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An expert system for mix design of high performance concrete
Muhammad Fauzi Mohd. Zain*, Md. Nazrul Islam1, Ir. Hassan Basri2
Department of Civil and Structural Engineering, Faculty of Engineering, Universiti Kebangsaan Malaysia,
43600 UKM Bangi, Selangor Darul Ehsan, Malaysia
Received 5 March 2004; received in revised form 20 August 2004; accepted 14 October 2004
Available online 10 December 2004
Abstract
This paper describes a prototype expert system called HPCMIX that provides proportion of trial mix of High Performance Concrete (HPC)
and recommendations on mix adjustment. The knowledge was acquired from various textual sources and human experts. The system was
developed using hybrid knowledge representation technique. It is capable of selecting proportions of mixing water, cement, supplementary
cementitious materials, aggregates and superplasticizer, considering the effects of air content as well as water contributed by superplasticizer
and moisture conditions of aggregates. Similar to most expert systems, this system has explanation facilities, can be incrementally expanded,
and has an easy to understand knowledge base. The system was tested using a sample project. The system’s selection of mix proportions and
recommendations regarding mix adjustment were compared favourably with those of experts. The system is user-friendly and can be used as
an educational tool.
q 2004 Elsevier Ltd. All rights reserved.
Keywords: High performance concrete; Mix design; Mix adjustment; Expert systems; Knowledge-based systems; Hybrid knowledge representation
1. Introduction
The selection of mix proportions is the process of
choosing suitable ingredients of concrete and determining
their relative quantities with the object of producing as
economically as possible concrete of certain minimum
properties, notably strength, durability, and a required
consistency [1]. Because the ingredients used are essentially
variable and many of the material properties cannot be
assessed truly quantitatively, selecting proportions for
concrete can also be defined as the process of finding the
optimum combination of these ingredients on the basis of
some empirical data as stated in relevant standards,
experience, and some rules of thumb [2].
Concrete mix design involves complicated issues, and
the correct ways to perform this can be achieved with
0965-9978/$ - see front matter q 2004 Elsevier Ltd. All rights reserved.
doi:10.1016/j.advengsoft.2004.10.008
* Corresponding author. Tel.: C603 89216223; fax. C603 89216147.
E-mail addresses: fauzi@vlsi.eng.ukm.my (M.F. Mohd. Zain),
nazrul@vlsi.eng.ukm.my (M. Nazrul Islam), drhb@vlsi.eng.ukm.my
(I. Hassan Basri).1 Tel.: C603 89216819; fax: C603 89216147.2 Tel.: C603 89216100; fax: C603 89216147.
experts’ advice and experience [3]. Mix design of High
Performance Concrete (HPC) is more complicated because
HPC includes more materials, like superplasticizer and
supplementary cementitious materials (e.g. silica fume, fly
ash, fillers, etc.). In addition, maintaining a low water-
binder ratio with adequate workability makes the design
process more complicated. Traditionally, experienced civil
engineers, largely based on their experiential knowledge,
do the job of mix design [4]. However, experts are not
always available, nor do they always have time to consult
all possible references, review available data, and so on.
Some companies do not have personnel with the experi-
ence to make necessary decisions regarding concrete mix
design. The conventional computer programs are useful
only in manipulating the numerical data and providing
mathematical reasoning for the final selection. They lack
the intuitive reasoning based on heuristic knowledge such
as experience and rules of thumb [5]. Many factors
influence concrete mix design, and their mutual relation-
ship is so complicated that it is impossible to formulate
mathematical models to express their mutual actions and
reactions [6]. In addition, adjustments of trial mixes are
always performed by taking into account the information
Advances in Engineering Software 36 (2005) 325–337
www.elsevier.com/locate/advengsoft
M.F. Mohd. Zain et al. / Advances in Engineering Software 36 (2005) 325–337326
from concrete quality tests, experts’ advice and experience.
It is believed that the problem of mix design and
adjustment of HPC can be alleviated if the engineer’s
knowledge can be augmented with some ‘expert system’
for affirming his judgment.
This paper describes a prototype expert system called
HPCMIX. The purposes of developing HPCMIX were to
improve the process of selecting and proportioning HPC
constituents and to make the knowledge of HPC easily
available to the concrete industry. It is also capable of
diagnosing causes of mix performance failure and giving
recommendations on corresponding performance
adjustment. However, like other expert systems, the
developed expert system will serve as a decision support
system; it will not replace completely human expert’s
decision making.
2. Expert systems and concrete mix design
An expert system is defined as ‘a computer program
designed to model the problem-solving ability of a human
expert’ [7]. It utilises observed or available information to
produce ‘high grade’ knowledge and solves problems by
qualitative reasoning ‘using the heuristic knowledge of the
human expert’ [8]. Expert systems are most useful when the
knowledge is based on heuristics, which is often the case in
concrete mix design. Since concrete mix design and
adjustments are somewhat complicated, time-consuming
and tedious tasks, and also because it is not always possible
to be helped by the experts, there were some efforts to
develop expert system for concrete mix design. These
systems [2,3,9,10] give proportion of concrete mix
especially for normal concrete. A brief review of these
systems is available elsewhere [11]. Most of these systems
are rule-based systems, work using DOS operating system
and do not consider the cost of concrete mix selected. None
of these systems can diagnose causes of performance failure
of trial mix and give recommendations on corresponding
performance adjustment. Most importantly, they do not
consider the criteria of mix design for HPC such as
maintaining a low water-binder ratio and use of super-
plasticizer, silica fume, and so on. Therefore, these systems
cannot be used for mix design and adjustment of high
performance concrete.
