listed in Table 5. The calculated values, i.e.
subprocesses, are described in equations 1, 2, 3, and 4. The ranges of the system parameters (variables) are listed in Table 6.
19
The values for the subprocesses illustrated in Fig. 6 are calculated using the following equations:
(T,P,U)TH = (T,P,U)NF ⦁ (T,P,U)CH (1)
UNF = UPA / SB area (2)
PNF = GRA⦁ AM% / SB area (3)
AM% = 32% (4)
Where UPA is the underground parking area required, including car and bike parking, car and
pedestrian corridors, and storage; SB is the site boundary; GRA, the gross residential area; and
AM%, the percentage of amenity area over GRA.
UPA is calculated using the City of Toronto's Zoning by-Law amendments [94] and the average
area for corridors and storage calculated from the nine case studies as presented in Appendix 4.
The SB area is calculated using Dynamo’s built-in geometry functions. The AM% was extracted
from the case studies and is assumed constant for all building alternatives, meaning that the PNF
only varies according to the GRA. This assumption applies for all projects with similar
characteristics as those seen in the case studies: high-rise condominium buildings in downtown
and central waterfront Toronto. However, the tool users may edit this value according to location
and project specific requirements.
Table 6. Ranges of the geometry system variable parameters for design optimization
Input Data type [unit] Range (min; max; step) Description
TNF integer 20; 70; 10 Tower number of floors
x1,y1,x2,y2 double [m] -50; 50; 10 Step 3 in Table 5
r1,r2 integer [°] 0; 270; 10 Steps 4 and 9 in Table 5
n1,n2 integer 5; 20; 5 Steps 5 and 9 in Table 5
b1,b2 double 0.4; 0.8; 0.1 Steps 6 and 9 in Table 5
f1,f2 boolean true; false Steps 8 and 9 in Table 5
20
The selection of the minimum value for the TNF shown in Table 6 is based on the minimum
building height threshold for defining a high-rise building, i.e. 50 m according to the CTBUH
[16] assuming a fixed ceiling height of 3.3 m. The maximum TNF value is determined using the
National Building Code of Canada 2015 [95] threshold for ‘dynamically sensitive’ buildings
which are defined as those with a slenderness ratio between 4 and 6. Due to the 750 m2 floor
plate restriction imposed by the City of Toronto, it is expected that, on average, all buildings
taller than 230 meters would have a higher slenderness ratio than 6, considering a fixed ceiling
height of 3.3 m.
2.4 Design Evaluation
Fig. 7 lists the proposed design metrics for design evaluation with their respective objectives and
constraints. These metrics were selected based on the documentation conducted in the context
analysis section. Three design objectives were selected: To minimize EE, maximize the floor
area ratio (FAR), and maximize the architectural score (ARCH). The FAR is calculated by
dividing the gross residential area (GRA) by the area of the site and is a common metric for
assessing project feasibility during conceptual building design. The underlying processes for the
calculation of each objective and the reasoning behind their selection, are described in the
following sections.
Fig. 7. Design evaluation metrics, goals, and constraints. The data sources for each are listed in
Table 7 and Table 8.
21
The resources used for documenting the design constraints stated are listed in Table 7, these
include urban-level, fire-proofing, structural, and multidisciplinary building codes. The most
critical design goals were extracted from three papers which analyzed the conceptual design
process. The assumptions for design evaluation are listed in Table 8.
Table 7. Resources used for documenting goals and constraints.
Name Type
City of Toronto Tall Buildings Guidelines (2013) Urban-level building code
City planning - Zoning By-Law NO 560 (2013) Urban-level building code
Ontario Building Code Compendium (2006) Multidisciplinary building
code
Ontario Requirements for New High-Rise Buildings Fire-proofing building code
National Building Code of Canada (2015) – Chapter 4 Structural building code
Benchmarking current conceptual high-rise design processes
(2010) [24] Stakeholders goals
Conceptual High-Rise Design A design tool combining
stakeholders and demands with design (2016) [43] Stakeholders goals
A Knowledge-Based Approach to Preliminary Design of
Structures (1990) [44] Stakeholders goals
Table 8. Conceptual design assumptions and scope for design evaluation
Related
goal Description
EE Lateral and vertical stability system: tube in tube according to case studies.
EE Floor system: flat slab without column heads.
FAR Area of walls and columns is neglected.
FAR Area of elevator, stairs, and MEP (mechanical, electrical, and plumbing) shafts
is not included.
FAR Neglects the effect of sharp corners for the GRA calculation.
ARCH The architectural score neglects the effect that highly irregular buildings may
have on torsional structural forces.
ARCH Assumes that “aesthetics” is connected to building irregularity. A measure to
avoid the generation of building solutions with square-like shapes.
ARCH Assumes that a better ‘view’ involves more unobstructed lines of sight to
landmarks, parks, and the water bodies.
22
2.4.1 Embodied Emissions (EE)
The scope of the LCA for estimating EE is shown in Fig. 8. Both the product and construction
stages are considered due to the scale of high-rise building construction which involves the
operation of high fuel use machinery. This includes the installation or erection of building
components, their transportation from manufacturing plant to site, and the disposal of excess
excavated soil. Fig. 9 details the processes and elements considered within the LCA system
boundaries. Table 9 lists the estimated EE factors per element-specific unit and their associated
calculation method with data sources. Lastly, Table 10 consolidates the total EE factors per
material and building section.
Fig. 8. High-level system scope for the estimation of EE. The stages are defined per the ISO
14040 standard [96]. The construction stage includes excavation of underground levels,
installation of structural and envelope elements, and transportation of construction materials
and excess soil. Fig. 9 expands on the details of the system scope.
23
Fig. 9. Detailed system scope for the estimation of EE. Only structural and envelope components are included. The façade ratios were
extracted from the context analysis. The construction stage includes excavation and installation. The estimation of on-site fuel use is
based on a model proposed in [97]. The installation includes concrete pumping and hoisting of façade components, data sources are
shown in Table 9.
24
Table 9. Specifications and data sources used for the estimation of EE.
Classification Materials or activities EE Units Source (Location) Detailed specification
Structure Concrete (tower and podium
slabs) 320 kgCO2-eq / m3 concrete EC3 (Ontario)
25 MPa. SCMs: 20% Total, Fly Ash: 0.15, W/C Ratio: 0.45, max. aggregate
size: 0.75 in
Structure Concrete (underground slabs) 350 kgCO2-eq / m3 concrete EC3 (Ontario) 30 MPa. SCMs: 20% Total, Fly Ash: 0.15, W/C Ratio: 0.45, max. aggregate
size: 0.75 in
Structure Concrete (walls and columns) 400 kgCO2-eq / m3 concrete EC3 (Ontario) 35 MPa. SCMs: 20% Total, Fly Ash: 0.15, W/C Ratio: 0.45, max. aggregate
size: 0.75 in
Structure Reinforcing steel 1300 kgCO2-eq / Mg rebar EC3 (Ontario) 400R/W MPa. CAN/CSA G30.18M Grade.
Structure Concrete pumping (at site) 40 kgCO2-eq / m3 concrete Ecoinvent v3.5 (Global) Machine operation, diesel, >= 74.57 kW, high load factor. See Appendix 12.
Structure Concrete transportation (from
manufacturing plant to site) 1 kgCO2-eq / m3 concrete Ecoinvent v3.5 (Global) Freight, lorry 3.5-7.5 metric ton, EURO3. See Appendix 13.
Structure Rebar transportation (from
manufacturing plant to site) 3 kgCO2-eq / Mg rebar Ecoinvent v3.5 (Global) Freight, lorry 3.5-7.5 metric ton, EURO3. See Appendix 14.
Façade (60%) Double-glazed window wall 290 kgCO2-eq / m2 façade OneClick LCA (Ontario) Appendix 16
Façade (32%) Spandrel panel 120 kgCO2-eq / m2 façade OneClick LCA (Ontario) Appendix 17
Façade (8%) Exterior walls 90 kgCO2-eq / m2 façade OneClick LCA (Ontario) Appendix 18
Façade Installation of walls and window
walls 3 kgCO2-eq / m2 façade Ecoinvent v3.5 (Global)
Includes unloading, moving on-site, hoisting, anchoring, and sealing, See
Appendix 19.
Façade Transportation (from
manufacturing plant to site) 9 kgCO2-eq / m2 façade Ecoinvent v3.5 (Global) Freight, lorry 3.5-7.5 metric ton, EURO3. See Appendix 16.
Excavation Excavation equipment use
(excavator and bulldozer) 4
kgCO2-eq / m3
excavated material Ecoinvent v3.5 (Global) Fuel estimation from [97], see Appendix 20.
Excavation Transportation (from site to
landfill/fill site) 20
kgCO2-eq / m3
excavated material Ecoinvent v3.5 (Global) Freight, lorry 3.5-7.5 metric ton, EURO3. See Appendix 11.
SCM: Supplementary cementing materials, W/C: water to concrete, R: designated regular, W: weldable.
