A MODEL BASED ON ARAS-G AND AHP METHODS FOR … filea model based on aras-g and ahp methods for multiple criteria prioritizing of heritage value zenonas turskis*, edmundas kazimieras

A MODEL BASED ON ARAS-G AND AHP METHODS

FOR MULTIPLE CRITERIA PRIORITIZING

OF HERITAGE VALUE

ZENONAS TURSKIS*, EDMUNDAS KAZIMIERAS ZAVADSKAS†

and VLADISLAVAS KUTUT‡

Department of Construction Technology and Management

Vilnius Gediminas Technical University, Saul _etekio al. 11, LT-10223

Vilnius, Lithuania*[email protected]

†[email protected]‡[email protected]

The paper discusses the meaning and nature of urban cultural heritage, and the available

methods for its valuation in the perspective of sustainable city development. From this per-

spective, decision-making problems of renovation often involve a complex decision-makingprocess in which multiple requirements and conditions have to be taken into consideration

simultaneously. In project development it is hardly possible to get exhaustive and accurate

information. As a result, the situations occur, the consequences of which can be very damaging

to the project. Sometimes the loss is related to symbolic values that the public perceive asdisregarded by the project, despite the overall improved conditions. This paper presents the

multiple criteria assessment of alternatives of the cultural heritage renovation projects in

Vilnius city. The model consists of the following elements: determining attributes set a®ectingbuilt and human environment renovation; information collection and analysis, decision mod-

eling and solution selection. The main purpose of the model is to improve the condition of the

built and human environment through e±cient decision making in renovation supported by

multiple attribute evaluation. Delphi, AHP and ARAS-G methods, considering di®erentenvironment factors as well as stakeholders' needs, are applied to solve problem.

Keywords: Heritage; assessment; multiple criteria; decision making; MCDM; ARAS-G; AHP.

MSC 1991: 90B50, 90B90, 91B06, 91B10

1. Introduction

Urban cultural heritage is the physical representation of a community identity. The

term `built heritage' refers to monuments, groups of edi¯ces and sites of historical,

aesthetic, archaeological, scienti¯c, ethnological or anthropological value. Historical

building preservation is becoming increasingly important world-wide due to the

emphasis on cultural heritage and its potential bene¯ts.1 One can speak of it in terms

International Journal of Information Technology & Decision Making

Vol. 12, No. 1 (2013) 45�73

°c World Scienti¯c Publishing CompanyDOI: 10.1142/S021962201350003X

45

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http://dx.doi.org/10.1142/S021962201350003X

of cultural heritage as opposed to natural heritage, but both aspects can also be

thought of together as constituent elements of a national heritage and are historically

strongly connected to one another.2 Cultural heritage should be protected and

transmitted to the future generations as it has been transmitted from past generation

to the present. Market forces or other unconscious and undirected phenomena can-

not solve the serious problems of sustainability. The Operational Guidelines for the

Implementation of the World Heritage Convention3 suggest that historic buildings

are regarded as world cultural heritage properties since they are of outstanding

universal value from the point of view of history, art or science. It is also suggested

that world heritage properties may support a variety of ongoing and proposed uses

that are ecologically and culturally sustainable. Nowadays, cultural heritage is under

threat and danger (pollution, natural disasters, wars, etc.). At the last decade the

increasing urbanization of the world coupled with global issues of climate change,

water shortage, air pollution, environmental degradation, economic restructuring

and social exclusion. It does serious damage to historical buildings and cultural

human-made artifacts, and the costs of cleaning and repair are enormous. Although

buildings are long lasting they require continual maintenance and restoration. In this

domain, cultural heritage management as the art, vocation and practice of managing

cultural heritage resources and as a multi-discipline research area has a vital role.4 It

demanded to take a deeper look at the future to cities in Europe. We live in an

increasingly urban world, where more than half of humanity lives in an urban area.5

Convention Concerning the Protection of the World Cultural and Natural Heritage

points out that the world heritage possesses various kinds of use value like scienti¯c

value, aesthetic value and recreational value, as well as multiple nonuse value like

bequest value and existence value.6 Urban development implies the creation of new

assets in terms of physical, social and economic structures. The Council of Europe

has opened a new treaty for rati¯cation on the topic of cultural heritage called

Council of Europe Framework Convention on the Value of Cultural Heritage for

Society.7 In 1972, the UNESCO General Conference adopted the Convention Con-

cerning the Protection of the World's Cultural and Natural Heritage, otherwise

known as the World Heritage Convention. The rationale of the convention was that

there are places of `outstanding universal value', that these are part of the heritage of

all humankind and that their protection is therefore a shared responsibility.8 Today's

restoration and preservation of cultural heritage is an important task because of its

historical signi¯cance, symbolism, and economic bene¯ts.9

There are a number of potential heritage bene¯ts and reasons for heritage pres-

ervation10:

. It raises social value (Heritage can tell the people how the past looked like as it

record some signi¯cant historical events. People would be proud of their unique

social roots by appreciating the evidence of human activity).

. It reduces or removes risks to a heritage asset.

46 Z. Turskis, E. K. Zavadskas & V. Kutut

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. It raises economic value (By successful revitalizing and converting them to tour-

ists' spots such as museum, their value can raise substantially because it can

attract visitors and boosts tourism industry, which is an important economic

pulse. It secures the optimum viable use of a heritage asset in support of its long

term conservation.

. It makes a positive contribution to economic vitality and sustainable communities.

. It makes higher historical and cultural value (Heritage is an evidence displaying

history, re°ecting people behaviors and culture in the past);

. It better reveals the signi¯cance of a heritage asset and therefore enhances our

enjoyment of it and the sense of place.

. The process of sustainable urban management requires a range of tools addressing

environmental, social and economic concerns in order to provide the necessary

basis for integration.

Wang and Zeng11 stated that e®ective and proper evaluation for reuse selection

may accelerate the implementation of sustainable conservation and proposed model

of a multi-objective decision-making process for reuse selection of historic buildings.