3. Development of the HPCMIX
3.1. Knowledge acquisition
Knowledge for the HPCMIX was acquired from text-
books and manuals written by experts and related
professional institutions [1,12–21], research papers from
journals and conference proceedings [3,22–29] and experts
involved in concrete production. Thus knowledge was
acquired by text analysis (i.e. collection of knowledge from
the literature) and interviewing experts. Only unstructured
interviews of several experts involved in teaching, research
and consultation of concrete production were performed
in this project. Experts were asked to describe their
knowledge about the selection of concrete proportions,
diagnosing the problems in mix design and adjustments,
and their solutions. However, the main source of
knowledge was the literature mentioned above. By
analysing the knowledge from these sources, a more
objective perspective of the most appropriate expertise was
achieved, instead of being restricted to a single view
preferred by a particular expert. It may be relevant to
mention here that acquiring knowledge from these sources
was felt to be the most difficult and time-consuming task in
the prototype development process.
The mix design procedure for HPC developed by Aitcin
[15] was used as the mix design procedure for the HPCMIX
because of its wide acceptability among the experts in
Malaysia (Alternatively, in future, any other state-of-the-art
mix design method can be added to the system as a new
module without affecting overall performance of the
system). The Aitcin method follows the same approach as
ACI Committee 211 [18]. It is a combination of empirical
results and mathematical calculations based on the absolute
volume method. A flow chart of this method is presented
latter in this paper (Section 4.4). The procedure is initiated
by selecting five different mix characteristics or materials
proportions in the following sequence: water-binder
ratio, water content, superplasticizer dosage, coarse
aggregate content and entrapped air content. The suggested
water-binder ratio is obtained from a ‘compressive strength
vs. water-binder ratio’ graph for a given 28-day compressive
strength. The mixing water content is determined on the
basis of saturation point of superplasticizer. The super-
plasticizer dosage is deduced from the dosage at the
saturation point. The coarse aggregate content is obtained
as a function of the typical particle shape. The method
suggests using 1.5% as an initial estimate of entrapped air
content, and then adjusting it on the basis of the result
obtained with trial mix.
The flow diagram developed for the acquired knowledge
regarding mix performance adjustment is shown in Fig. 1.
The diagram shows that three criteria were considered for
judging the performance of a mix, i.e. strength, workability
and durability. For example, if a mix fails in strength
performance, then information is required about test results
regarding any of the following to find out possible causes of
performance failure: (i) fracture pattern, (ii) bond failure
pattern, (iii) passage of fracture surface, and (iv) effect of
water-binder ratio. On the other hand, if the workability
performance is inadequate then it is required to know which
workability performance of the following is inadequate:
(i) rapid slump loss, (ii) low slump, or (iii) inadequate
workability. The flow diagram developed for the purpose of
cost estimation of a designed mix is shown in Fig. 2.
Fig. 1. Flow diagram of mix performance adjustment.
Fig. 2. Flow diagram of cost estimation.
M.F. Mohd. Zain et al. / Advances in Engineering Software 36 (2005) 325–337 327
The figure shows that unit cost of ingredients, mix
composition and batch size are the required information
for cost estimation of a concrete mix.
3.2. Knowledge representation
Through the efforts of researchers in artificial intelli-
gence, a number of effective ways of representing knowl-
edge in a computer were developed [7]. The selection from
these knowledge representation techniques depends on the
nature of the expertise to be computerized, as well as the
practical capabilities and facilities of the expert system tool
used [30]. In developing HPCMIX, a hybrid approach of
knowledge representation (i.e. rule and frame systems) was
followed using Kappa-PC expert system shell [31]. Thus,
the domain of mix design of HPC was modelled using
object-oriented approach and production rules.
As an illustration of object-oriented approach, Fig. 3
shows the object hierarchy of Binder class. It consists of two
subclasses namely Cement and SupplCemMats (Supplemen-
tary Cementitious Materials). Each of these subclasses
includes several instances. For example, SupplCemMats
subclass consists of SilicaFume, FlyAsh, GGBS (ground
granulated blast-furnace slag), RiceHuskAsh and Others
instances. The attributes of objects were defined as object
slots. Slots can be thought of as descriptions of a particular
object. They add detail structure, list attributes or properties
which can be single or multiple-valued, textual strings or
numeric, or even Boolean. Slot values can be pre-defined,
restricted to a range or set of pre-specified possible values,
user-defined or determined from user consultations with
Fig. 3. Object hierarchy of the Binder class.
M.F. Mohd. Zain et al. / Advances in Engineering Software 36 (2005) 325–337328
the system. For example, the slots of the instance FlyAsh
(Fig. 3) includes, among others, content in percent, specific
gravity and content in kg/m3 (kilogram per cubic metre).
Table 1 shows these slots and the corresponding slot values.
Interactions among objects, instructions from one object
to another, and processes of objects were codified in the form
of methods, functions and rules. The first approach involves
enhancing objects so that they represent the behaviour of the
things to which they correspond. Methods are written in KAL
(Kappa-PC Application Language) and stored within the
object. The next approach to representing processes involves
functions. Functions are also written in KAL and can either
be user-defined or built-in the system. Kappa-PC provides a
library of over 300 functions that allow for the manipulation
of its knowledge base. The third approach involves using
rules to represent the relationship between causes and effects
which specifies the conditions under which a particular
action or inference can occur. The syntax for methods,
functions and rules is identical; therefore, the same syntax
can be used to add an object, to write a method or to create a
rule. An example of a simple rule written for the coarse
aggregate content of the mix design module is shown below.