25
The material specifications listed in Table 9 were selected using data obtained from the context
analysis section. The case studies structural plans were used in the definition of the compressive
strength of concrete per building section. Likewise, the façade materials and components were
selected based on the most common assemblies seen in the case studies. The estimation of on-
site and transportation related GHG emissions was based on global LCA factors for the use of
high load diesel equipment obtained from the database Ecoinvent v3.5 [84].
Although the adopted factors for freight, lorry 3.5-7.5 metric ton, are reported by Ecoinvent as
global values, the data sources of their life cycle inventory are highly influenced by European
standards. Hence, the values for a North American context may partially differ. However, this
variation is not expected to significantly alter the results consolidated in Table 10 since all
transportation related emissions are lower than 20 kgCO2-eq / m3 of material; considerably less
than all other impacts calculated.
Table 10. Consolidated LCA factors per material
Classification Material EE Units
Structure Concrete (tower and podium
slabs) 360 kgCO2-eq / m
3 concrete
Structure Concrete (underground slabs) 390 kgCO2-eq / m3 concrete
Structure Concrete (walls and columns) 440 kgCO2-eq / m3 concrete
Structure Reinforcing steel (all sections) 1300 kgCO2-eq / Mg rebar
Façade (60%) Double-glazed window wall
(all sections) 300 kgCO2-eq / m
2 façade
Façade (32%) Spandrel panel (all sections) 140 kgCO2-eq / m2 façade
Façade (8%) Exterior walls (all sections) 100 kgCO2-eq / m2 façade
Excavation Excavated material
(underground) 30
kgCO2-eq / m3 excavated
material
26
The transportation distances from manufacturing plant to construction site, and from construction
site to landfill for excess soil, were determined using relevant data for a site located in downtown
Toronto. The data sources and calculation processes are detailed in the appendices section, as
referenced in Table 9. The construction stage excludes offshoring and formwork materials and
activities due to lack of available data to accurately represent them.
With the LCA factors determined, the next step involves their integration into Dynamo, followed
by the estimation of the total EE. This process is illustrated in Fig. 10, reflecting four main
steps: classification, data extraction, data storage, and calculation. First, the components of the
3D model are classified per discipline of interest, i.e., earthworks, architectural, and structural.
Next, the material quantities are extracted according to each discipline, building section, and
element-specific unit. These are then stored in a Dynamo directory (Q) which passes the data to
the last step: calculation, which outputs the total EE in GgCO2-eq.
Fig. 10. Data flow diagram for the estimation of EE.
27
While the extraction of the material quantities for excavation activities and façade elements is
seamlessly performed in Dynamo using 3D geometry functions, the estimation of structural
related material quantities entails a more complex process. This is due to the proportional
relationship between building parameters, such as floor area and height, with the concrete and
rebar use of structural elements, such as the core, frames, and slabs. In detailed design stages, the
process would involve the use of finite element modeling (FEM) techniques. However, due to
the nature of conceptual design, namely the low level of information available at this stage, the
present study developed an estimation model instead of relying on FEM techniques for the
assessment of structural material quantities.
The proposed model calculates the volume of concrete and the mass of rebar required for all the
reinforced concrete (RC) structural elements. The outputs are normalized in terms of the gross
floor area of the building in m2. The model, which operates in Dynamo, executes a linear
regression function formulated from previous findings [52]. The linear regression was created as
part of this study and it was chosen due to the partial linear relationship observed between height
and concrete use ratio per building area. The study considered thirty-six high-rise building
scenarios by varying structural attributes such as the height, area, and structural system and
materials, and then calculated structural material quantities using FEM techniques. The resulting
material quantities for RC tube-in-tube buildings with RC cores, RC frames and RC flat slabs are
the data inputs for the estimation model in the present study. The resulting functions are shown
in Equations 5 and 6. The calculation steps are presented in Appendix 21.
RU (H) = 7.41 • 10-5 (H) + 0.0371 ; R² = 0.8319 (5)
CU (H)= 5.10 • 10-4 (H) + 0.2562 ; R² = 0.8141, (6)
where RU is the rebar use ratio in Mg/m2, CU is the concrete use ratio in m3/m2, H is the total
height of the building in meters including underground floors, and R2 is the coefficient of
determination for the regression.
28
The results obtained by [52] are considered representative for RC-framed high-rise buildings
located in Toronto due to the similarities between the loading criteria and drift limits
implemented and the ones found in the National Building Code of Canada v2015 [87].
Specifically, 2.50 kN/m2 for distributed dead loads, 4.00 kN/m for façade dead loads, 3.00
kN/m2 for distributed live loads, a maximum inter-story drift limit of 1/400 the ceiling height,
and a maximum total lateral displacement of H/500.
2.4.2 Floor Area Ratio (FAR)
In high-rise residential development, the FAR is critical for assessing project feasibility as it is
directly related to the number of units that can be sold. It is calculated by dividing the gross
residential area (GRA) by the area of the site. The FAR has been widely used as a benchmark
metric during conceptual building design, even over budget constraints [47]
The GRA of each design alternative is calculated using Dynamo’s built-in geometry functions.
This area excludes MEP openings and elevator and stair shafts but does not neglect the areas of
walls and columns. The number of elevators and their dimensions are provisioned per the
requirements stated in the OBC2012 [98] and the Ontario Requirements for New High-Rise
Buildings document [99], which vary according to the residential area and height of the building.
Fig. 11 illustrates the scope for the estimation of the FAR. The calculation steps and
assumptions for the FAR are listed in Appendix 22.
Fig. 11. Scope for the estimation of the floor area ratio (FAR). Hallways and structural
components area are both included in the calculation of the total residential floor area.
29
2.4.3 Architectural Score (ARCH)
The architectural score reflects two goals that are relevant during conceptual building design:
improving aesthetics and maximizing site views. It has been determined that these are the most
and the third most important early stage objectives in North American projects, with FAR being
the second [47]. The current study proposes the following metrics for incorporating them:
1. Unobstructed views score:
Measures ‘how good the view is’ by calculating the ratio between the unobstructed view
lines, i.e. direct lines of sight, and the total view lines from the building to a specific point
of interest. The top picture in Fig. 12 illustrates the unobstructed view lines in yellow and
the obstructed ones in red. The origin points are placed on the building façade with a 5 m
vertical and horizontal spacing, while the target viewpoints are imported by the user from
Revit and can be located anywhere. The site conditions were incorporated by using the
City of Toronto Sketchup-based 3D massing database [100]. It was chosen due to its high
reliability in comparison to the OpenStreetMap public database, as the former more
accurately represented downtown Toronto’s buildings and landmarks; both data sources
were tested during the development of the tool, leading to the aforementioned
conclusions. Due to the unconventional data format found in the City of Toronto’s
webpage and the inaccurate 3D model available in OpenStreetMap (.osm) format, a novel
data translation system was implemented to convert Sketchup (.skp) mesh-based files into
native Dynamo surfaces, resulting in a total of 2500 surfaces imported for a downtown
Toronto case study.
2. Irregularity score:
Captures the building aesthetics by assigning a higher score to the shapes with a higher
number of vertices, as represented in the bottom section of Fig. 12. The minimum and
maximum values are coded into Dynamo as constraints during the design generation and
were defined based on typical values found in the case studies.
The total architectural score (ARCH) aggregates both metrics by assigning the same level of
importance to each and calculating a single percentage-based score. However, the level of
importance can be varied by the user from 0 to 1, where 0 would cancel the irregularity score.
30
Fig. 12. Illustration of the calculation process for the unobstructed views and irregularity
scores.
2.5 Design Constraints
Table 11 lists the constraints documented in accordance with structural, architectural, and fire
hazard building regulations. Some of these were directly incorporated into the tool while others
must be set by the user before running the optimization. The incorporation of these restrictions is
critical for ensuring that the output design solutions are usable for further design phases.
In terms of structural constraints, the calculation of factored wind loads was conducted for
ultimate limit state (ULS) conditions according to chapter 4 of the NBC2015 [87]. All
calculations were performed for dynamically sensitive buildings, i.e., those taller than 60 meters
or with a slenderness ratio between 4:1 and 6:1, as defined in the NBC2015. The total lateral
displacement at the top of the building was calculated using the simplified model for reinforced
concrete buildings proposed in [91] which requires two main inputs: the structural effective
widths and the total shear at the base due to ULS factored wind loads. The structural effective
widths along and parallel to wind direction were extracted using the longest side of the building
shape and the maximum distance perpendicular to it.
31
Table 11. Design constraints used for design evaluation.
Category - source Constraint Value or range Reference Consideration
Structural - NBC2015 [95]
Slenderness Ratio threshold for dynamically sensitive buildings.
Surpassing it qualifies buildings as ‘very dynamically sensitive’
and brings the need for the wind tunnel procedure.