The decision makers often simultaneously manage one or several alternatives/

projects with con°icting and noncommensurable criteria.12 It can be noted that

management of cultural heritage objects is multi-stage multiple criteria group

decision-making process.13

Despite growing attention by researchers and policy makers on the economic

value of cultural heritage sites, debate surrounds the use of adequate methods.14 The

economic value of cultural heritage can be de¯ned as the amount of welfare that

heritage generates for society. Many historical, socio-economic, geographical, and

political factors come into play in determining the level and rate of development of

any given country.2

Over the last years there is a growing demand for cultural destinations or `cultural

tourism'. Cultural heritage provides a variety of socioeconomic functions,15 e.g.,

opportunities for education and training, economic bene¯ts through tourism devel-

opment, inspiration for scienti¯c research, etc. Authenticity is acknowledged as a

universal value and an essential driving force that motivates tourists to travel to

distant places and times.16 From the tourist-as-a-consumer standpoint, a discussion

of the similarities between authenticity and satisfaction in the context of cultural

tourism also seems relevant.

However, very few tools are available to determine appropriately restoration

priorities for the diverse historical heritages, perhaps because of a lack of system-

atized decision-making aids.17

Heritage building conservation is multidimensional and extends beyond building

retro¯t and renovation; however such retro¯tting is one of the most important fea-

tures of conservation necessary to increase building energy e±ciency and reduce

greenhouse gas emissions.18

A Model Based on ARAS-G and AHP Methods 47

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Nijkamp and Riganti19 presented overview of valuation methods for cultural

goods. The main two of them are:

(1) Multiple criteria analysis is a class of multidimensional evaluation methods

that is rather rich in scope, as it is able to encapsulate both priced and nonpriced

e®ects, as well as both quantitative and qualitative e®ects of an object under

investigation;

(2) Social cost-bene¯t analysis aims to assess the costs and bene¯ts of a proposed

public project for society at large. In the early literature, the Pareto-optimality

concept played a prominent role.

Despite the rapid development, the ¯eld of data mining and knowledge dis-

covery20 is still vaguely de¯ned and lack of integrated descriptions. This situation

causes di±culties in teaching, learning, research, and application. Mo®ett and Sar-

kar21 provided a review of multiple criteria decision-making methods that may

potentially be used during systematic conservation planning for the design of con-

servation area networks. They reviewed 26 methods and presented the core ideas of

19 of them. Zavadskas and Turskis22 provided extended review about multiple cri-

teria decision-making methods in economics. The research topics of the lot papers

published in the last decade can be classi¯ed into three major directions: decision

support, multiple criteria decision making, and data mining and risk analysis.23

Ranking algorithms normally need to examine several criteria.24 Peng et al.25

selected four MCDM methods, including TOPSIS, VIKOR, PROMETHEE and

WSM, to solve ranking problem. Only few of them are applied to assess and rank

heritage objects.

Iyer-Raniga and Wong18 presented an integrated life cycle framework developed

by combining life cycle modeling with building energy e±ciency simulation software.

Decision making in environmental problems of human made cultural heritage can be

complex due to varying measurements, di®erences in input parameters, lack of

comparable exact data, and components that may involve subjective and qualitative

factors.26 Thus, making a decision on restoration priorities may be greatly depended

on the administrators' intuitive and subjective judgments, and this nonstandard

procedure results in causes, a complexity in assessing appropriate restoration

needs.17 The economic dimension is seen as the most important prerequisite for the

ful¯llment of human needs and for any lasting improvements to the living conditions

of citizens.

Kaya and Kahraman26 proposed an environmental impact assessment method-

ology based on an integrated fuzzy AHP�ELECTRE approach in the context of

urban industrial planning. Taking social, political, economic, and ecological factors

into account simultaneously, the methodology provides a basis for better decision

making by identifying, predicting, evaluating the environmental e®ects of develop-

ment schemes. Most, if not all, environmental investment decisions depend on

multiple attributes and are subject to one or more constraints.


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Based on scienti¯c study Han et al.27 provided evidence that the majority of

respondents are willing to pay for environment conservation. Marinoni et al.28

described a decision support software system referred to as the multiple criteria

analysis tool. The system identi¯es a portfolio of decision options that return a

maximum aggregated bene¯t under a constrained budget. Increasingly, qualitative

aspects of progress are seen as being just as important as material improvements.

By 1970s many countries were experiencing the combined e®ects of recession,

economic restructuring and social reactions against the modernist planning agenda.29

Cultural policy today requires a better understanding of the complex interrelation

between the economy and culture.30 Despite the considerable economic bene¯ts in

conserving built heritage, often exceeding costs of their conservation, urban planners,

particularly in developing countries, have ignored the issue of built heritage.31

Investigation and discussion of problems associated with the old town renovation

have intensi¯ed since the 1990s. Current debates about urban sustainability tend to

focus on technical issues, such as carbon emissions, energy consumption and waste

management, or on the economic aspects of urban regeneration and growth.32 The

heated debates on sustainable urban development in the world are going on now, and

a compact city appears to be one of the best options for sustainable devel-

opment.33�35 In order to design and implement renovation of the built environment

based on sustainable development principles it is necessary to follow these

principles36�38 from idea till implementation. Suitable decisions must be made

starting from the brief stage.39�43

E®ective and proper evaluation for reuse selection may accelerate the

implementation of sustainable conservation.11 From this perspective, decision-

making problems of renovation often involve a complex decision-making process in

which multiple requirements and conditions have to be taken into consideration

simultaneously.37 Integrated analysis and rational decision making at the micro-,

meso- and macro-levels are needed to mitigate the e®ects of the construction and real

estate sector.44

2. The Prioritization Model

The process of renovation of the built and human environment can be divided into

four main phases.

(1) Data collection and analysis. At the initial phase renovation purposes, tasks,

results, main participants, their aims and their relations are determined, type of

building de¯ned, analysis of renovation necessity performed.