If:
Table 1
Descripti
Instance:
Slot Nam
ContentP
SpecificG
ContentK
Shape of the coarse aggregate is cubic
Then:
Coarse aggregate content of the mix should be1100 kg/m3.
In KAL format, this rule was written as:
If:
CoarseAgg: ShapeOfAgg #ZCubicThen:
CoarseAgg: ContentKgPerM3 #Z1100;on of an Instance using Slots
FlyAsh, Parent Class: SupplCemMats
e Slot Value Comment
ercent 10.00 User defined value
ravity 2.50 User defined value
gPerM3 53.19 System derived value
Where, CoarseAgg is an instance representing coarse
aggregate; ShapeOfAgg is a slot of CoarseAgg instance
representing the shape of the coarse aggregate; Cubic is the
value of the slot ShapeOfAgg representing that the shape of
the coarse aggregate is cubic; and so on. There are two
approaches for evaluating production rules: backward
chaining and forward chaining [7,32–34]. The HPCMIX
uses a forward-chaining or data-driven inference
mechanism.
4. Knowledge base modules of the HPCMIX
The HPCMIX knowledge base consists of three design
related modules namely Mix Design, Mix Performance
Adjustment and Cost Estimation modules and an accessory
module named General Information module. The modules
can be accessed from the main interface window shown in
Fig. 4. A brief description of each of the above modules is
given in the following sections.
4.1. Mix Design module
The objective of Mix Design module is to proportion
HPC mixes. It consists of four submodules namely First
Trial Batch, Trial Batch for Laboratory, One Cubic Metre
SSD Composition (SSD stands for saturated-and-surface-
dry) and Batch Composition as shown in Fig. 5. The First
Trial Batch submodule computes proportions of concrete
mixes according to the data supplied by the user. It gives
composition of one cubic metre of concrete for field
conditions of aggregates. The Trial Batch for Laboratory
submodule helps in computing quantities for making
concrete samples for laboratory testing. This gives the
user opportunity to test mix design results in a laboratory for
desired performance requirements for a small amount of
proportioned ingredients. The One Cubic Metre SSD
Composition submodule calculates proportions for one
cubic metre of concrete for SSD conditions of aggregates.
The Batch Composition submodule activates a function to
calculate amounts for batch quantities for field conditions of
aggregates.
4.2. Mix Performance Adjustment module
The Mix Performance Adjustment module helps in
adjusting mix proportions after laboratory testing. The
interface window of this module is shown in Fig. 6.
Incorporating the knowledge that experts use in diagnosis,
the Mix Performance Adjustment module diagnoses
possible causes of performance failure of concrete mix
and recommends corresponding remedial measures. The
Quantitative Advice button (Fig. 6) of this module gives
specific quantitative recommendations on achieving various
performances of HPC mix. The module also displays
reasons for giving any recommendation.
Fig. 4. Main interface window of the HPCMIX.
Fig. 5. Interface window of the Mix Design module.
M.F. Mohd. Zain et al. / Advances in Engineering Software 36 (2005) 325–337 329
Fig. 6. Interface window of the Mix Performance Adjustment module.
M.F. Mohd. Zain et al. / Advances in Engineering Software 36 (2005) 325–337330
4.3. Cost Estimation module
If the cost estimation is felt to be necessary, it can be
carried out using the Cost Estimation module. The first step
is to input unit cost of the proportioned ingredients. A
transcript image in the Cost Estimation interface window
helps the user by displaying information about the ranges of
unit cost of concrete ingredients. Although these values vary
from country to country, this information will help the user
in getting idea about approximate unit cost of ingredients.
After inputting the unit cost of ingredients, the user gets the
costs of one cubic metre of concrete and of a particular batch
size by pressing appropriate buttons.
4.4. General Information module
Fig. 7 shows the interface window of the General
Information module. The objective of this module is to
assist the user in the efficient utilisation of the HPCMIX
prototype. The module provides a user-friendly environ-
ment whereby the various guidelines and information are
available to the user. The User Guide button assists the user
in the efficient consultation process. The user gets advice on
the fundamentals of HPC and the approaches of mix design
and adjustment of HPC through HPC Technology and
Principle of Mix Design buttons of this module. A
knowledge dictionary is also available through Knowledge
Dictionary button to assist the user with unfamiliar technical
terms. This dictionary is also useful as an educational
feature. The dictionary includes: basic definitions of
concrete mix design, HPC and expert system; types of
tests for evaluation of fresh and hardened concrete; and
statistical measures used in assessment of concrete mix
design. In addition, a general conceptual flow diagram of
HPC mix design using Aitcin method (see Fig. 7) and
various photographs showing the testing of fresh and
hardened concretes are also accessible through appropriate
buttons of this module.
5. Case study and evaluation of the HPCMIX
The objective of this case study was to evaluate the
performance of the HPCMIX consultation process and
results when it was applied to a HPC mix. The case study
was carried out on an example mix design of HPC from a
classical textbook [15]. This example was selected because
the author of this textbook is considered as one of the well-
known experts in the domain of HPC. Another expert (Dr
Hilmi Mahmud, Associate Professor, University of Malaya,
Malaysia), who has been involved in teaching, research and
consultation in the production of concrete for past fifteen
years, was asked to perform concrete mix design based on
the information of this textbook. The results of the design
obtained from HPCMIX were compared with those
reported in this textbook (referred to as Expert-1) and
those calculated by Dr Hilmi Mahmud (referred to as
Expert-2).