Maximum 6:1 (max. height : max. width). 4.1.7.3 yes
Total lateral displacement under ULS conditions Lateral Displacement < H/500 4.1.3.6 yes
Podium - Toronto’s Tall
Building Guidelines [101]
Podium wall height Minimum 10.5 m to 80% of adjacent right of way /
Maximum 24 m Pg 38 yes
Additional Podium wall height Up to 100% of adjacent street right-of-way if
additional 3m step back is provided / max. 24m Pg 42 yes
First floor height Minimum 4.5 m Pg 41 yes
Grade separation between public sidewalk and private residential
unit entrance on ground floor Maximum separation: 0.9 m Pg 44 user-dependent
Distance separation between the front property line and private
residential unit entrance on ground floor Minimum separation: 3 m Pg 44 user-dependent
Tower - Toronto’s Tall
Building Guidelines [101]
Floor plate area (excluding balconies) Maximum area: 750 m2 Pg 46 yes
Step back the tower (including balconies) away from the face of
the Podium
Minimum step back distance: 3 m from podium
boundary. Pg 47 yes
The separation distance from both property lines and other towers Minimum 25 m, or widest dimension of the tower
floor plate Pg 49 yes
Height of building. Maximum building height designated by the
city. Should comply with neighbouring buildings.
This study uses a minimum height of 100 m and a
maximum of 240 m as the threshold height between
typical high-rise buildings and super tall ones.
Pg 19 yes
32
General - OBC2012 [98]
Minimum story height - living room, dining, kitchen 2300 mm over 75% of space, 2100 mm overall
space 9.5.3.1 yes
Minimum story height - bedroom 2300 mm over 50% of space, 2100 mm overall
space 9.5.3.1 yes
Minimum story height – basement or underground 2100 mm over 75% of space 9.5.3.1 yes
Minimum story height - washroom, hallway, all other areas 2100 mm overall space 9.5.3.1 yes
Maximum height of stairs Vertical height not exceeding 3.7 m 9.8.3.3 yes
Fire - Ontario
Requirements for New
High-Rise Buildings [99]
Elevators
At least one elevator with 2.2 m2 usable area.
Provision of elevators per number of units
shown in Appendix 22.
- yes
Stairs Above and below grade exit stairs are separated - yes
33
Chapter 3 Results and Discussion
Results
3.1 Context Analysis
Fig. 13 and Fig. 14 show the modeled EE for the structural and façade components of nine
downtown Toronto case studies. More specifically, Fig. 13 shows total EE in terms of kgCO2-eq
per square meter of building area (Fig. 13-1) and per cubic meter of building space (Fig. 13-2).
The error bars correspond to the variation among the case studies sample. On average, the EE
varied between 200 and 420 kgCO2-eq/m2, and 50 to 110 kgCO2-eq/m
3.
The results are highly dependent on the chosen normalization metric (i.e. m2 or m3). Although it
is common practice to use floor area as the normalization metric for building material quantity
estimations on LCA studies [14,37,50,57,87], floor area normalized results lack the required
context to compare several building sections because of the variability introduced by different
floor to floor heights. For instance, in the sample of case studies, the podium floor to floor
heights varied between 3.5 to 6.4 m, a substantial amount (2.9 m) when compared to the
underground (2.7 to 3.5 m, 0.8 m) and tower (2.8 to 3.3 m, 0.5 m). The high variability among
the floor to floor heights of the case studies podium section is the factor driving the variation
observed in Fig. 13-1. In contrast, the volume normalized results shown in Fig. 13-2 are a better
representation to compare EE intensities among building sections since floor to floor heights are
part of the calculation process. The results suggest that the underground is the most EE-intensive
section, followed by the tower and the podium. Although the envelope related EE-intensity was
the lowest for the underground, the contribution of structural components was enough to increase
its total EE intensity over other sections. The average volume-based EE-intensities calculated
are: 96, 85, 65 kgCO2-eq/m3 for the underground, tower, and podium, respectively.
The Sankey diagram in Fig. 13 illustrates the average contribution of each section, material, and
component type to the total EE in GgCO2-eq. The contribution of structural components is 82% of
the total emissions considered, with façade elements accounting for the remaining portion.
Among materials, concrete is responsible for the highest share, representing 45% of the total
modeled emissions, followed by rebar and envelope components.
34
Fig. 13. Box-and-whisker plot showing EE per building section in case studies. Normalized by square meters of floor space in (1) and by
cubic meters of building space in (2). Results based on a sample of 9 buildings in Toronto including only structural and envelope
components. Whiskers plotted for maximum of 1.5 IQR (interquartile range).
35
Fig. 14. Sankey diagram showing the average distribution of EE seen in case studies. Results
based on a sample of 9 buildings in Toronto including only structural and envelope components.
1 Gg (gigagram) is equivalent to 1 kt (one thousand metric tonnes).
The results obtained in this section correspond to the average EE intensities seen in previous
studies analyzing RC-framed high-rise buildings [37,55,102]. Based on these results the authors
establish that the most important section in terms of total contributions to EE is the tower, hence
the majority of the proposed tool functionalities described in section 3 are connected to this
section.
The high EE-intensity of the underground section was a critical reason to consider conceptual
design parameters affecting underground space, such as ‘number of underground levels required’
or UNF (described in Fig. 6), which is a function of the number of car and bike parking spaces
required as provided in zoning by-law regulations [94]. However, additional work is needed to
better understand further driving factors of the variability. Despite the simplified approach for
the estimation and the small sample of case studies, the results presented elucidated the typical
construction materials, and standards used in high-rise construction in Toronto, while providing a
general sense of EE-intensities per building section and according to area and volume-based
normalization metrics. This estimation exercise was the defining factor for several assumptions
made on section 3.
36
3.2 Generative Design Tool (GenGHG)
The proposed generative design tool, ‘GenGHG’ for short, can be accessed through two user
interfaces (UIs): a visual programming interface available by opening the script in Dynamo 2.6
(Fig. 15), and a simplified, ready-to-use, UI for running the optimization and visualizing the
results (Fig. 16). The latter runs in Autodesk’s Refinery (v0.60.2) and can be executed from
Revit’s or Dynamo’s generative design tab. Fig. 15 shows the Dynamo script flow divided into
five steps: inputs, design generation, design evaluation, outputs, and visualization. Each step
contains a series of code blocks and functions that perform a specific task. Some of these were
coded from scratch while others were extracted from open source third-party Dynamo packages,
such as Topologic [103], LunchBox for Dynamo, Dynamo Text, MeshToolkit, Clockwork for
Dynamo 2.x, spring nodes, and Ampersand, which are publicly available through the Dynamo
packages website (https://dynamopackages.com/).
GenGHG (available in https://github.com/julianzpe/genghg) comes shipped in with a
supplementary folder containing all the required packages and dependencies but the user is
responsible for their installation. Additionally, the Dynamo script contains detailed functionality
descriptions for groups of code, which the user can follow to extract particular pieces of data that
are not available through Refinery’s UI, such as geometry functions, assumption values, LCA
factors, and 3D geometry of the imported site conditions. The user may also export a specific
data point from Refinery to Dynamo and visualize it in a 3D environment. This environment
allows the user to zoom in, pan, and orbit around the generated building, enabling designers to
quickly evaluate high-performing solutions based on their perspectives and aesthetic preferences.
GenGHG’s code is presented through an easy to follow flow, which enables users to add custom
functionalities according to their design preferences, objectives, and constraints, all of this
without requiring extensive coding expertise.
37
Fig. 15. GenGHG’s visual programming flow. Each group of codes has a specific functionality within the major steps: inputs, design generation, design evaluation,
outputs, and visualization. This functionality is stated in the title of the group. Objects in the 3D visualization can be hidden or shown according to the user’s
preference. A high-resolution snapshot of the code flow is available in the following link: https://julianzaraza.page.link/genghgcapture
Inputs
Design Generation
Design Evaluation Outputs Visualization
38
Fig. 16. GenGHG’s user interface in Refinery (Autodesk®). The create study window with the optimization setup (left) includes the
objectives to be optimized, inputs to varied, and constraints for outputs. The generation settings for the NSGA-II can be configured at the
end of the window. The design exploration window includes 3D visualizations for all the generated solutions in addition to a parallel
coordinates plot showing the ranges of the parameters varied and a dedicated view for the outputs of the selected alternative.
Optimization setup Design exploration
39
The left picture in Fig. 16 shows the optimization setup window in Refinery. It is available to
the user through Refinery’s ‘create study’ option once the code has been exported from Dynamo
using the ‘export for generative design’ button. The selection of the goals and project-specific
constraints must be performed there. This includes which goals are to be optimized, upper and
lower bounds for selected constraints, and the generation settings which control the NSGA-II
optimization process. The generation settings are defined as follows:
1. The population size is the starting number of solutions in which the NSGA-II selection
process occurs, meaning that each alternative in the population set is a potential high-
performing solution that can thrive to the next generation set. After several tests using the
case study described in section 4.3, it was established that the optimization found high-
performing design alternatives faster by using a population size between 48 and 120. The
set of randomly generated solutions was used as baseline for the definition of high-
performing solutions.
