-
el
ChV1, Br
Multi-criteria decision makingAnalytical hierarchy process
n rvidof pialvieiple
tionnaire survey of building experts is conducted to assess the
relative importance of the criteria andaggregate them into six
independent assessment factors. The FEAHP is used to prioritize and
assign important
d criteria. A numerical example, illustrating the implementation
of the model isel provides guidance to building designers in
selecting sustainable building
n andan impenergindire
Automation in Construction 30 (2013) 113125
Contents lists available at SciVerse ScienceDirect
Automation in
j ourna l homepage: www.e lsto environmental performance targets
that appropriate strategiesand actions are needed to make
construction activities more sustain-able [13]. The pace of actions
towards sustainable application de-pends on decisions taken by a
number of actors in the constructionprocess: owners, managers,
designers, rms, etc. [4,3]. An importantdecision is the sustainable
selection of building materials to be usedin building projects.
Careful selection of sustainable building mate-rials has been
identied as the easiest way for designers to beginincorporating
sustainable principles in building projects [5]. The se-lection of
building materials is regarded as a multi-criteria decision
The earlier attempt to establish comprehensive means of
simul-taneously assessing a broad range of sustainability
considerations inbuilding materials was the Building Research
Establishment Envi-ronmental Assessment Method (BREEAM) [9]. BREEAM
known asthe rst commercially available and most widely used
assessmentmethod was established in 1990 in the United Kingdom.
Sincethen many different tools have been launched around the
world(e.g. Building for Environmental and Economic
Sustainability(BEES), Leadership in Energy and Environmental Design
(LEED),Building environmental performance assessment criteria
(BEPAC),problem [6], largely based on trusting exper
Corresponding author. Tel.: +44 7828049507.E-mail addresses:
[email protected] (P.O. Akadiri)
(P.O. Olomolaiye), [email protected] (E.A. Chinyio).1 Tel.:
+44 117 32 82211.2 Tel.: +44 1902 321043.
0926-5805/$ see front matter 2012 Elsevier B.V.
Allhttp://dx.doi.org/10.1016/j.autcon.2012.10.004ctly (through the
pres-response to these im-
rganizations committed
stage in decision-making. It should be noted that this pure form
ofsustainability assessment is a challenge to develop and evidence
ofachieving this in practice is yet to be seen [8].sures on often
inefcient infrastructure). Inpacts, there is growing consensus
among o1. Introduction
The construction, t-out, operatiobuildings are signicant factors
of humboth directly (through material andconsequent pollution and
waste) and 2012 Elsevier B.V. All rights reserved.
ultimate demolition ofact on the environmenty consumption and
the
numerical approach, due to lack of formal and availability of
measure-ment criteria or strategies. In addition, many of the
current evaluationapproaches were criticized for overemphasizing
the environmentalaspects [7]. Ideally, sustainability assessment
would integrate social,technical, environmental and economic
considerations at everymaterials.Fuzzy logic weightings for the
identiegiven. The proposed modMulti-criteria evaluation model for
the sbuilding projects
Peter O. Akadiri a,, Paul O. Olomolaiye b,1, Ezekiel A.a School
of Technology, University of Wolverhampton, Wulfruna Street,
Wolverhampton, Wb Faculty of Environment and Technology, University
of West of England, Coldharbour Lane
a b s t r a c ta r t i c l e i n f o
Article history:Accepted 10 October 2012Available online 12
December 2012
Keywords:Building material selectionSustainable criteria
Sustainable material selectioselection methods fail to
proprinciples, and the processproposes a building mater(FEAHP)
techniques, with aed based on sustainable trience rather than
using
, [email protected]
rights reserved.ection of sustainable materials for
inyio a,2
1LY, UKistol, BS16 1QY, UK
epresents an important strategy in building design. Current
building materialse adequate solutions for two major issues:
assessment based on sustainabilityrioritizing and assigning weights
to relevant assessment criteria. This paperselection model based on
the fuzzy extended analytical hierarchy processw to providing
solutions for these two issues. Assessment criteria are
identi-bottom line (TBL) approach and the need of building
stakeholders. A ques-
Construction
ev ie r .com/ locate /autconEnvironmental Resource Guide (ERG),
and Environmental ResourceGuide (ERG)). BREEAM, LEEDS, ENVEST and
other existing methodsfor assessing buildings whose remit is
largely restricted to an envi-ronmental protection and resource
efciency agenda have limitedutility for assessing socio and
economic factors as opposed to envi-ronmental sustainability, since
they are predominantly focused onenvironment which is just one of
the four principles underpinningsustainable building. Even against
this single principle, they are
-
Saaty [17], Saaty and Shang [27].Even though AHP has been widely
used to address the multi-
114 P.O. Akadiri et al. / Automation in Construction 30 (2013)
113125only able to offer relative assessment as opposed to absolute
[10].Another criticism that has been raised concerns the fact that
the ma-jority of the assessment methods were designed for new
construc-tion, and hence have focused on the design of the
constructedbuildings. Although energy, water and occupant comfort
were wellcovered in the tools, there was little focus on the effect
of the build-ing system's life during operation. This is especially
true for envelopeperformance. This tendency has resulted in the
failure of manyassessment methods to properly consider other
assessment criteriasuch as durability, lifecycle cost, and the
effects of premature build-ing envelope failures. To be considered
truly sustainable, assessmentmethods will have to be recast under
the umbrella of sustainabilityenvironmental, social, technical and
economic [11]. Broadeningthe scope of discussion beyond
environmental responsibility andembracing the wider agenda of
sustainability are increasingly neces-sary requirements.