Fig. 7. Interface window of the General Information module showing mix design flow diagram.
M.F. Mohd. Zain et al. / Advances in Engineering Software 36 (2005) 325–337 331
The user clicked on Mix Design button of the main
interface window (Fig. 4) for mix design purpose. This
opened the interface window of the Mix Design module
(Fig. 5). From that window, the user learned about the
functions of the buttons regarding mix design submodules
and got advice about each submodule by pressing on
appropriate Advice button. The input data, consultation
process and explanation of results of each submodule of the
mix design module as well as mix performance adjustment
module are described in the following sections. These
sections also include description about user-friendliness,
advantages, disadvantages and an overall evaluation of the
system.
5.1. First trial batch
Data for the first trial batch composition of the present
case study is described in Appendix A. In this example, a
100 MPa concrete mix was designed with Type I Portland
cement, a naphthalene-type superplasticizer, a dolomitic
limestone, a siliceous natural sand and silica fume.
For each concrete ingredient of Appendix A, a related
window appeared one after another as the user continued
with data input process. For example, window that
appeared for data input related to silica fume is shown in
Fig. 8. The system provided information about each
concrete ingredient similar to that shown for silica fume
(Fig. 8). Each of these windows also displayed several
buttons namely Data Input, Advice, Main Screen, Mix
Design Screen, Back and Exit. If the user wanted to input
data about an ingredient, he pressed on the Data Input
button. As a result, the system prompted the User Request
form such as shown in Fig. 9. This form displayed the
limits of the value of the desired input parameter. After
entering the value of the parameter, the user pressed on OK
button and, as a consequence, the window for the next
input parameter automatically appeared. If the user was not
sure about the value of the parameter, he could press on
Comment button (Fig. 9) for usual range of values as
shown in Fig. 10, which also gave the sources of
information. For the beginners or students of concrete
technology who are not familiar about the parameter may
press Advice button (see Fig. 8). This operation gives
detailed information about the parameter in the second
transcript image (see Fig. 8 for advice on silica fume).
Moreover, the beginners can access to knowledge
dictionary through Knowledge Dictionary button
(see Fig. 7). These facilities make the system very
user-friendly and suitable for using as an educational tool.
At the end of data input session, the Summary of Input
Data window appeared. From that window, the user could
verify input data. If there was any inconsistency, the user
could go back and modify it. After verifying input data, the
user could see design values (i.e. First Trial Batch
composition) by pressing on Final Design button. The
design values after moisture adjustment as proportioned by
the HPCMIX are shown in Fig. 11. This figure also shows
the key input parameters that were input by the user. From
this window, the user might request for explanation about
the mix design process, might go back to main screen or
Fig. 8. A typical window during data input process.
M.F. Mohd. Zain et al. / Advances in Engineering Software 36 (2005) 325–337332
might exit the mix design session by pressing on appropriate
buttons.
Table 2 shows the comparison of the trial batching mix
design selected by the developed system and the experts. It
can be observed from Table 2 that the results of the three
mix designs are very close except cement and fine aggregate
contents. The cement content suggested by the system is the
lowest (i.e. 464.38 kg/m3) and that suggested by Expert-1 is
the highest (i.e. 470.00 kg/m3). This was because the system
maintained high precision in computation by considering up
to ten digits after decimal point. But Expert-1 rounded off
the figures to integer values for convenience. For example,
total binder content was rounded off from 518.5 to
520 kg/m3 and silica fume content was rounded off from
Fig. 9. A typical User Request form during data input (after pressing Data
Input button of Fig. 8.
51.85 to 50 kg/m3 by Expert-1. On the other hand, cement
content suggested by Expert-2 was of intermediate value
(i.e. 466.67 kg/m3) because he considered only two digits
after decimal point in his calculation. Due to the variation in
cement content, the variation in fine aggregate content was
also observed among the three mix designs
(i.e. 777.51 kg/m3 by HPCMIX, 772.00 kg/m3 by Expert-1
and 775.17 kg/m3 by Expert-2) in order to maintain the total
displaced volume according to absolute volume method.
However, Expert-2 indicated that the differences among the
three mix designs were not critical. Expert-2 also indicated
that the concrete proportions selected by the system were
accurate enough for the first trial batching. It may be
relevant to mention here that, for a particular mix design,
there are always several answers, which can satisfy the
requirements of the specification [3]. Thus the concrete
proportions selected by the system were accurate enough for
Fig. 10. A typical explanation window during data input (after pressing
Comment button of Fig. 9.
Fig. 11. Window showing composition and key data of First Trial Batch submodule.
M.F. Mohd. Zain et al. / Advances in Engineering Software 36 (2005) 325–337 333
the first trial batching. The developed system is, therefore,
capable of proportioning the first trial batch of HPC mix
with acceptable accuracy.