2. The number of generations is the number of rounds in which the optimization steps will be
performed; each round includes selection, cross-over, and mutation [91]. A minimum of
10 generations is recommended based on experimentation. However, rising the number of
generations also substantially increases the total running time.
3. The ‘seed’ dictates the ID number of the set of design alternatives to be considered as the
initial population. Varying the seed is useful when the algorithm is not showing
convergence. For GenGHG, it was determined that the seed must be varied if no outputs
are observed over the first 10 minutes after initializing the optimization, this process
requires restarting Refinery and rerunning the optimization study.
Once the optimization is finished, the user is presented with a UI like the one shown in the right
section of Fig. 16. It contains three menus for visualizing the non-dominated, i.e. Pareto,
solutions: a window with 3D snapshots of each design alternative (top left), a parallel
coordinates plot showing how the chosen design performs in comparison to the other solutions
(bottom left, a zoomed-in version is illustrated in Fig. 18), and a menu containing a 3D
visualization and the resulting values for the chosen alternative (right).
40
3.3 Demonstration in a Case Study
A case study was used to test and validate the outputs of the tool in a real-world setting,
capturing the challenges that developers face during the conceptualization of high-rise residential
projects. An existing building site was selected from the publicly available database
urbantoronto.ca [30]. The selection process involved the evaluation of several downtown
Toronto sites with the aim to find one that could easily exemplify the intrinsic trade-offs
involved in conceptual building design. Fig. 17 shows the location of the selected site including
neighbouring buildings and urban conditions. The lot has access to two major downtown Toronto
streets and a total ground area of 2,500 m2. The fixed inputs for design generation were 3.3, 6,
and 3.2 m for the tower, podium, and underground ceiling heights.
The high density of the zone, and the site proximity to landmarks, parks, and the waterfront,
accentuate architectural design trade-offs, such as maximizing views and the FAR while
complying with height and floor plate restrictions per zoning by-laws. Further, the triangular
shape of the lot, and its large area, when compared to the maximum tower floor plate allowed in
Toronto (750 m2) [101], increases the number of possible tower shapes that could fit within the
site perimeter. As such, the use of GenGHG becomes a promising option for the generation and
evaluation of design alternatives while minimizing EE and maximizing the FAR and the ARCH
score as defined in section 3.4.
A
Landmarks
Site
Parks
Waterfront
41
Fig. 17. 3D (A) and plan (B) visualizations of the selected site (highlighted in red). The 3D mesh
model of the existing conditions was extracted from the city of Toronto 3D massing database
[100] and later rendered in Revit 2021.
3.4 Design Alternatives for a Case Study
The exploration of the design space for the selected site was performed using GenGHG through
two methods: a multi-objective optimization that minimizes EE, and maximizes the FAR and the
architectural score (ARCH); and a randomly driven generation of design solutions. The former
delivers the non-dominated solutions (NDS) set, while the latter populates the graph with
thousands of randomly generated solutions (DS) providing context to the Pareto front with
constrained and unconstrained design alternatives.
The calculation of the views component of the architectural score considered maximizing views
to landmarks, parks, and the waterfront, as shown in Fig. 17(A). To simplify the analysis, a
single target point per each area highlighted in Fig. 17 was implemented. These points are
placed in Revit and then imported into Dynamo through a .csv file. The optimization study was
performed with a population size of 120; 100 generations; a seed number equal to 5; a crossover
rate of 0.8 which comes predetermined in Refinery v0.62.2; and a mutation rate of 0.05. The
mutation rate is automatically assigned by Refinery to match the number of inputs, i.e. higher
rates are assigned to optimization problems with fewer inputs (up to 0.3 in single variable
studies). The total running time for the optimization was 38 hours in a workstation with an Intel
Core i7-7700k 4.0GHz, NVIDIA Quadro K200 and 32 GB of RAM. The randomization study
generated over 5,000 design alternatives over 149 hours, i.e. 6 days, of running time. The
N
B
42
resulting design alternatives from both studies are shown in Fig. 18, in which the upper and
lower bounds for each metric can be identified along with the frequency of a value being
repeated.
Fig. 18. Parallel coordinates diagram showing the ranges of a subset of metrics for the
randomly generated design alternatives.
Fig. 19 illustrates the generated alternatives plotted according to the three main metrics: EE,
FAR, and ARCH. The NDS are highlighted in red, corresponding to a total of six data points
after ruling out repeated and unconstrained results, considering the constraints listed in Table 11.
These constraints were initially inputted as part of the design generation process, as described in
section 3.2, but after running the optimization it was determined that not all the resulting
solutions were properly filtered by Refinery’s search engine. Hence, an additional filter was
applied to the outputted NDS, resulting in the six data points mentioned above. This behaviour
appears to be a consequence of the high number of constraints applied at the beginning of the
optimization process, which the current version of Refinery (v0.62.2) does not handle well.
Fig. 20 shows 3D and 2D visualizations for three (A1, A2, A3) of the six NDS, representing the
boundaries and mid-point of the sample Pareto front, as noted in Fig. 19. Given that Refinery
only outputs the latest generation of solutions (NDS), a gap between those and the randomly
generated ones (DS) is observed. This also indicates the high performance of genetic algorithms
for finding the Pareto front in a multi-dimensional design space. In this specific case, the design
space was represented by 432,000 possible solutions, which would have taken 990 days to
complete assuming the same generation speed observed in the randomization study.
43
Fig. 19. Design exploration results including randomly generated solutions (DS) and a sample of non-dominated solutions (NDS). A1,
A2, and A3 solutions are described in Fig. 20. EE: Embodied greenhouse gas (GHG) emissions, as described in 2.4.1. FAR: Floor area
ratio as described in section 2.4.2. ARCH: Architectural score as defined in section 2.4.3. An animation showing a subset of the explored
designs is available here: https://github.com/julianzpe/genghg/blob/master/20200818_GenGHG_core.gif.
A1
A2 A3
A1
A2
A3
A1
A2
A3
44
Fig. 20. 3D visualizations and 2D plans of the subset of non-dominated solutions (NDS) for the
selected site.
3.5 Analysis and Discussion of Results
The results suggest that there is a strong linear relationship between EE and the FAR. An
expected behaviour since the FAR is a function of the building floor area, which is proportional
to the building height and the tower floor plate area (FPA), two metrics that drive EE. The
opposite behaviour is observed between EE and the ARCH score since more parameters are
affecting the results. For instance, building alternatives with equal areas and heights can generate
different EE and ARCH scores due to building shape distinctions.
Fig. 21 shows this behaviour by comparing an NDS (A2) against a DS (B1). B1 is a constrained
building alternative with the same GRA and total height as A2 but with a different tower
geometry. A superposition showing the differences in geometry is attached in Appendix 23.
These were enough to achieve a 31% increase in the ARCH score (from 53 to 84%) and a 7%
reduction in EE, corresponding to 1 GgCO2-eq.
ND solution 1 (A1) ND solution 2 (A2) ND solution 3 (A3)
EE: 20.9 GgCO2-eq
FAR: 20.8
ARCH: 87%
GRA: 35,632 [m2]
TH: 215 [m]
FPA: 722 [m2]
EE: 13.4 GgCO2-eq
FAR: 12.5
ARCH: 84%
GRA: 21,400 [m2]
TH: 149 [m]
FPA: 722 [m2]
EE: 11.1 GgCO2-eq
FAR: 7.6
ARCH: 65%
GRA: 13,104 [m2]
TH: 149 [m]
FPA: 441 [m2]
3D
Plan
3D
Plan
3D
Plan
45
Fig. 21. Comparison between best and worst performing alternatives with equal FAR (floor area ratio). DS: randomly generated
solutions, NDS: non-dominated solutions, ST: structural; EN: envelope; EX: excavation; TH: total height; FPA: floor plate area; ENA:
envelope area.
A2
B1
B1
EE: 13.4 GgCO2-eq
ST: 8.9
EN: 3.9
EX: 0.6
FAR: 12.5
EE: 14.4 GgCO2-eq
ST: 8.9
EN: 4.9
EX: 0.6
FAR: 12.5
A2
46
The significant difference in the ARCH score is a result of building façade orientations that are
yielding a higher number of unobstructed views in A2. In terms of EE, the envelope area was
found to be a critical factor since it is 20% lower in A2 when compared to B1 (16,948 vs 21,121
m2) resulting in a reduction on envelope related EE from 4.9 to 3.9 GgCO2-eq.The relationship
between EE and façade area for the explored design space is shown in the left section of Fig. 22
where A2 and B1 are highlighted. Lastly, structural and excavation EE remained constant
representing 66.7 and 4.5%, and 61.8 and 4.2%, of total EE in A2 and B1, respectively.