(2) Decision modeling phase. After aims and the need for renovation are de¯ned, the

next and very important phase is decision modeling. Information is analyzed,

models formed, evaluation criteria selected and alternatives are distinguished in

this phase. Decision making means the selection of the best alternative from

numerous alternatives. Analysis of the built environment renovation and


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decision making is sophisticated because of many possible alternatives appearing

in aims establishment, projecting, construction, and usage stages. These

alternatives sometimes not even interact. In order to create optimal renovation

strategy all the stakeholders' needs must be considered. Accordingly, renovation

alternatives must be analyzed based on many criteria.45 In this phase also the

information about already implemented renovation projects, best practice

examples, strengths and weaknesses of the projects is needed.

(3) Decision selection phase. The main aim of this phase is to select the best

alternative, evaluate expected results and make the ¯nal decision. In order to

choose the best decision (alternative) methods of multiple criteria analysis can be

applied. It is very important to choose the most suitable method in this case and

to select the alternative which satis¯es the stakeholders' needs at the highest

degree.

(4) Implementation phase. Implementation phase is the last phase of decision-

making process. The decision is transferred to implementers and whether

examined the best alternative selection is made. The project performance should

be evaluated during the development process as well as after ¯nishing in order to

assess the existing situation when compared with the planned. The presented

model comes to the conclusion that multiple criteria decision making (MCDM)

approach is to be the most advantageous for decision making in the ¯eld of the

built and human environment renovation.

The main steps of problem solution are as follows (Fig. 1):

Determine aim and scope! generate criteria set! generate set of alternatives!determine criteria weights ! determine criteria scores ! selection of aggregation

model ! evaluation, priority setting and improving decisions ! implementing

selection.

Subjective judgments of multiple evaluators are utilized to assess the relative

importance of each criterion in constructing a pairwise comparison matrix.46 The

AHP method was selected and applied. The analytic hierarchy process (AHP)47�55 is

a theory proposed by Saaty of measurement through pairwise comparisons and

relies56�58 on the judgments of experts to derive priority scales, and it has been

applied in a variety of engineering and science categories.17,24 It is these scales that

measure intangibles in relative terms. The comparisons are made using a scale of

absolute judgments that represents how much one element dominates another with

respect to a given attribute. The judgments may be inconsistent, and how to measure

inconsistency and improve the judgment, when possible to obtain better consistency

is a concern of the AHP.59

2.1. Gray number

Deng60 developed the Gray system theory. According to him, the Gray relational

analysis has some advantages: it involves simple calculations and requires a smaller

number of samples; a typical distribution of samples is not needed; the quanti¯ed


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outcomes from the Gray relational grade do not result in contradictory conclusions

from the qualitative analysis; the Gray relational grade model is a transfer functional

model that is e®ective in dealing with discrete data.61

Gray theory is an e®ective mathematical means to:

. Deal with problems described by incomplete information

. To avoid the inherent defects of conventional, statistical methods,

Fig. 1. The multiple criteria expert system for cultural heritage assessment.


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http://www.worldscientific.com/action/showImage?doi=10.1142/S021962201350003X&iName=master.img-002.jpg&w=163&h=429

. And advantage is to use a limited amount of data to estimate the behavior of an

uncertain system when the data are discrete and the information is incomplete.62

Due to the presence of incomplete information and uncertain relations it is very

di±cult to use ordinary methods.

White number, gray number and black number are three classi¯cations to dis-

tinguish the uncertainty level of information. Let �x ¼ ½�; �� ¼ fxj� � x � �; � and

x 2 Rg. Then, �x which has two real numbers � (the lower limit of �x) and � (the

upper limit of �x) de¯ned as follows:

. If � ! �1 and � ! 1, then �x is called the black number which means without

any meaningful information.

. Else if � ¼ �, then �x is called the white number which means with complete

information.

. Otherwise, �x ¼ ½�; �� is called the gray number which means insu±cient and

uncertain information.

Nevertheless, the obtained information from real world is always uncertain or

incomplete. Hence, extending the applications from white numbers (crisp values) to

gray numbers is necessary for real-world applications. The basic de¯nitions and

operations of gray number are described as follows.

Let a gray number is de¯ned to be gray number de¯ned by two parameters ð�; �Þ.Let þ;�;� and � denote the operations of addition, substraction, multiplication

and division, respectively. The basic operations of gray numbers �n1 and �n2 are

de¯ned as follows:

�n1 þ�n2 ¼ ðn1� þ n2�; n1� þ n2�Þ addition; ð1Þ�n1 ��n2 ¼ ðn1� � n2�; n1� � n2�Þ substraction; ð2Þ�n1 ��n2 ¼ ðn1� � n2�; n1� � n2�Þ multiplication; ð3Þ

�n1 ��n2 ¼n1�n2�

;n1�n2�

� �division; ð4Þ

k � ð�n1Þ ¼ kn1�; kn1��

Number product of gray numbers

if k is positive real number; ð5Þ

ð�n1Þ�1 ¼ 1

n1�;1

n1�

� �: ð6Þ

2.2. Gray multi attribute decision-making model: An additive ratio

assessment method with gray values (ARAS-G)63

ARAS method64 is based on the argument that phenomena of complicated world

could be understood by using simple relative comparisons. It is argued that the ratio


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of the sum of normalized and weighted values of criteria, which describe alternative

under consideration, to the sum of the values of normalized and weighted criteria,

which describes the optimal alternative, is degree of optimality, which is reached by

the alternative under comparison.

According to the ARAS method,37,65�67 a utility function value determining

the complex relative e±ciency of a reasonable alternative is directly proportional

to the relative e®ect of values and weights of the main criteria considered in a

project.

The ¯rst stage is gray decision-making matrix (GDMM) formation. In the

GMCDM of the discrete optimization problem any problem to be solved is rep-

resented by the following DMM of preferences for m reasonable alternatives (rows)

rated on n criteria (columns):

~X ¼

�x01 � � � �x0j � � � �x0n

..

. . .. ..

. . .. ..

.

�xi1 � � � �xij � � � �xin

..

. . .. ..

. . .. ..

.