5.2. Trial batch for laboratory
The calculated mixture proportions should be checked by
means of trial batches prepared and tested for the intended
performance requirements [15]. These tests and observation
on the designed mix are usually performed in the laboratory
by using a small portion of the designed mix, i.e. by
laboratory trial batch. For the purpose of validation of
Table 2
Comparison of results of First Trial Batch
Item HPCMIX Expert-1a Expert-2b
Water-Binder
Ratio
0.27 0.27 0.27
Water (l/m3) 123.38 124.00 123.40
Cement (kg/m3) 464.38 470.00 466.67
Silica Fume
(kg/m3)
51.60 50.00 51.85
Coarse Aggre-
gate (kg/m3)
1066.40 1066.00 1066.40
Fine Aggregate
(kg/m3)
777.51 772.00 775.17
Superplasticizer
(l/m3)
10.66 10.70 10.72
Air Content (%) 1.50 1.50 1.50
a Expert-1: Aitcin [15] (see reference).b Expert-2: Dr Hilmi Mahmud, University of Malaya, Malaysia.
the system, data used for an example laboratory testing is
described in Appendix B. The results computed by the
system and those of the experts are compared in Table 3. It
can be observed that the proportions selected by the
HPCMIX were close enough to those selected by
the experts. The highest variation was observed in
fine aggregate content, which was only 0.69 kg/m3
(i.e. 72.42–71.73Z0.69). According to the expert’s opinion,
the variations among the three mix designs were negligible.
Thus the present system can be used for the calculation of
laboratory trial mix of HPC with acceptable accuracy.
Table 3
Comparison of results of Laboratory Trial Batch
Item HPCMIX Expert-1a Expert-2b
Water-Binder
Ratio
0.27 0.27 0.27
Mixing Water
(litre)
11.49 11.53 11.42
Cement (kg) 43.26 43.70 43.18
Silica Fume
(kg)
4.81 4.70 4.80
Coarse Aggre-
gate (kg)
99.33 99.10 98.68
Fine Aggregate
(kg)
72.42 71.80 71.73
Superplasticizer
(litre)
0.99 0.99 0.99
Air Content (%) 1.50 1.50 1.50
a Expert-1: Aitcin [15] (see reference).b Expert-2: Dr Hilmi Mahmud, University of Malaya, Malaysia.
Table 5
Comparison of results of Batch Composition
Item HPCMIX Expert-1a Expert-2b
Water-Binder
Ratio
0.29 0.29 0.29
Mixing Water
(litre)
875.08 872.00 875.12
Cement (kg) 3600.00 3600.00 3600.00
Coarse Aggre-
gate (kg)
8400.00 8400.00 8400.00
Fine Aggregate
(kg)
6120.00 6120.00 6120.00
Superplasticizer
(litre)
64.00 64.00 64.00
a Expert-1: Aitcin [15] (see reference).b Expert-2: Dr Hilmi Mahmud, University of Malaya, Malaysia.
M.F. Mohd. Zain et al. / Advances in Engineering Software 36 (2005) 325–337334
5.3. One cubic metre SSD composition
This submodule can be used to compute composition of
one cubic metre of concrete for SSD conditions of
aggregates using the data of laboratory trial batch. It can
also be used for conversion of concrete composition from
field conditions of aggregates to SSD conditions of
aggregates. During computation, it takes into account
moisture conditions of aggregates in the bins as well as
water hidden in liquid superplasticizer. The data for a
sample example are presented in Appendix C. The results
are compared in Table 4 that shows good agreement
between the results of the HPCMIX and those of the
experts. The highest variation was only 0.88 kg/m3
(i.e. 1074.51–1073.63Z0.88) in coarse aggregate content.
The system gave proportions closer to those of Expert-2.
Expert-2 was consulted and he agreed that the variation was
negligible and the concrete proportions selected by the
system were accurate enough to make the trial batching.
These results serve as a means of verification of the
accuracy of knowledge base of the developed system.
5.4. Batch composition
This submodule computes batch composition for a
concrete construction project for field conditions of
aggregates. The system collects following data from the
user in order to compute batch composition: one cubic metre
SSD composition, moisture conditions of aggregates in the
field, and the expected batch size. It was assumed, for
example, that a concrete batch plant was to produce 8 m3 of
HPC on the basis of the SSD composition shown in
Appendix D. The user input those data by pressing
appropriate buttons of the Batch Composition interface
window. After the completion of data input, the user got
results of the batch composition for field conditions of
aggregates. The results are compared with those of the
experts in Table 5. Again, it can be seen that the results of
the HPCMIX compared well with those of the experts.
Table 4
Comparison of results of One Cubic Metre SSD Composition
Item HPCMIX Expert-1a Expert-2b
Water-Binder
Ratio
0.27 0.27 0.27
Water (l/m3) 144.61 144.88 144.57
Cement (kg/m3) 478.70 478.35 478.72
Fly Ash (kg/m3) 53.19 53.15 53.19
Coarse Aggre-
gate (kg/m3)
1074.51 1073.63 1074.57
Fine Aggregate
(kg/m3)
723.65 722.84 723.72
Superplasticizer
(l/m3)
9.57 9.57 9.57
Air Content (%) 1.50 1.50 1.50
a Expert-1: Aitcin [15] (see reference).b Expert-2: Dr Hilmi Mahmud, University of Malaya, Malaysia.
The only variation with the result of Expert-1 was observed
in mixing water requirement (i.e. 875.08–872.00Z3.08 l).
Expert-1 rounded off the water contributed by liquid
superplasticizer from 5.6 to 6.0 l in one cubic metre SSD
composition calculation. This excess water contributed by
superplasticizer (i.e. 6.0–5.6Z0.4 l) in one cubic metre
composition was multiplied by 8 for getting 8 m3 compo-
sition (i.e. 0.4!8Z3.2 l). On the other hand, this excess
water (i.e. 3.2 l) was subtracted from the required mixing
water and hence the value was less (i.e. 872 l instead of
875.2 l). Again Expert-2 was consulted and he agreed that
the performance of the HPCMIX in computing batch
composition was satisfactory. This comparison also gives
a positive indication of the accuracy of knowledge base of
the system.