Although the building shape discrepancies between the two data points are hard to perceive, the
reduction in EE is substantial: 7%, also equivalent to 1.75 times all excavation EE. This
considering that both have the same height, FAR, and irregularity score. To add perspective, the
difference in envelope area between A2 and B1 (4,175 m2) is equivalent to the façade area of 10
floors of building B1 (resulting from 126.5 m of building perimeter length and a 3.3 m floor to
floor height). This exemplifies how generative design approaches can help designers find high-
performing solutions that otherwise might be left unconsidered. As shown above, two designs
that seem very similar to the human eye are yielding considerably different results. Data-driven
evaluations as the ones performed by GenGHG are then critical for improving building design.
Fig. 22. Plots of Façade area and floor plate area versus EE. DS: randomly generated
solutions. NDS: sample non-dominated solutions.
A2
A4
B1 C1
• •
47
The right section in Fig. 22 shows the floor plate area (FPA) plotted against EE. The 750 m2
FPA constraint is noticeable as the NDS plateau at that point. The results suggest that this
constraint is forcing elevated EE since the only way to keep increasing GRA, while keeping the
FPA constant, is to increase the height. These increases in height come associated with a
premium in EE [55]. The sequential blank spaces between alternatives is a result of the 10-floor
step for the number of floors of the tower (NFT) as described in Table 6.
The FPA constraint was introduced by the City of Toronto in 2013 as a response to the
accelerated rate of high-rise residential and commercial construction, especially in the downtown
and central waterfront area [94]. The reasoning behind the constraint involves the minimization
of shadow impacts, negative wind conditions on surrounding streets, loss of sky view from the
public realm; the creation of architectural interest by visually diminishing the scale of the
building mass; and the provision of an ‘elegant’ building profile for the downtown skyline, as
described in the City of Toronto Tall Building Design Guidelines [101]. However, small changes
to this constraint might yield reductions in EE while generating a minimal impact on the City’s
goals. For instance, if the FPA constraint were to be increased 10% (from 750 to 825 m2) the
width of the building can only increase up to 3 m, a minimal change when compared to its height
(i.e. at least 149 m). Additionally, a broader set of high-performing design alternatives become
available as a consequence of the 10% increase in FPA. This is exemplified in Fig. 22 by the
design option C1, which has the same height and FAR as A4 but 6.2% less EE (16.5 compared to
17.6 GgCO2-eq). The ARCH score only varied 2%, with A4 holding a slightly higher score (84%)
meaning that had the FPA constraint not existed, C1 would have likely been part of the NDS set.
Using the City of Toronto publicly available development projects database [90] and 2019 data,
the authors found that many of the active projects in the City’s permitting pipeline have an FPA
above the 750 m2 threshold. This demonstrates that developers are highly interested in
maximizing FPA, an expected behaviour since it is ultimately increasing the FAR of the site.
Interestingly, this market-driven pursuit is also slightly reducing EE (if the height is maintained
fixed). The noncompliance with the FPA constraint has resulted in low permit approval rates by
the City of Toronto City Council, according to 2017 data [104]. However, the City Council’s
approval decisions can be appealed through the Ontario Municipal Board (OMB) leading to
approved projects that did not necessarily fulfil the City’s regulations. Interestingly, projects
48
rejected by City Council have been assessed to have an 85% probability of then being accepted
by the OMB, according to a 2018 study [105].
If GenGHG were to be used on the 154 high-rise residential buildings with active development
applications in Toronto as of September 2019, the city-wide potential EE savings can be at least
144 GgCO2-eq, more than the city’s monthly waste-related GHG emissions in 2017 (125 GgCO2-
eq) [106]. This assuming a 7% potential reduction in total EE per project and considering that
their tower FPA is 750 m2, and an equal height of 149 m for all of them; a conservative approach
taking into account that the average height for the buildings sample is 165 m with a median of
156 m. The estimated city-scale EE savings demonstrate the potential that the use of GenGHG
can have for helping cities and countries meet, or get closer to, their GHG emission reductions
targets without impacting the supply and characteristics of tall residential condominiums.
3.6 Limitations and Future Work
Although the conceptual building design process normally involves design simplifications and
high-level assumptions, there are several ways in which these are adding unwanted uncertainty to
the estimation of EE. Namely, the estimation model formulated in section 2.4.1 is only varying
the EE intensity based on the height and GRA. Although this can be a fair simplification for
conceptual design, the effect that building geometry has on structural components is being left
unconsidered. This is particularly important on ‘very dynamically sensitive’ buildings, i.e. those
with slenderness ratios above 6:1 [95], in which torsional and overturning wind-driven effects
can be increasing the need for concrete and steel in shearwalls. In the present study, this
limitation was overcome by constraining the optimization through the maximum 6:1 slenderness
ratio threshold. However, its effect on the building envelope, specifically on the amount of
aluminum frame and steel anchors required was not modeled.
The consideration of wind and building shape effects on the structure and envelope of very
dynamically sensitive buildings would entail the use of finite element modeling (FEM)
techniques. This process would bring the need for accurate modeling of the lateral and vertical
resisting systems, such as shearwalls, columns, slabs, and beams, as well as all the components
that support the building envelope. The downside is that modeling all these elements can
significantly alter the tool’s performance since the number of Dynamo geometries can
substantially increase. Currently, this number is close to 5,000, considering both site conditions
49
and building modeled elements (simplified faces representing the façade, slabs, and building
sections) which yielded an average running time of 3.3 minutes per generated data point. It is
estimated that considering the additional elements required in the FEM approach, the number of
Dynamo geometries could reach up to 9,000, hence, considerably impacting total running times.
However, this decrease in performance can be offset by optimizing the existing code as
suggested at the end of this section.
Additionally, computational fluid dynamics (CFD) simulations can be implemented for a more
accurate calculation of wind loads as a function of building shape and wind directionality.
Currently, GenGHG calculates these using the simplified wind load dynamic procedure
described in Chapter 4 of the NBC2015 [95]. Although CFD results are not accepted by the
NBC2015 for detailed structural analyses, CFD models have been proven to be an effective
method for analyzing the wind loading trade-offs of different building shapes during early design
stages [107]. CFD models have also been used for the estimation of wind effects on pedestrian
level comfort [108], expanding the metrics that can be incorporated into the ARCH score.
In very dynamically sensitive buildings the wind tunnel procedure is the only accepted method
for the calculation of wind loads [95], it involves the construction of a downscaled version of the
building and the measurement of wind speeds in several experimental settings [95]. CFD models
can be used during the optimization step as part of the EE estimation process to produce the set
of high-performing alternatives. These can be later used in the wind tunnel procedure. Novel
CFD third-party Dynamo packages, such as Butterfly® for Dynamo [109], can facilitate the
process. Butterfly is based on the open-source CFD tool named OpenFOAM© [110]. The
implementation of CFD can ultimately increase the advantages of relying on generative design
tools as a more comprehensive exploration of the design space can be conducted introducing
futher design trade-offs. On the other hand, running times may also be significantly increased.
Another important limitation is that GenGHG only considered the superstructure within the EE
system boundaries, leaving out the concrete and steel required for foundation and offshoring
structural elements. The driving factor for this decision was the high uncertainty involved in their
design, which is particularly sensitive to project-specific factors such as soil type, soil density,
and sometimes in the structural designer and contractor’s preference [111], all of which can vary
significantly from project to project. Further, capturing this variability requires an important
50
sample of building structural designs including geotechnical reports. The lack of such database
was the underlying reason for not including this aspect in the analysis. However, the
incorporation of foundation components can be performed in future studies by using a
combination of case studies and structural design formulas provided in building codes which
vary according to the parameters described above. It is expected that the incorporation of the
foundation would increase the EE premium for height described in section 2.4.1. To
contextualize this impact, a 2018 study in a high-rise office project, found that the foundation
can contribute up to 22% of the total EE [66].
Another limitation of the present study is the exclusive incorporation of embodied impacts (i.e.
A1 to A5 as shown in Fig. 8) within the LCA system boundaries. Although embodied impacts
are particularly important considering short to mid-term emissions reduction targets, such as the
ones pledged by the Government of Canada for 2030 [112], the long-term GHG emissions of the
produced designs are being left unconsidered. Building rotation and interior volume space are
factors that are deeply connected to operational emissions since they can vary heating and
cooling loads, lightning requirements, and occupant comfort. The modular nature of GenGHG
and its capacity to produce parametric building designs, enables the incorporation of operational
aspects in further versions. These, however, would require a comprenhensive analysis including
the lifespan of the building, i.e. at least 50 years, and changes that can occur over this period,
particularly with respect to the GHG intensity of the electricity grid and climate change.
Lastly, there are several limitations in the calculation of the architectural score (ARCH) given
that it is partially bounded to subjective preferences. The unobstructed views metric can be
considered fairly objective, however, developers should be cautious about what they define as
target points, since a ‘better view’ may not always mean more unobstructed lines of sight to the
selected landmarks. In terms of the irregularity score, the authors acknowledge that high building
irregularity, as defined in section 2.4.3, would not always be correlated to better aesthetics. For
this reason, users can modify their subjective preferences between these two metrics or turn them
off as desired. To better capture the effect of building aesthetics in the ARCH score, future work
could incorporate the use of machine learning techniques powered by human input. This has
been conducted before through image recognition using Deep Neural Networks [113,114]. The
incorporation of Pyhton 3 in Dynamo 2.7 enables the use of many popular and open-source
machine learning (ML) libraries within Dynamo, meaning that further versions of GenGHG can
51
incorporate these ML-based methodologies. These systems may also possibilitate the exploration
of unconstrained building shapes and their effect on EE through the use of ML-based topology
optimizations for structural components, as conducted in [115].