�xm1 � � � �xmj � � � �xmn

26666666664

37777777775; ð7Þ

i ¼ 0; m ; j ¼ 1; n ;

where m �� number of alternatives, n �� number of criteria describing each

alternative, �xij �� gray value representing the performance value of the i

alternative in terms of the j criterion, �x0j �� optimal value of j criterion.

If optimal value of j criterion is unknown, then

�x0j ¼ maxi

� xij ; if maxi

� xij is preferable; and

�x0j ¼ mini

� x �ij ; if min�i x

�ij is preferable:

ð8Þ

Usually, the performance values �xij and the criteria weights �wj are viewed as

the entries of a DMM. The system of criteria as well as the values and initial weights

of criteria are determined by experts.68 The information can be corrected by the

interested parties by taking into account their goals and opportunities.

Then the determination of the priorities of alternatives is carried out in several

stages.

Usually, the criteria have di®erent dimensions. The purpose of the next stage is to

receive dimensionless weighted values from the comparative criteria. In order to

avoid the di±culties caused by di®erent dimensions of the criteria, the ratio to the

optimal value is used. There are various theories describing the ratio to the optimal

value. However, the values are mapped either on the interval ½0; 1� or the interval

½0;1Þ by applying the normalization of a DMM.


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In the second stage, the initial values of all the criteria are normalized �� de¯ning

values ��x ij of normalized decision-making matrix (DMM) � �X :

� �X ¼

��x 01 � � � ��x 0j � � � ��x 0n

..

. . .. ..

. . .. ..

.

��x i1 � � � ��x ij � � � ��x in

..

. . .. ..

. . .. ..

.

��xm1 � � � ��xmj � � � ��xmn

26666666664

37777777775; ð9Þ

i ¼ 0;m ; j ¼ 1;n :

The criteria, whose preferable values are maxima, are normalized as follows:

��x ij ¼xijPmi¼0 �xij

: ð10Þ

The criteria, whose preferable values are minima, are normalized by applying two-

stage procedure:

�xij ¼1

�x �ij

; ��x ij ¼�xijPmi¼0 �xij

: ð11Þ

When the dimensionless values of the criteria are known, all the criteria, originally

having di®erent dimensions, can be compared.

The third stage is de¯ning normalized-weighted matrix �� X . It is possible to

evaluate the criteria with weights 0 < �wj < 1. Only well-founded weights should be

used because weights are always subjective and in°uence the solution. The values of

weight �wj are usually determined by the expert evaluation method. The sum of

weights wj would be limited as follows:

Xnj¼1

wj ¼ 1 ð12Þ

�X ¼

�x01 � � � �x0j � � � �x0n

..

. . .. ..

. . .. ..

.

�xi1 � � � �xij � � � �x in

..

. . .. ..

. . .. ..

.

�xm1 � � � �xmj � � � �xmn

26666666664

37777777775; ð13Þ

i ¼ 0;m ; j ¼ 1;n :

Normalized-weighted values of all the criteria are calculated as follows:

�x ij ¼ ��x ij ��wj ; i ¼ 0;m ; ð14Þ


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where wj is the weight (importance) of the j criterion and �x ij is the normalized rating

of the j criterion.

The following task is to determine the values of optimality function:

�Si ¼Xnj¼1

�x ij ; i ¼ 0;m ; ð15Þ

where �Si is the value of optimality function of i alternative.

The biggest value is the best, and the least one is the worst. Taking into account

the calculation process, the optimality function �Si has a direct and proportional

relationship with the values �xij and weights �wj of the investigated criteria and

their relative in°uence on the ¯nal result. Therefore, the greater the value of the

optimality function �Si, the more e®ective the alternative. The priorities of

alternatives can be determined according to the value �Si. Consequently, it is

convenient to evaluate and rank decision alternatives when this method is used.

The result of gray decision making for each alternative is gray number �Si. There

are several methods for transforming gray values to crisp values. The center-of-area

is the most practically and simple to apply:

Si ¼1

2ðSi� þ Si�Þ: ð16Þ

The degree of the alternative utility is determined by a comparison of the variant,

which is analyzed, with the ideally best one S0. The equation used for the calculation

of the utility degree Ki of an alternative Ai is given below:

Ki ¼SiS0

; i ¼ 0;m ; ð17Þ

where Si and S0 are the optimality criterion values, obtained from Eq. (16).

It is clear, that the calculated values Ki are in the interval [0; 1] and can be

ordered in an increasing sequence, which is the wanted order of precedence. The

complex relative e±ciency of the reasonable alternative can be determined according

to the utility function values.

3. Case Study

A decision support model for prioritizing restoration needs of cultural heritages is

proposed in this case study. The problem under consideration, its ¯ndings, and made

conclusions, which are made based on the solution results, are of crucial importance

for city's institutions (special plans of heritage sites for the design, coordination and

approval authorities), which are responsible for every actual problem.

Vilnius is an outstanding example of a medieval foundation which exercised a

profound in°uence on architectural and cultural developments in a wide area of

Eastern Europe over several centuries (http://whc.unesco.org/en/list/541). In the

townscape and the rich diversity of buildings that it preserves, Vilnius is an


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exceptional illustration of a central European town that evolved organically over ¯ve

centuries. Despite invasions and partial destruction, it has preserved an impressive

complex of Gothic, Renaissance, Baroque and classical buildings as well as its

medieval layout and natural setting. Gothic style, Baroque, Classicism, and his-

toricism prevail; the fusion of several styles into a harmonious whole is a distinctive

feature of Vilnius. A very interesting fact is that churches in the Old Town are of

various religions. Scattered really close to each other churches of Catholicism, Pro-

testantism, Orthodoxy and Judaism do not surprise local inhabitants.69 Nearly 3300

cultural heritage properties in Vilnius City are inscribed on the Cultural Property

Register, including 16 urban territories which are abundant in valuable architecture

pertaining to various periods and styles. The Vilnius Old Town is included into

UNESCO World heritage list. The most signi¯cant cultural monument is the Old

Town of Vilnius that was inscribed on the UNESCOWorld Heritage List in 1994 (list

No. 541). The Vilnius Old Town includes many churches, museums, living houses

and architectural monuments.