5.5. Mix performance adjustment
The purpose of this module is to diagnose possible causes
of performance failure of a HPC mix and to recommend on
corresponding remedial measures in order to achieve the
desired performance. These features are demonstrated in
this portion of the case study. It was assumed, for example,
that the strength performance of the mix was not achieved in
the laboratory testing of the first trial batch. Careful
examination of the failure pattern of the test specimen
revealed that fracture surface passed through hydrated
cement paste. After getting these data from the user, the
inference engine of the HPCMIX forward chained through
its knowledge base and produced recommendation as stated
ahead (see Fig. 6): ‘Use lower water-binder ratio’. The
system also explained the reason for giving this recommen-
dation (see Fig. 6): ‘If the fracture surface passes almost
entirely through the hydrated cement paste around the
aggregates, a stronger concrete can be made with the same
aggregates by lowering further the water-binder ratio’. The
system also recommended, when the user pressed on
Quantitative Advice button, that increasing the 28-day
compressive strength of concrete by 1 MPa necessitate the
addition of approximately 8.66 kg/m3 of extra cement to
M.F. Mohd. Zain et al. / Advances in Engineering Software 36 (2005) 325–337 335
the mix. These recommendations and explanation exactly
matched with those of the experts [3,15] and these
recommendations were better than the recommendation
from any individual of these experts. Thus the performances
of the prototype to diagnose mix performance failure and to
give recommendation on corresponding performance
adjustment are considered to be satisfactory.
5.6. User-friendliness and the user interface
The HPCMIX user interface was designed for user
friendliness to enable its efficient utilization in the
following ways.
(i)
Due to the flexibility in determining flow of consul-tation, the user may need to be advised on the most
appropriate sequence of design steps. In order to
achieve this goal, the User Guide button in the General
Information module gives brief information of the
overall consultation steps. On the other hand, the first
Transcript Image in the beginning of each consultation
session provides information regarding the steps to
follow for that session (for example, see Fig. 5). In
addition, Advice buttons are available in most of the
modules (see Fig. 5, for example) to guide the user
about design procedure. Moreover, each consultation
window contains some buttons to guide the user for the
next design steps.
(ii)
The advantages and disadvantages of each alternativesolution are made easily available to the user so that he
can interact with the HPCMIX with better perspective.
For example, the Advice button in the Binder Types
window during the data input for binder types opens
text information on binders (similar to Silica Fume
window in Fig. 8). It explains the advantages and
disadvantages of using cement only and cement and
pozzolans as binder. The user will then be able to have
a better understanding of the alternative use of these
binders.
(iii)
Each User Request form of data input has a Commentbutton attached to it as described earlier (see Fig. 9).
This helps the user by expanding the meaning of a
question and thus aids the user in responding more
efficiently to the prompts of the design procedure.
(iv)
One of the distinguishing characteristics of an expertsystem such as the HPCMIX is the transparency of its
reasoning process and knowledge base. This advantage
is available to the user through Explain button (for
example, see Fig. 11), which displays the rules that
have been used by the inference mechanism, thus
explaining the reasons for arriving at a particular
recommendation. Another example of explanation
facility is shown in the second transcript image of the
Mix Performance Adjustment module (Fig. 6), which
explains the reasons for giving any recommendation
regarding mix performance adjustment.
(v)
As mentioned earlier, the General Information modulecontains a Knowledge Dictionary. Basic knowledge
about HPC, mix design and expert system is available
there. It also contains some statistical information. This
dictionary is very useful as an educational feature.
Moreover, concrete mix design flow diagram (see
Fig. 7) and several photographs are included in the
General Information module to enhance the edu-
cational performance of the HPCMIX.
5.7. Advantages, disadvantages and overall
evaluation of the system
It may be mentioned, by considering all these features,
that the consultation process of the HPCMIX is reason-
ably satisfactory and systematic. The flow of consultation
is flexible, allowing the user to reset data, to go back for a
new consultation, to review input values and other
procedures until he is satisfied with the results. The
ability of the HPCMIX to run using Windows operating
system, to give recommendation on possible causes of
performance failure of the mix, and the facility of
knowledge dictionary make this system superior to similar
other systems in the domain. The system-user interaction
is very interactive with explanatory facilities available
throughout the consultation session. Moreover, the system
gives information of data at every stages of data input,
which makes it superior to conventional programs of
concrete mix design. It has facilities like Explain, Advice
and Comment buttons as well, which make the system
very user-friendly.
The main disadvantage of the developed system is that it
is applicable for concrete compressive strength from 40 to
160 MPa for which Aitcin method is valid. Another
limitation of the system is that it is not platform
independent. The user will need a runtime version of
Kappa-PC in order to use the system. However, in future,
these limitations can be handled by incorporating another
state-of-the-art mix design method and by using another
state-of-the-art expert system shell if they are proved to be
useful.