In addition to the aforementioned limmitations and recommendations for future work, increasing
the computational performance of GenGHG is a critical next step for expanding the depth of the
design exploration process. These improvements can be achieved by using third-party packages
that rely on lower-level languages such as C and C++. The package Topologic [116], which was
built on C++, represents a promising approach for handling many of the functions used in
GenGHG. Lastly, exploring the use of interactive optimization to guide the process with human
input could be a potential method for decreasing running times while producing design solutions
that are closer to the user’s expectations and subjective preferences, as shown in [117].
52
Chapter 4 Conclusions
The conceptual design of high-rise residential buildings is not a simple process since it involves
the consideration of multiple objectives in a fast-paced and time-limited setting. The proposed
generative design tool, with its ability to measure the relative performance of multidisciplinary
objectives in a data-driven 3D environment, represents a promising approach for minimizing
embodied emissions while considering goals and constraints that are inherent during high-rise
residential building design.
This research demonstrated that the use of GenGHG in early design phases can provide industry
practitioners with high-performing solutions, enabling a wider design space exploration when
compared to traditional approaches. While GenGHG was developed specifically for developers
working in the downtown Toronto’s context, its modular and easy-to-read code flow allows the
modification of location-sensitive parameters and the addition of further improvements, such as
the incorporation of energy efficiency metrics and urban-level analyses. This without requiring
extensive programming experience given the nature of visual programming tools such as
Dynamo.
The novel geometry system developed was able to effectively generate variable design
alternatives that were suitable for optimization and randomization studies, enhancing the design
exploration process. The system produced realistic, modern-looking, building shapes with no
human input while considering the three major building sections in high-rise residential
development: tower, podium, and underground. A system with such characteristics has not been
proposed before in the literature. Along with the geometry system, a new mesh-based data
translation workflow was proposed for the incorporation of site conditions exported from the
City of Toronto 3D massing database. This mesh-based approach was able to generate 2,500
native Dynamo surfaces for a downtown Toronto case study, providing the basis for the views
analysis.
The use of GenGHG in industry practice is potentialized by the high connectivity that Dynamo
has with popular software packages in the Architecture, Engineering, and Construction (AEC)
industry. Autodesk Revit, in particular, is enhancing the incorporation of generative design
workflows as part of its core functionalities. This enables the execution of GenGHG in future
53
versions of Revit, i.e. v2021, during early design stages. However, the open-source nature of
Dynamo also allows the use of the tool without the need for licensed software.
Additionally, from the analysis of nine Toronto-based high-rise case studies, this work concluded
that the tower is the most critical building section in high-rise building design accounting for
about 60% of the total embodied emissions and showing an average GHG intensity of 275
kgCO2-eq/m2. It was determined that although area-based GHG intensities are commonly used in
building LCAs, they are not reliable for comparing buildings and building sections with different
floor to floor heights. Building volume normalized intensities were used to overcome this issue,
which led to the conclusion that the underground is the section with the highest GHG volumetric
intensity with 96 kgCO2-eq/m3 followed by the tower and podium with 85 and 65 kgCO2-eq/m
3,
respectively. This considering that foundation and offshoring elements were outside of the scope
of the estimation.
After validating the tool in a case study, it was observed that (as expected) there is a linear
relationship between EE and the FAR, while the ARCH score and EE did not show a
recognizible trend. When comparing a Pareto front solution with a randomly generated one with
equal area and height, the former had a 31% larger ARCH score with 7% less EE (corresponding
to 1 GgCO2-eq). In terms of the effect of design constraints, it was assessed that the City of
Toronto Tall Buildings Regulations have a growth-inducing impact on EE by forcing the
creation of slimmer, taller, towers. A 10% increase in the floor plate area constraint was
determined to yield a 6% reduction in embodied emissions while generating a minimal impact on
building width, i.e., 3 m or 2% percent of the height for a 149 m tall tower.
This research elucidated the potential of generative design strategies during the conceptual
design of high-rise residential buildings, proposed novel systems for the generation and
evaluation of design alternatives, and delivered GenGHG, a ready-to-use, open-source tool based
in Dynamo. Additionally, this work proposed next steps for overcoming many of the limitations
encountered, such as the incorporation of energy efficiency analysis, detailed wind-structure and
soil-structure evaluations through CFD and FEM, and improvements in the calculation of the
architectural score through ML-based image recognition techniques. Lastly, GenGHG has the
potential of being one of the strategies that countries can use for meeting, or getting closer to,
GHG emissions reduction targets at a city scale.
54
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63
Appendices
Appendix 1
Conceptual design assumptions for design generation
Type Description
Architectural The tower section contains 100% of the residential units.
Architectural The podium section contains retail outlets and amenities.
Architectural The podium section is maintained fixed across building alternatives
Architectural The underground section contains parking and storage, planned in accordance with zoning
by-laws.
Geometric The tower shape can vary within the site boundary.
Geometric The podium and underground sections preserve the same shape of the site boundary.
Appendix 2
Resources used for documenting goals and constraints. Name Type
City of Toronto Tall Buildings Guidelines (2013) Urban-level building code
City planning - Zoning By-Law NO 560 (2013) Urban-level building code
Ontario Building Code Compendium (2006) Multidisciplinary building code
Ontario Requirements for New High-Rise Buildings Fire-proofing building code
National Building Code of Canada (2015) Multidisciplinary building code
CSA - Concrete Design Handbook A23.1/2/3 Structural building code
Benchmarking current conceptual high-rise design processes (2010) [24] Stakeholders goals
Conceptual High-Rise Design A design tool combining stakeholders and
demands with design (2016) [43] Stakeholders goals
A Knowledge-Based Approach to Preliminary Design of Structures
(1990) [44] Stakeholders goals
64
Appendix 3
Conceptual design assumptions and scope for design evaluation
Related goal Description
EE Lateral and vertical stability system: tube in tube according to case studies.
EE Floor system: flat slab without column heads.
FAR Area of walls and columns is neglected.
FAR Area of elevator, stairs, and MEP (mechanical, electrical, and plumbing) shafts is not included.
FAR Neglects the effect of sharp corners for the GRA calculation.
ARCH The architectural score neglects the effect that highly irregular buildings may have on torsional structural forces.
ARCH Assumes that “aesthetics” is connected to building irregularity.
ARCH Assumes that a better ‘view’ involves more unobstructed lines of sight to landmarks, parks, and the water bodies.
65
Appendix 4
Calculation of underground area as a function of gross residential area using the City of Toronto Zoning By-law
569-2013.
Requirement type Car parking - tenant requirement
Car parking - visitor
requirement
Bike parking
requirement
Value
Minimum rate per unit
type
Maximum rate per unit
type
Minimum rate per unit
type Minimum rate per unit
ba 1b 2b +3b ba 1b 2b +3b ba 1b 2b +3b
Zoning By-law 569-
2013 minimum rate per
unit type
0.3 0.5 0.8 1 0.4 0.7 1.2 1.5 0.1 0.1 0.1 0.1 1
Average number of units
extracted from case
studies
39 302 164 44 39 302 164 44 39 302 164 44 549
Average area per unit
type [m2] 45 60 83 106 45 60 83 106 45 60 83 106 73.5
Total parking spaces
required
12 151 132 44 16 212 197 66 4 31 17 5 549
339 491 57 549
Minimum parking spaces
to be provided (50% of
the rates per unit type)
170 246 29 549
Total residential area
[m2] 38,151
Parking spaces required
per m2 of gross
residential area
0.0044 0.0064 0.0007 0.0144
Average area per parking
spot including corridors
[m2]*
34.5 34.5 34.5 2.2
Underground area
required per gross
residential area [m2]
0.153 0.222 0.026 0.032
Total minimum
underground area
required per gross
residential area [m2]**
0.278
Total maximum
underground area per
gross residential area
[m2]**
0.369
ba: bachelor apartment. 1b: one bedroom apartment, 2b: two bedroom apartment, +3b: three or more bedroom apartment.*extracted from case
studies; **Includes a 1.32 area factor for building operations such as garbage disposal, machine rooms, and lockers; calculated from the case
studies.
66
Appendix 5
Transportation distances
Type Materials or activities Average distance [km] ± std [km] Samples Source
Structure Concrete transportation (from manufacturing plant
to site) 14 ± 11 5
Appendix 6
Structure Rebar transportation (from manufacturing plant to
site) 33 ± 6 9
Appendix 7
Façade Transportation (from manufacturing plant to site) 1099 ± 589 4 Appendix 8
Excavation Transportation (from site to landfill/fill site) 98.5 ± 65 8 Appendix 9
std: standard deviation
Appendix 6
Structure: Concrete transportation (from manufacturing plant to site). Extracted from Google Maps [118].