Old town includes various neighborhoods, individual houses and their groups. In

addition, it has a complicated system of engineering structures and service lines often

going through the remaining foundations of old buildings and communication lines.

All of them have been formed under various conditions at various periods of time.

To the solution process were involved 16 executors, consisting of most govern-

ment o±cials and employers from the state enterprise of heritage design works and

care of buildings \Monuments of Lithuania". The Municipality, with the support of a

group of experts from Vilnius Gediminas Technical University, has selected a

number of di®erent buildings of the Vilnius. Seven high-skilled designing and con-

servation professionals were involved to the discussion and selected eight of feasible

alternative buildings Aiði ¼ 1; 8Þ, which are compared against eight criteria

(Table 6). The most signi¯cant criteria are identi¯ed through three Delphi rounds

and an alternative process is provided for carrying out an assessment of restoration

urgency of cultural heritage. The most important roles for cultural heritage preser-

vation and conservation organizations are historical and architectural categories (see

Table 7). These categories prevails destination of the buildings. The selected

buildings (Table 1) should be analyzed, assessed, and renovated or retro¯tted.

Table 1. Feasible buildings alternatives under consideration.

Alt. No. Building Construction period

A1 Church of St. John the Baptist and St. John Evangelist the Apostle 1387�1426 years

A2 Tilto St. 7 1931�1933 years

A3 Jogailos St. 3 End of XIX centuryA4 �ZaliujuE�zeru St. 47 End of XVIII century

A5 Pylimo St. 22 1830�1840 years

A6 Klaip_edos St. 7 1730�1780 yearsA7 Barboros Radvilait _es St. 6

A8 Stuokos � Gucevičiaus St. 9 End of XIX century


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The linguistic ratings of buildings and gray scorings of building heritage values

are presented in Table 2.

Description of the selected objects is presented in Table 4. Scoring of the buildings

construction criteria are made by applying scale, which is presented in Table 3.

Criteria weights (Table 7) were determined by applying AHP method. Each of

seven experts (p1; . . . ; p7) prepared pairwise comparison matrix of criteria import-

ance (Table 6). AHP method employs the scale48,49,54 (Table 5). In Table 6 are

presented results of criteria weights evaluation according to the ¯rst expert. The

presumptive assessment of criteria importance results is presented in Table 7.

3.1. Determining of the heritages buildings' integrated index value

To integrate values of multiple criteria and for determining heritage valuability class

of building there is applied ARAS-G method. For solution is prepared initial DMM

(presented in Table 8). There are applied group weights, which are determined by

applying AHP method. The solution process is described in Tables 8�11. The sol-

ution results are shown in Table 11.

Integrated multiple criteria value of heritage building is determined in comparison

of calculated integrated building`s heritage value with the value, which is presented

in Table 10.

Table 2. The linguistic variables or

the ratings of buildings heritage value.

Gray numbers

Linguistic variables � �

Very Low (VL) 0.00 0.20Low (L) 0.10 0.30

Medium Low (ML) 0.20 0.40

Medium (M) 0.35 0.65

Medium High (MH) 0.60 0.80High (H) 0.70 0.90

Very High (VH) 0.80 1.00

Table 3. The scale for criteria evaluation

(based on Saaty's scale).

Importance De¯nition

1 Very small importance

3 Moderate importance5 Essential or strong importance

7 Very strong importance

9 Extreme importance

2, 4, 6, 8 Intermediate values


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Tab

le4.

Thedescription

ofthestructuresstateof

theselected

objectsan

dassessmentof

values

accordingto

thescale.

Alt.

Design

Con

tent

Score

Notes

Category

Score

inpoints

A1

Fou

ndation

Stoneconcrete

6Detectedleak

ycrackswider

than

5mm.They

aremoisture

dam

aged

andmolddueto

poor

waterproo¯

ng

Historical,Cultural,an

dMem

orial

8

Rem

ainsof

di®erenteras

of

construction

9

Wall

Brick

mason

ry7

Cracksin

wallswider

than

5mm

Stylistic

epoch'sremainsinside

8

Floors

Claybrick

°oo

r�

vau

lted

arches

4Visible

cracksin

arches

Stylistic

epoch'sremainsof

the

buildingfacades

7

Architectural�

composite

valueof

facades

8

Roo

f:Bearingstruc-

ture/C

overing

Wooden/C

laytiling

5De°ection

ofwooden

construction

ofroof

isgreaterthan

1/100of

thelength,25

%of

them

are

rotten.Leakingroof

coating.

Buildingfunctionalityin

old

townarea

9

Building'sconstructiontech-

nologyan

dquality

7

Valuab

les

Stained

glasses

8Fallingdow

nseparateparts

Con

structionperiodsof

build-

ing'sevolution

A2

Fou

ndation

Reinforced

concrete

4Goodquality


dMem

-orial

4

Rem

ainsof

di®erenteras

of

construction

3

Wall

Yellow

rowlock

mason

ry4

Allinner

sideop

enings

were

dam

aged

byreplacingwoo

den

window

swithplastic

ones

Stylistic


5

Floors

Wooden

onwoo

den

beams

4Thestudieshav

enot

beendon

eStylistic

epoch'sremainsof

the

buildingfacades

4

Architectural�

composite

valueof

facades

5


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Tab

le4.

(Continued

)

Alt.

Design

Con

tent

Score

Notes

Category

Score

inpoints

Roo

f:Bearingstruc-

ture/C

overing

Wooden/T

in5

Firedam

aged

roof

oftheeastern

halfof

thebuilding.

Roofcon-

structionsnearbythedam

aged

cornices

andoldan

tennas

stan

dsaresleepy.Partof

the

constructionsareshiftedor

deformed

dueto

thesnow

load

s

ontheelem

ents.Tin

ondeckis

relayed

duringtherepairs.


old

townarea

5


nologyan

dquality

6

Valuab

les

Polygo

nal

reinforced

concrete

balconieswithdecorative

metal

works.