In order for expert systems not to become obsolete, they
must be nurtured and kept current [35]. All expert systems,
the HPCMIX included, cannot claim completeness in their
knowledge bases; they are always subject to upgrading,
modification and correction. It should be recognized that
HPCMIX is a research prototype; and hence, it must be
refined and tested further for commercial use. The existing
knowledge base of the prototype can be improved by
refining, expanding, and reinforcing its knowledge base
using new findings as reported in literature or new
experience from domain experts. It must also be kept in
mind that, like other expert systems, HPCMIX will serve as
a decision support system; it will not replace completely
human expert’s decision making.
M.F. Mohd. Zain et al. / Advances in Engineering Software 36 (2005) 325–337336
6. Conclusions
This paper presented a prototype expert system called
the HPCMIX developed for mix design of HPC. The
HPCMIX is capable of selecting proportions of mixing
water, cement, supplementary cementitious materials,
superplasticizer and aggregates, considering the effects of
air content as well as water contributed by superplasticizer
and moisture conditions of aggregates. Using an expert
system shell, HPCMIX was developed according to the
mix design method proposed by Aitcin. Alternatively, in
future, any other state-of-the-art mix design method of
HPC can be added to the system as a new module without
affecting overall performance of the system. In addition to
proportioning concrete mixes, the system is also capable
of giving recommendations on mix performance adjust-
ment. It was found that it is feasible, efficient and
effective, to use an expert system approach for the
proportioning of HPC mix. The ability of the system to
give comments and advice about an input data, together
with the facility of knowledge dictionary, makes this
expert system very useful for the educational environment.
Additional knowledge to expand the scope of the system
can be added without major modification of the structure
of the program.
The best approach to making a mix design of HPC, of
course, is to use proportions previously established for
similar concrete using the same materials. In addition,
rules-of-thumb and past experience should also be used,
wherever possible. Where such prior information is limited
or unavailable, the HPCMIX can be used to assist the user in
the mix design of HPC.
Acknowledgements
The authors would like to express sincere gratitude to
Universiti Kebangsaan Malaysia for providing the fund for
the research and MBT (Malaysia) Sdn. Bhd for the supply of
materials and technical support throughout the research
program. The authors would like to thank Dr Hilmi
Mahmud (University of Malaya, Malaysia) for his
invaluable input and patience.
Appendix A. Data for first trial batch
It was supposed that a 100 MPa concrete had to be made
with the following data [15]: a Type I Portland cement; a
naphthalene-type superplasticizer with a total solids
content of 40% and specific gravity of 1.21; a dolomitic
limestone having maximum sizeZ10 mm, specific gravity
(SSD)Z2.80, absorptionZ0.8%, field moistureZ0%, and
the shape of the particles can be described as between
average and cubic; a siliceous natural sand of
specific gravity (SSD)Z2.65, absorptionZ1.2%, and field
moistureZ3.5%. Silica fume at 10% replacement (of total
cementitious material) was to be used; its specific gravity
was 2.20. The dosage of solids superplasticizer at the
saturation point was 1.0%.
Appendix B. Data for laboratory trial batch
In order to test First Trial Batch of HPC mix, the
following specimens are needed [15]: three 100!200 mm
cylinders for tests at 1, 7, 28 and 91 days in compression;
three 150!300 mm cylinders for tests at 28 days in
compression; three 150!300 mm cylinders for tests
for elastic modulus at 28 days; and three beams 100!100!400 mm for tests for modulus of rupture at 28 days. A
slump test, an air content test and a unit mass test will be
done on the fresh concrete. Except the air content test, the
concrete used for these tests will be recovered. Knowing
that an air test needs 15 kg, the amount of concrete to make
this trial batch needs to be calculated, assuming 10% extra
materials to compensate for losses.
Appendix C. Data for one cubic metre SSD composition
A trial batch with an adequate consistency and adequate
initial and final slumps was made using the following
quantities of materials [15]: waterZ12 l, cementZ45 kg,
Fly ashZ5 kg, coarse aggregateZ100 kg, fine aggregateZ70 kg, and superplasticizerZ0.9 l. The air content of this
trial batch was 1.5%. The materials used to make this trial
batch had the following properties: coarse aggregate-
specific gravity (SSD)Z2.75, absorptionZ1.0%, and field
moistureZ0%; and fine aggregate-specific gravity
(SSD)Z2.65, absorptionZ1.0%, and field moistureZ3.9%. The fly ash used had a specific gravity of 2.50. The
superplasticizer was naphthalene based one with a specific
gravity of 1.21 and a solid content of 42%. What is the
composition of 1 m3 of such concrete?
Appendix D. Data for batch composition
A concrete batch plant is to produce 8 m3 of HPC on the
basis of the following SSD composition [15]: w/cZ0.29,
waterZ130 l, cementZ450 kg, coarse aggregatesZ1050 kg, fine aggregatesZ750 kg, and superplasticizerZ8 l (liquid) and 4 kg (solid). The aggregates having the
following water contents are in the bins: coarse aggregate-
specific gravity (SSD)Z2.75, absorptionZ0.8%, and field
moistureZ0.8%; and fine aggregate-specific gravity
(SSD)Z2.65, absorptionZ1.0%, and field moistureZ3.0%. The superplasticizer is a naphthalene superplasticizer
containing 42% solids and having a specific gravity of 1.21.
What are the masses of materials that must be weighed to
make 8 m3 of concrete?
M.F. Mohd. Zain et al. / Advances in Engineering Software 36 (2005) 325–337 337
References
[1] Neville AM. Properties of concrete, 4th ed. Essex: Longman Group
Limited; 1995.
[2] Akhras G, Foo HC. A knowledge-based system for selecting
proportions for normal concrete. Ex Sys Appl 1994;7(2):323–35.