ID Name Distance to downtown Toronto [km]
1 Lafarge Canada (54 Polson St, Toronto, ON M5A 1A5) 3.4
2 Lafarge Canada (535 Commissioners St, Toronto, ON M4M 1A5) 7.2
3 Dufferin Concrete Toronto Plant (650 Commissioners St, Toronto, ON M4M
1A7) 7.6
4 Bathurst Mobile Ready-mix Inc (107 Manville Rd, Scarborough, ON M1L 4J2) 20.6
5 ML Ready Mix Concrete Inc. (29 Judson St, Etobicoke, ON M8Z 1A4) 31.7
Average 14
Standard deviation (std) 11
The selection of the manufacturing plants was based on publicly available data extracted from
Google Maps. The selected plants are those expected to serve the areas of the downtown and
central waterfront Toronto area according to different manufacturers websites. Hence, the
selected values are specific for the case study used for the tool demonstration in section 3.3.
However, these inputs can be changed by the user. The process involves opening GenGHG in
Dynamo’s visual programming interface and referencing a new .csv file for the new project
specific LCA factors. The data structure of the .csv file should match the one shown in Table 10,
or in the sample .csv files delivered as part of GenGHG supporting files.
67
Appendix 7
Structure: Rebar transportation (from manufacturing plant to site). Extracted from Google Maps [118].
ID Name Distance to downtown Toronto [km]
1 Mansteel Limited (105 Industrial Rd, Richmond Hill, ON L4C 2Y4) 40.6
2 Ontario Rebars (9 Cedar Ave, Thornhill, ON L3T 3W1) 33.6
3 Salit Steel Concord (300 Connie Crescent, Concord, ON L4K 5R2) 40.3
4 Woodbridge Steel Ltd (127 Woodstream Blvd, Woodbridge, ON L4L 7Y5) 37.4
5 Harris Rebar (980 Intermodal Dr, Brampton, ON L6T 0B5) 37.5
6 SSAB Swedish Steel (2425 Matheson Blvd E, Mississauga, ON L4W 5K4) 25
7 National Concrete Accessories (172 Bethridge Rd, Etobicoke, ON M9W 1N3) 26.4
8 Salit Steel (43 Bethridge Rd, Etobicoke, ON M9W 1M6) 26.2
9 EMJ Metals (305 Pendant Dr, Mississauga, ON L5T 2W9) 33.3
Average 33
Standard deviation (std) 6
Appendix 9
Excavation: Transportation (from site to landfill/fill site) Extracted from Google Maps [118].
ID Name Distance to downtown Toronto [km]
1 WM - 14301 Highway 48 Stouffville, Ontario 50
2 Walker - 3081 Taylor Rd, Niagara Falls or 685 River Road, Welland 124
3 WM - 5768 Nauvoo Rd/ 8039 Zion Line Watford, ON N0M 2S0 79
4 GFL Waterfront - Unwin Ave 250
5 GFL Pickering - 1070 Toy Ave 44
6 GFL Fenmar - 38 Fenmar 38
7 Walker - 3081 Taylor Rd, Niagara Falls or 685 River Road, Welland 124
5 NewAlta - 65 Green Mountain Road, Stoney Creek, ON WM 79
Average 98.5
Standard deviation (std) 65
Appendix 8
Façade: Transportation (from manufacturing plant to site). Extracted from Google Maps [118].
ID Name Distance to downtown Toronto [km]
1 Kawneer Manufacturing Plant (Bloomsburg, P.A. - U.S.A) 543
2 Kawneer Manufacturing Plant (Cranberry, P.A. - U.S.A) 479
3 Kawneer Manufacturing Plant (Springdale, A.R. - U.S.A) 1743
4 Kawneer Manufacturing Plant (Dublin, G.A. - U.S.A) 1629
Average 1099
Standard deviation (std) 589
68
Appendix 10
EE factors from Ecoinvent [84] for transportation
Description EE [kg CO2-eq] Unit
Freight, lorry 3.5-7.5 metric ton, EURO3 0.51284 per km
Machine operation, diesel, >= 74.57 kW, high load factor 149.125 per day
Diesel production low-sulfur 0.52361 per kg
Appendix 11
Calculation of EE for transportation of excavated materials
Description Value Unit
Average capacity (lorry 3.5-7.5 metric ton) 5500 kg
Transportation distance (Appendix 9) 98.5 km
Total for Freight, lorry 3.5-7.5 metric ton, EURO3 (Appendix 10) 50.5 [kg CO2-eq]
Average dry soil density for uncompacted sandy-clay loam 2355 kg/m3
Cubic meters of soil per lorry 2.3 m3
Total EE per cubic meter of excavated material 21.6 [kg CO2-eq]/m3
Although the present study assumes that all the excavated material must be send out to landfill, it
is also possible that a portion of it might go to infill. This aspect was not considered due to lack
of data supporting this claim, and due to the already low contribution of excavation to the total
EE (as seen in seen in Table 9,
Appendix 12
Calculation of EE for concrete pumping
Description Value Unit
Average capacity (Truck-Mounted Boom Pump - 63Z-Meter) 160 m3/h
Hours [h] per cubic meter 0.01 h/m3
Total for 1 day of machine operation, (2 high load factor diesel, >= 74.57
kW equipment: concrete mixer and concrete pump) (Appendix 10) 298.3 [kg CO2-eq]/d
Days [d] per cubic meter 0.15 d/m3
Total EE per cubic meter of excavated material 44.7 [kg CO2-eq]/m3
69
Appendix 14
Calculation of EE for transportation of rebar
Description Value Unit
Average capacity (lorry 3.5-7.5 metric ton) 5500 kg
Transportation distance (Appendix 5) 33 km
Total for Freight, lorry 3.5-7.5 metric ton, EURO3 (Appendix 10) 17.11 [kg CO2-eq]
kg of rebar per lorry 5500 kg
Total EE for transportation per Mg of rebar 3.11 [kg CO2-eq]/Mg
Appendix 15
Calculation of EE for transportation of façade components (unitized window walls)
Description Value Unit
Average capacity (lorry 3.5-7.5 metric ton) 5500 kg
Transportation distance (Appendix 5) 1099 km
Total for Freight, lorry 3.5-7.5 metric ton, EURO3 (Appendix 10) 563 [kg CO2-eq]
kg of 1m2 of unitized window wall (market average) 39 kg
m2 of unitized window walls per lorry (calculated using weight) 140 m2
m2 of unitized window walls per lorry (calculated using dimensions) 61 m2
maximum m2 of unitized window walls per lorry (estimation) 61 m2
Total EE for transportation per m2 of façade components (average) 9.17 [kg CO2-eq]/m2
Appendix 13
Calculation of EE for transportation of concrete
Description Value Unit
Average capacity (Concrete mixer 6cy) 6.1 m3
Transportation distance (Appendix 5) 14 km
Total for Freight, lorry 3.5-7.5 metric ton, EURO3 (Appendix 10) 7.2 [kg CO2-eq]
Average concrete density 2400 kg/m3
Cubic meters of soil per lorry/concrete mixer 6.1 m3
Total EE for transportation per cubic meter of concrete 1.2 [kg CO2-eq]/m3
70
Appendix 17
Spandrel panel composition (sw)
Glass Spandrel Value Units GWI [kg CO2-
eq]
Gypsum plaster board, regular, generic, 6.5 - 25 mm, 10.725 kg/m2
(for 12.5 mm), 858 kg/m3
OneClick LCA
(Ontario) 1.0 m2 80.0
Hot-dip galvanized steel sheets, recommended sheet steel thickness
range: 0.4 - 3.0 mm, zinc coating: 20 micrometers (0.28kg/m2 sheet
steel)
OneClick LCA
(Ontario) 1.0 m2 11.0
Processed glass, non-low-e coating, 72x84in, 72x96in, 96x130in,
130x204in, 2500 kg/m3, Solarban, Sungate, Vistacool, Solarcool,
Clarvista, Herculite (Vitro)
OneClick LCA
(Ontario) 1.0 m2 4.3
Steel, stainless, 304 (NREL) OneClick LCA
(Ontario) 5.4 kg 22.8
Mineral Wool 1 in OneClick LCA
(Ontario) 1.0 m2 6.1
Total spandrel panels 1.0 m2 124
Appendix 18
Exterior wall composition (ew)
Metal wall Value Units GWI [kg CO2-
eq]
Gypsum plaster board, regular, generic, 6.5 - 25 mm, 10.725 kg/m2 (for
12.5 mm), 858 kg/m3
OneClick LCA
(Ontario) 1.0 m2 3.6
Rock wool insulation panels, unfaced, generic, L= 0.037 W/mK, 150
kg/m3 (applicable for densities: 100-150 kg/m3)
OneClick LCA
(Ontario) 1.0 m2 7.7
Gypsum board with glass mat sheathing, 1/2in, 2.03 lb/ft2 (Gypsum
Association)
OneClick LCA
(Ontario) 1.0 m2 4.4
Air and water barrier system, mechanically fastened, 0.184 lbs/ft2,
Tyvek (DuPont)
OneClick LCA
(Ontario) 1.0 m2 1.2
Steel façade panel, 10-36inx6-40ft (Metal Construction Association) OneClick LCA
(Ontario) 1.0 m2 30.4
Steel, stainless, 304 (NREL) OneClick LCA
(Ontario) 8.6 kg 36.4
Mineral wool 1 in OneClick LCA
(Ontario) 1.0 m2 6.08
Total exterior walls 1.0 m2 90
Appendix 16
Window wall composition (ww)
Windows Source
(Location) Value Units GWI [kg CO2-eq]
Thermally improved aluminum extrusions (profiles), anodized
(Aluminum Extruders Council (AEC))
OneClick LCA
(Ontario) 13.1 kg 261.1
Processed glass, double-pane IGU, 72x84in, 72x96in, 96x130in,
130x204in, 2500 kg/m3, Solarban, Sungate, Vistacool, Solarcool,
Clarvista, Herculite (Vitro)
OneClick LCA
(Ontario) 20.0 kg 27.3
Total window walls 1.0 m2 288
71
Appendix 19 Calculation of EE for the installation of façade components
Unit process Coverage Assumptions Source
Truck unloading 2014-18
Global
Units unloaded at the same time: 4 panels (1,2x3,2m each). [119]
Unloading time: 8 minutes.