5Thetopsurfaceof

balconiesexter-

nal

layer

nearbyrailingis

crumbled,thelower

layer

in

man

yplacesiscracked

tometal

kits.Metal

kitsaredam

aged

by

corrosion.

Con

structionperiodsof

build-

ing'sevolution

A3

Fou

ndation

Claybrick

mason

rywith

bou

lders

7Theexistingbasem

entisin

good

condition.Itisrecommended

to

deepen

them

.


dMem

-

orial

7

Rem

ainsof

di®erenteras

ofconstruction

8

Wall

Claybrick

mason

ry7

Wallsof

façadearesplitted-o®

from

crosswallsan

darecon-

siderab

lydeformed.Thereisa

big

number

ofcracksin

facades.

Stylistic


6

Floors

1.W

ooden;

2.Mon

olithic

concrete;

3.Claybrick

vau

lted

2W

ooden

ceilingisbentan

dwalking

feel

vibration

sStylistic

epoch'sremainsof

the

buildingfacades

8

Architectural�

composite

valueof

facades

9


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Tab

le4.

(Continued

)

Alt.

Design

Con

tent

Score

Notes

Category

Score

inpoints

Roo

f:Bearingstruc-

ture/C

overing

Wooden/T

in4

Woo

den

bearingroof

constructions

arepartlyrotten

andtincover

layer

isconsiderab

lydam

aged

bycorrosion.


old

townarea

8


nologyan

dquality

4

Valuab

les

Plastic

decorsof

façades

5Oneof

themostexpressivearchi-

tecturalof

thebuildingitem

s.

Con

structionperiodsof

build-

ing'sevolution

A4

Fou

ndation

Claybrick

mason

ryon

bou

lders

mason

ryfoot

5Historical,Cultural,an

dMem

-

orial

7

Rem

ainsof

di®erenteras

ofconstruction

4

Wall

Claybrick

mason

ry5

Itisseekingto

return

theoriginal

layou

t

Stylistic


5

Floors

1.Mon

olithic

concrete;

2.W

ooden

andlathed

3Replacedfrom

1962

to1964

years

Stylistic

epoch'sremainsof

the

buildingfacades

5

Architectural�

composite

valueof

facades

6

Roo

f:Bearingstruc-

ture/C

overing

Wooden

truss/T

in4


old

townarea

6


nologyan

dquality

7

Valuab

les

Thegreatportico

ofIonicorder

columns

5Con

structionperiodsof

build-

ing'sevolution

A5

Fou

ndation

Bou

lderswiththesm

allpartof

clay

brick

mason

ry

8Thebasem

entiswithou

twater-

proo¯

ng.

Thereareob

vious

cracks.A

mortarischipped.


dMem

-

orial

4

Rem

ainsof

di®erenteras

of

construction

9


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Tab

le4.

(Continued

)

Alt.

Design

Con

tent

Score

Notes

Category

Score

inpoints

Wall

Ceram

icbrick

mason

ry4

Alotof

obviouscracksin

walls

exceedingthepermissible.

Stylistic


5

Floors

Wooden

onto

theglued

timber

beams

3Beamsareconsiderab

lydecom

-

posed.In

°oors

therearealotof

tearsan

dwalkingfeel

vibration

s.

Stylistic

epoch'sremainsof

the

buildingfacades

3

Architectural�

composite

valueof

facades

3

Roo

f:Bearingstruc-

ture/C

overing

Wooden/C

laytiling

3Tiled

roof

constructionisrotten

andwithleak

ingmem

brane.


old

townarea

5


nologyan

dquality

3

Valuab

les

Buildingisdelivered

tothe

Medieval

CityW

all

9Con

structionperiodsof

build-

ing'sevolution

A6

Fou

ndation

Bou

ldersan

dbrick

mason

ry4


dMem

-orial

4

Rem

ainsof

di®erenteras

of

construction

4

Wall

Brick

mason

ry4

Obviouscracksfrom

1upto

12mm

widein

walls.Lon

gitudinal

wallsbentou

twardupto

70mm.

Stylistic


5

Floors

Wooden

onwoo

den

beams

3Partially

arerotten.

Stylistic

epoch'sremainsof

the

buildingfacades

5

Architectural�

composite

valueof

facades

5

Roo

f:Bearingstruc-

ture/C

overing

Wooden/C

laytiling

4Roofwooden

constructionrotten

andleak

ingsurfacelayer.


old

townarea

5


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Tab

le4.

(Continued

)

Alt.

Design

Con

tent

Score

Notes

Category

Score

inpoints


nologyan

dquality

4

Valuab

les

��Con

structionperiodsof

build-

ing'sevolution

A7

Fou

ndation

Bou

ldersan

dceramic

brick

mason

ry

5Thebasem

entiswithou

twater-

proo¯

ng.


d

Mem

orial

5

Rem

ainsof

di®erenteras

ofconstruction

5

Wall

Brick

mason

ry5

Walls(especiallynearbywindow

s)

hav

ealotof

cracksfrom

1to

5mm.Theirdeviation

from

the

verticalisupto

20mm.

Stylistic


5

Floors

Wooden

beams

3Mostof

beamsarerotted

or

semi-rotted

Stylistic

epoch'sremainsof

the

buildingfacades

6

Architectural�

composite

valueof

facades

6

Roo

f:Bearingstruc-

ture/C

overing

Wooden/C

laytiling

4Roofconstructionisrotted

and

leak

ingcoveringlayer


old

townarea

8

Building'sconstruction

technolog

yan

dquality

5

Valuab

les

��Con

structionperiodsof

build-

ing'sevolution

A8

Fou

ndation

Bou

lderswithclay

bricks

mason

ry

4Under

basem

entexitswithou

t

grou

ndwater

pressure,this

washes

thelimemortarfrom

basem

entmason

ry.Thebase-

mentiswithou

twaterproo¯

ng.


d

Mem

orial

4

Rem

ainsof

di®erenteras

of

construction

5


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Tab

le4.

(Continued

)

Alt.