[3] Bai Y, Amirkhanian SN. Knowledge-based expert system for concrete
mix design. J Cons Eng Mngt, ASCE 1994;120(2):357–73.
[4] Islam MN, Al-Mattarneh HMA, Zain MFM, Basri HB. Towards an
expert system for HPC mix design. Proceedings of the world
conference on concrete materials and structures, Shah Alam,
Malaysia, May; 2002.
[5] Foo HC, Akhras G. Expert systems and design of concrete mixtures.
Conc Int 1993;15(7):42–6.
[6] Oh J, Lee I, Kim J, Lee G. Application of neural networks for
proportioning of concrete mixes. ACI Mat J 1999;96(1):61–7.
[7] Durkin J. Expert systems design and development. New Jersey:
Macmillan; 1994.
[8] Hickey JP, Aldridge AJ. An expert system for the prediction of
aquatic toxicity of contaminants. In: Hushon JM, editor. Expert
systems for environmental applications. Washington: American
Chemical Society; 1990.
[9] Smith LM. Interim report on COMIX: an expert system for concrete
mix design, Report No. M4.87/1, Central Laboratories, New Zealand;
1987.
[10] Malasri S, Maldonado S. Concrete mix designer. Comp Appl Conc
Tech, ACI 1988;SP113-3:33–41.
[11] Islam MN, Al-Mattarneh HMA, Zain MFM, Basri HB. Expert
systems and concrete mix design: a review. Proceedings of the sixth
international conference on concrete technology for developing
countries, Amman, Jordan, October; 2002.
[12] Malier Y, editor. High performance concrete: from material to
structure. London: E and FN Spon; 1992.
[13] Shah SP, Ahmad SH, editors. High performance concrete and
applications. London: Edward Arnold; 1994.
[14] Nawy EG. Fundamentals of high strength high performance concrete.
London: Longman Group Limited; 1996.
[15] Aitcin PC. High-performance concrete. London: E and FN Spon;
1998.
[16] Safiuddin M. Influence of different curing methods on the mechanical
properties and durability of HPC exposed to medium temperature.
MSc Thesis, Universiti Kebangsaan Malaysia, 1998.
[17] Day KW. Concrete mix design, quality control and specification,
second ed. London: E and FN SPON; 1999.
[18] ACI Committee 211. Standard practice for selecting proportions for
normal, heavyweight, and mass concrete (ACI 211.1-91). Detroit:
American Concrete Institute; 1991.
[19] ACI Committee 363. State-of-the-art report on high-strength concrete
(ACI 363R-92). Detroit: American Concrete Institute; 1992.
[20] ACI Committee 211. Guide for selecting proportions for high strength
concrete with Portland cement and fly ash (ACI 211.4R-93). Detroit:
American Concrete Institute; 1993.
[21] ACI Committee 201. Guide to durable concrete. ACI Manual of
Concrete Practice: Part 1 (ACI 201.2R-92). Detroit: American
Concrete Institute; 1995.
[22] de Larrard F. A method for proportioning high-strength concrete
mixtures. Cem Conc Agg 1990;12(2):47–52.
[23] Mehta PK, Aitcin PC. Principles underlying production of high-
performance concrete. Cem Conc Agg 1990;12(2):70–8.
[24] Rougeron P, Aitcin PC. Optimisation of the composition of high-
performance concrete. Cem Conc Agg 1994;16(2):115–24.
[25] Mehta PK, Aitcin PC. Microstructural basis of selection of
materials and mix proportions for high-strength concrete. Proceed-
ings of the one-day short course on concrete technology/HPC:
Properties and Durability, University Malaya, Kuala Lumpur,
May; 1997.
[26] Aitcin PC. Durable concrete: current practice and future trends.
Proceedings of the one-day short course on concrete technology/HPC:
Properties and Durability, University Malaya, Kuala Lumpur, May;
1997.
[27] Aitcin PC. The durability of high performance concrete. Proceedings
of the one-day short course on concrete technology/HPC: Properties
and Durability, University Malaya, Kuala Lumpur, May; 1997.
[28] Mahmud H. Cement replacement materials in concrete- for strength,
durability and economy. Proceedings of the one-day short course on
concrete technology/HPC: Properties and Durability, University
Malaya, Kuala Lumpur, May; 1997.
[29] Neville A, Aitcin PC. High-performance concrete—an overview.
Materials and Structures 1998;31:111–7.
[30] Islam MN, Zain MFM, Basri HB. Development of an expert system
for high performance concrete mix design. Proceedings of the
international conference on robotics, vision, information and signal
processing, Penang, Malaysia, January, 2003.
[31] IntelliCorp Inc. KAPPA-PC Ver. 2.4 User’s Manual, IntelliCorp Inc.,
CA, 1997.
[32] Basri HB. An expert system for preliminary landfill design in
developing countries. PhD Thesis, Department of Civil Engineering,
University of Leeds; 1994.
[33] Basri HB. An expert system for landfill leachate management. Env
Tech 2000;21:157–66.
[34] Basri NEA. An expert system for the design of composting facilities in
developing countries. PhD Thesis, Department of Civil Engineering,
University of Leeds, 1999.
[35] Kaetzel LJ, Clifton JR, Klieger P, Snyder K. Highway concrete
(HWYCON) expert system user reference and enhancement guide.
Gaithersburg: National Institute of Standards and Technology
(BFRL); 1993.
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