Unloading vehicle: Small hoisting rig (high load machine operation).
Moving on-site 2014-18
Global
Moving vehicle: forklift.
Moving rate: 12 units per hour . [120]
Hoisting 2014-18
Global
Hoisting rate: 12 units/h. [119]
Hoisting machine: Crane - on-site elevator with 32m/min speed, 8 panels at the
same time (1.2x3.2m each). [120]
Anchoring 2001-18
Global
Number of anchors: four steel screws of 64g per panel. [121]
Energy needed to drill is not considered in the study.
Construction hardware is not included.
Placement machine: 5KW vacuum lifter, 10 min/panel.
Uses electricity (Ontario’s electricity mix).
Sealing 1997-18
Global
Mass of sealant: 5.7g per panel (1.2 by 3.2m each). [122]
Construction hardware is not included.
Fire protection material: Glass wool. [121]
WW product system does not include fire-proofing.
Total = 2.5 kgCO2-eq / m2 façade
Calculated by modeling the process described above using OpenLCA [123] software
72
Appendix 20
Fuel use for excavation equipment per cubic meter of excavated material, adapted from [97].
Type Comb. Q
hp BSFC TAF D fs ft d B C S t Fuel use
[gal]
Fuel
use [l]
Density
[kg/l]
Fuel use
[kg]
Diesel factor
[kg CO2-
eq/kg] [84]
EE [kg
CO2-eq]
Bulldozer 1 1.308
564 0.367 1.01 300 0.236 0.6 2.4 0.8 2.0 0.5 1.044
fs (soil type factor): sand–gravel = 0.236; sandy-clay loam = 0.217; common earth = 0.166; clay = 0 Excavator. D in [ft]
Excavator 1 1.308
400 0.367 1.01 8.465 3.317 12 3 1.8 7.0 0.8 5.8 0.5 3.039
fs (soil type factor): common earth = 8.465; sandy-clay loam = 14.907; sand–gravel = 16.412; hard clay = 0
ft (scraper type factor): excavator = 3.317; truck-mounted = 4.165; trench-box = 0
Note: EE, total emissions (kg CO2-eq) per cubic meter of excavated material; Q, quantity of soil dozed/moved/excavated (cy, 1.30795 cy = 1 m3); hp, engine horsepower; EFss, steady-state emission factor
(g/hp-h); BSFC, brake specific fuel consumption (gal/hp-h); TAF, transient adjustment factor (unitless); DF, deterioration factor (unitless); SPM, adjustment to PM emission factor for fuel sulphur content
(g/hp-h); HC, in-use adjusted hydrocarbon emissions (g/hp-h); 453.6, conversion factor from pounds to grams; 0.87, carbon mass fraction of diesel; 44/12, ratio of CO2 mass to carbon mass; D, distance (ft:
bulldozer or miles: dump truck), used 98.5m (61.205 miles); d, trench depth (ft); B, bucket capacity (cy); C, loading capacity (cy); S, average hauling speed (mph); t, load–dump time (min). Note: Q, quantity
of soil dozed/moved/excavated (cy); hp, engine horsepower; BSFC, brake specific fuel consumption (gal/hp-h); TAF, transient adjustment factor (unitless); D, distance (ft) – miles for truck; d, depth (ft); B,
bucket capacity (cy); C, loading capacity (cy); S, speed (mph); t, cycle time (min).
Formulas adopted from: A. Hajji, The use of construction equipment productivity rate model for estimating fuel use and carbon dioxide (CO 2 ) emissions.
Case study: bulldozer, excavator and dump truck, Int. J. Sustain. Eng. 8 (2015) 111–121. https://doi.org/10.1080/19397038.2014.962645. [97]
73
Appendix 21 Data for the structural quantities’ estimation model.
Values extracted from (Foraboschi, P. et al., 2014) [55] Calculated ratios
Height
[m]
Rebar Concrete
GFA
[m2]
Rebar
[Mg/m2]
Concrete ratio
[m3/m2] Core
[kN]
Frames
[kN]
Core and
Frames [kg]
Core
[kN]
Frames
[kN]
Core and
Frames [m3]
80 3096 253 341452 51600 3156 2326 8000 0.043 0.29
120 6900 591 763910 115000 7392 5200 17280 0.044 0.30
160 15624 1675 1764027 260400 20940 11954 36000 0.049 0.33
200 28664 3451 3274787 477740 43134 22131 57800 0.057 0.38
240 49490 7406 5801797 819842 92540 38765 105840 0.055 0.37
280 90121 12969 10512273 1502028 162115 70706 189280 0.056 0.37
The volume of concrete for core and frames was calculated using the following conversion rates: 2400 kg/m3 as the density of concrete,
and 101.97 kN/kg for converting kN to kg.
y = 7E-05x + 0.0371R² = 0.8319
0.000
0.010
0.020
0.030
0.040
0.050
0.060
0.070
0 50 100 150 200 250 300
Reb
ar
use f
or
co
re a
nd
fra
mes
[Mg
/m2]
Building height [m]
Regression model for the estimation of rebar use per floor area
y = 0.0005x + 0.2562R² = 0.8141
0.000
0.050
0.100
0.150
0.200
0.250
0.300
0.350
0.400
0.450
0 50 100 150 200 250 300Reb
ar
use f
or
co
re a
nd
fra
mes [
m3/m
2]
Building height [m]
Regression model for the estimation of concrete use per floor area
74
Appendix 22
Calculation of the FAR
Abbreviation Value Unit Source
- Number of transport elevator shafts per 100 units 1.33 -
OBC2012 [86] and the Ontario
Requirements for New High-Rise
Buildings document [87]. Values
extracted from [124].
- Number of service elevator shafts per 100 units 0.33 -
- Average unit size 73.5 m2
- Average ratio of shaft area over GFA according to
average occupant loads* seen in the case studies 0.04 -
NTE Number of transport elevators per m2 of GFA 1.9E-04 -
NSE Number of service elevators per m2 of GFA 4.7E-05 -
AES Area of single elevator shaft 4.48 m2
ATS Area of typical stair shaft 26.82 m2
AME Estimated share of MEP openings area per m2 of GRA 0.01 -
TNE Total number of elevators
GFAi Gross floor area for floor "i" - m2
GRAt Building total gross residential area (includes corridors
but excludes shafts) - m2
FAR Floor area ratio
* Occupant load: The number of persons for which a building or part of building is designed. The occupant load factors are included in
Table 3.1.17.1 of the OBC.
𝑇𝑁𝐸 = 𝑟𝑜𝑢𝑛𝑑(𝑁𝑇𝐸 ∗ 𝐺𝐹𝐴, 0) + 𝑟𝑜𝑢𝑛𝑑(𝑁𝑆𝐸 ∗ 𝐺𝐹𝐴, 0)
𝐺𝑅𝐴𝑡 =∑𝐺𝐹𝐴𝑖 ∗ (1 − 𝐴𝑀𝐸) − 𝑇𝑁𝐸 ∗ 𝐴𝐸𝑆 − 𝐴𝑇𝑆
𝑛
𝑖=1
𝐹𝐴𝑅 = 𝐺𝑅𝐴 / 𝑆𝑖𝑡𝑒 𝑎𝑟𝑒𝑎
75
Appendix 23
Three- and two-dimensional superposition between solution B1 and A2
A2
B1
3D
Plan