Design

Con

tent

Score

Notes

Category

Score

inpoints

Wall

Claybrick

mason

ry4

Oneof

facades

has

aga

palmost

from

thegrou

ndupto

theridge

isaga

pof

approxim

ately

25mm

inwidth.Basem

ent

wallsarewet.

Stylistic


5

Floors

1.W

ooden;

2.Vau

lted

arches

3Signi¯cantde°ection

s,alotof

tears

areob

served

in°oors.The

°oo

rsaretoo°exible.W

ooden

beamsaresemi-rotted.

Stylistic

epoch'sremainsof

the

buildingfacades

4

Architectural�

composite

valueof

facades

3

Roo

f:Bearingstruc-

ture/C

overing

Wooden/C

laytiling

4Signfulpartof

tilesisbroken

and

coveringlayer

isleak

ing.

Bear-

ingroof

constructionsareof

good

condition.


old

townarea

4


nologyan

dquality

4

Valuab

les

��Con

structionperiodsof

build-

ing'sevolution


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Table 5. The fundamental Saaty's scale.

Intensity of

importance on an

absolute scale De¯nition Explanation

1 Equal importance Two activities contribute equally to the

objective

3 Moderate importance of one overanother

Experience and judgment strongly favor oneactivity over another

5 Essential or strong importance Experience and judgment strongly favor one

activity over another

7 Very strong importance An activity is strongly favored and its dom-inance demonstrated in practice

9 Extreme importance The evidence favoring one activity over

another is of tile highest possible order of

a±rmation2, 4, 6, 8 Intermediate values between the

two adjacent judgments

When compromise is needed

Reciprocals If activity i has one of the above numbers assigned to it when compared with activity

j, then j has the reciprocal value when compared with i

Rational Ratios arising from the scale If consistency was to be forced by obtaining nnumerical values to span the matrix

Table 6. Determining historical-architectural criteria weights for cultural heritage objects assessment

by applying AHP method (the ¯rst expert).

Criteria

Criteria x1 x2 x3 x4 x5 x6 x7 x8

Historical, Cultural, andMemorial

x1 1 9 2 3 5 7 9 9

Remains of di®erent eras of

construction

x2 1/9 1 1/7 1/5 1/3 1/2 2 1/2

Stylistic epoch's remainsinside

x3 1/2 7 1 1 4 5 7 7

Stylistic epoch's remains of

the building facades

x4 1/3 5 1 1 1 3 5 5

Architectural �� composite

value of facades

x5 1/5 3 1/4 1 1 2 4 4

Building functionality in old

town area

x6 1/7 2 1/5 1/3 1/2 1 1 1

Building's construction

technology and quality

x7 1/9 1/2 1/7 1/5 1/4 1 1 1

Construction periods of

building's evolution

x8 1/9 2 1/7 1/5 1/4 1 1 1

Weights 0.364 0.034 0.229 0.153 0.104 0.046 0.032 0.038

Consistency Ratio 0.031


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Tab

le7.

Presumptiveresultsof

determininghistorical-architecturalcriteria

weigh

ts.

Criteriaweigh

tsaccordingto

experts

Group`sweigh

ts

Criteria

p1

p2

p3

p4

p5

p6

p7

w�(m

in)

w�(m

ax)

w�(geometricmean)


dMem

orial

x 10.36

40.23

60.31

10.24

80.32

60.15

80.23

50.15

80.36

40.260

Rem

ainsof

di®erenteras

ofconstruction

x 20.03

40.02

80.02

20.02

20.02

10.03

10.03

10.02

10.03

40.027

Stylistic


x 30.22

90.35

40.14

20.14

90.24

80.32

60.32

70.14

20.35

40.240

Stylistic

epoch'sremainsof

thebuildingfacades

x 40.15

30.15

50.11

80.32

30.15

40.24

60.10

50.10

50.32

30.167

Architectural��

compositevalueof

facades

x 50.10

40.06

30.26

50.11

20.04

40.10

20.16

60.04

40.26

50.105


oldtownarea

x 60.04

60.09

90.06

50.06

80.11

30.04

70.06

90.04

60.11

30.069

Building'sconstructiontechnologyan

dquality

x 70.03

20.02

10.03

10.04

90.03

00.02

20.02

10.02

10.04

90.028

Con

structionperiodsof

building'sevolution

x 80.03

80.04

40.04

60.03

10.06

70.06

90.04

80.03

10.06

90.047

Consisten

cyratio

0.03

10.07

0.05

0.04

0.03

0.05

0.04

Tab

le8.

ARAS-G

method

.InitialDMM

(crisp

criteria

values

andgray

criteria

weigh

ts).

Alternatives

Criteriavalues

�x1

�x2

�x3

�x4

�x5

�x6

�x7

�x8

ww�

w�

w�

w�

w�

w�

w�

w�

w�

w�

w�

w�

w�

w�

w�

w�

0.15

80.36

40.02

10.03

40.14

20.35

40.10

50.32

30.04

40.26

50.04

60.11

30.02

10.04

90.031

0.069

A0(O

pt.values)

99

99

99

99

A1

89

87

89

78

A2

43

54

55

63

A3

78

68

98

44

A4

74

55

66

54

A5

49

53

35

35

A6

44

55

55

44

A7

55

56

68

56

A8

45

54

34

44


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Tab

le9.

ARAS-G

method

.Normalized

DMM.

Alt.

Normalized

criteria

values

�� x1

�� x2

�� x3

�� x4

�� x5

�� x6

�� x7

�� x8

ww�

w�

w�

w�

w�

w�

w�

w�

w�

w�

w�

w�

w�

w�

w�

w�

0.15

80.36

40.02

10.03

40.14

20.35

40.10

50.32

30.04

40.26

50.04

60.11

30.02

10.04

90.03

10.069

A0

0.17

310.16

070.16

980.17

650.16

670.15

250.19

150.1915

A1

0.15

380.16

070.15

090.13

730.14

810.15

250.14

890.1702

A2

0.07

690.05

360.09

430.07

840.09

260.08

470.12

770.0638

A3

0.13

460.14

290.11

320.15

690.16

670.13

560.08

510.0851

A4

0.13

460.07

140.09

430.09

800.11

110.10

170.10

640.0851

A5

0.07

690.16

070.09

430.05

880.05

560.08

470.06

380.1064

A6

0.07

690.07

140.09

430.09

800.09

260.08

470.08

510.0851

A7

0.09

620.08

930.09

430.11

760.11

110.13

560.10

640.1277

A8

0.07

690.08

930.09

430.07

840.05

560.06

780.08

510.0851

Tab

le10

.The

scale

for

determining

integrated

multiple

criteria

value

of

heritagebuildings

orthebuildings'heri-

tage

valueratings.

Lingu

isticvariables

Level

VeryLow

(VL)

0.1

Low

(L)

0.2

Medium

Low

(ML)

0.3

Medium

(M)

0.5

Medium

High(M

H)

0.7

High(H

)0.8

VeryHigh(V

H)

0.9


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Tab

le11.

ARAS-G

method

.Normalized-w

eigh

tedDMM

withsolution

results.

Alt.

Normalized-w

eigh

tedcriteria

values

�x1

�x2

�x3

�x4

�x5

�x6

�x7

�x8

w�

��

��

��

��

��

��

��

�S

KC

A0

0.02

70.06

30.00

30.00

50.02

40.06

00.01

90.05

70.00

70.04

40.00

70.01

70.00

40.00

90.00

60.01

30.18

1.00

A1

0.02

40.05

60.00

30.00

50.02

10.05

30.01

40.04

40.00

70.03

90.00

70.01

70.00

30.00

70.00

50.01

20.16

0.87

H

A2

0.01

20.02

80.00

10.00

20.01

30.03

30.00

80.02

50.00

40.02

50.00

40.01

00.00

30.00

60.00

20.00

40.09

0.49

ML

A3

0.02

10.04

90.00

30.00

50.01

60.04

00.01

60.05

10.00

70.04

40.00

60.01

50.00

20.00

40.00

30.00

60.14

0.79

MH

A4

0.02

10.04

90.00

10.00

20.01

30.03

30.01

00.03

20.00

50.02

90.00

50.01

10.00

20.00

50.00

30.00

60.11

0.62

M

A5

0.01

20.02

80.00

30.00

50.01

30.03

30.00

60.01

90.00

20.01

50.00

40.01

00.00

10.00

30.00

30.00

70.08

0.45

ML

A6

0.01

20.02

80.00

10.00

20.01

30.03

30.01

00.03

20.00

40.02

50.00

40.01

00.00

20.00

40.00

30.00

60.09

0.52

M

A7

0.01

50.03

50.00

20.00

30.01

30.03

30.01

20.03

80.00

50.02

90.00

60.01

50.00

20.00

50.00

40.00

90.11

0.62

MA

80.01

20.02

80.00

20.00

30.01

30.03

30.00

80.02

50.00

20.01

50.00

30.00

80.00

20.00

40.00

30.00

60.08

0.46

M


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DMM (Table 8) was processed applying the ARAS-G method. The calculation

results are also presented in Table 11. The priority order of the investigated foun-

dation installment alternatives can be represented as (Fig. 2). Alternatives ranks as

follows:

A1 A3 A4 ¼ A6 A2 A8 A5

It means that the best alternative is the ¯rst (A1), and the worst alternative is the

¯fth (A5). It can be stated that the ¯rst alternative (A1) is only 87% of optimal

alternative performance level, and the performance of the worst alternative A5 is

only 45%.

According to the given data on the criteria describing the alternatives, rational

solutions about retro¯t and project management can be made.

4. Conclusion

This study pays attention to the analytic process to clarify the main features of

complicated decisions and develop an approach well suited to the decision context. In

this paper, the conceptual model for the integrated analysis and determining

buildings' heritage value ratings is developed. It is emphasized that multiple criterion

incorporation should begin with the computation of the nondominated set at least in

the case of terminal stage protocols.

Integrating of three di®erent multiple criteria techniques allows solving compli-

cated and sophisticated problems. The most signi¯cant criteria are identi¯ed

through three Delphi rounds and providing an alternative process for carrying out an

assessment of restoration urgency of cultural heritage. In addition, the composition

of the professional team and the number of experts can be °exible according to

di®ering situations. The proposed AHP-based approach for weights of criteria

1

0.87

0.49

0.79

0.62

0.450.52

0.62

0.46

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

A0 A1 A2 A3 A4 A5 A6 A7 A8

Fig. 2. Comparison of alternatives performance level.


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estimating, not only leads to a logical result but also enables the decision makers to

visualize the impact of each criterion on the ¯nal result. Finally, the multiple criteria

analysis of the projects by ARAS-G method allows determining value of projects'

in compared with the optimal project. The determined buildings' heritage values

are compared with the scale of buildings' heritage values and conclusions could be

made.

To illustrate the model's e±ciency, eight cultural heritages were applied and the

results were analyzed. The decision support model presented in this paper can be

utilized for objective evaluation in a realistic consultation and a fairly advanced

administration. Based on this system, heritage buildings are evaluated.

The eight criteria set presented in this paper is not perfectly satisfactory for all

countries.

The MCDM-based grading system is of considerable use to urban planners. It

provides them with a stronger basis for determining which decision should be made.

This would facilitate urban regeneration through the integration of the conserva-

tion scheme into the city development plan, while minimizing con°icts between

stakeholders.

However, new criteria should not be added in¯nitely as it is a costly process both

in terms of data collection and computation; further, it may only intensify the

con°ict between alternative targets without yielding additional bene¯ts.

Another alternative is to apply other MCDM methods that may be capable of

taking into account the di±culties in assigning \crisp values" to di®erent attributes.

It would seem that gray MCDM methods �� based on the assumption that attri-

butes are not valued precisely �� may also be used in such cases.

Development of such a ranking method is a promising area of future research on

built heritage.

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A MODEL BASED ON ARAS-G AND AHP METHODS FOR … filea model based on aras-g and ahp methods for multiple criteria prioritizing of heritage value zenonas turskis*, edmundas kazimieras