Therefore there is a need for developing a systematic and
holisticsustainable material selection process of identifying and
prioritizingrelevant criteria and evaluating trade-offs between
environmental,economic, social and technical criteria [12]. The
characterization ofmaterial selection process as an essentially
multifaceted probleminvolving numerous, variegated considerations,
often with complextrade-offs among them, implied that a suitable
solution might befound among the family of multi-criteria decision
analysis (MCDA)methods [1316]. Further analysis and proling of the
selection prob-lem and the identication of the solution methods'
desirable capabil-ities, triggered the consideration of the
Analytic Hierarchy Process(AHP) developed by Saaty [17] as a
possible basis for sustainablematerial selection method
envisaged.
The analytic hierarchy process (AHP) [17,18] is widely used
fortackling multi-criteria decision-making problems in real
situations.Bahareh et al. [19] utilized the AHP as a multi-criteria
technique forsustainable assessment of ooring systems. They agree
that AHP pro-vides a framework for robust decision making that is
consistent withsustainable construction practices. The use of AHP
for sustainableassessment has been considered in approaches
developed by otherresearchers [6,7,13,1921]. In spite of its
popularity and simplicityin concept, this method is often
criticized for its inability to adequate-ly handle the inherent
uncertainty and imprecision associated withthe mapping of the
decision-maker's perception to exact (or crisp,according to the
fuzzy logic terminology) numbers.
To improve the AHP method and to facilitate sustainable
materials'selection process, the paper uses a fuzzy extended AHP
(FEAHP) ap-proach using triangular fuzzy numbers to represent
decision makers'comparison judgments and fuzzy synthetic extent
analysis [22]methodto decide the nal priority of different decision
criteria. The fuzzy settheory resembles human reasoning in its use
of approximate informa-tion and uncertainty to generate decisions.
It has the advantage ofmathematically representing uncertainty and
vagueness and provideformalized tools for dealing with the
imprecision intrinsic to manyproblems [22,23]. The proposed FEAHP
uses the triangular fuzzynumbers as a pair-wise comparison scale
for deriving the prioritiesof different selection criteria and
sub-criteria. The weight vectorswith respect to each element under
a certain criterion are developedusing the principle of the
comparison of fuzzy numbers. As a result,the priority weights of
the each material are calculated and basedon that, the most
sustainable material is selected. In particular, theapproach
developed can adequately handle the inherent uncertaintyand
imprecision of the human decision making process and providethe
exibility and robustness needed for the decision maker to
un-derstand the decision problem. These merits of the approach
devel-oped would facilitate its use in real-life situations for
makingeffective decisions.
Based on this information and the current research
deciencies,this paper proposes a multi-criteria decision-making
model using
the Fussy extended analytic hierarchy process (FEAHP) approach
tocriterion decision making problems, it has been generally
criticized be-cause of the use of a discrete scale of one to nine
which cannot handlethe uncertainty and ambiguity present in
deciding the priorities of dif-ferent attributes [28]. Even though
the discrete scale of AHP has the ad-vantages of simplicity and
ease of use, it is not sufcient to take intoaccount the uncertainty
associated with the mapping of one's percep-tion to a number [29].
The linguistic assessment of human feelingsand judgments is vague
and it is not reasonable to represent it interms of precise
numbers. It feels more condent to give interval judg-ments than xed
value judgments [28]. In this condition, linguistic vari-ables and
triangular fuzzy numbers can be used to decide the priority ofone
decision variable over the other. Synthetic extent analysis
methodis used to decide the nal priority weights based on
triangular fuzzyevaluate building materials based on their
sustainability. First,Section 2 describes the proposed FEAHP
approach. The developmentof sustainable assessment criteria for
building material selectionused in the FEAHP was discussed in
Section 3. Section 4 discussesthe complete implementation of the
FEAHP approach. The priorityweights computed for different
criteria, sub-criteria and alternativesare also discussed in this
section. Finally, Section 5 draws conclusionsand gives
recommendations where necessary. The current study con-tributes to
the building industry and sustainability research in at leasttwo
aspects. First it widens the understanding of selection criteria
aswell as their degree of importance. It also provides building
stake-holders a newway to select materials, thereby facilitating
the sustain-ability of building projects.
2. Fuzzy analytic hierarchy process methodology
2.1. Background
Singh et al. [24] describe AHP method as a multiple step
analyticalprocess of judgment, which synthesizes a complex
arrangement into asystematic hierarchical structure. It allows a
set of complex issues thathave an impact on an overall objective to
be compared with the impor-tance of each issue relative to its
impact on the solution of the prob-lem [25]. It is designed to cope
with the intuitive, the rational, and theirrational when making
multi-objective, multi-criterion and multi-actordecisionsexactly
the decision-making situation foundwithmaterial se-lection.
Furthermore, it can easily be understood and applied by
decisionmakers saddled with building material selection
process.
The application of AHP to a decision problem involves four
steps[25,26]. In structuring of the decision problem into a
hierarchicalmodel, material selection problem is dened, objective
is identied,criteria and attributes thatmust be satised to
objective are recognized.Objective is at rst level, criteria is at
second level, attributes are at thirdlevel, and decision
alternatives are at fourth level in hierarchical struc-ture of the
problem. In making pair-wise comparisons and obtainingthe judgment
matrix, the elements at a particular level are comparedusing
nine-point numerical scale to dene howmuchmore an elementis
important than other. If A and B are the elements to be
compared,then 1 denes that A and B are equal in importance, and 9
denesthat A is extremely more important. All pair-wise comparisons
aregiven in a judgment matrix. The next step is to determine the
localweights and consistency of comparisons. Local weights of the
elementsare calculated from the judgment matrix using eigenvector
method. Asthe comparisons in the matrix are made subjectively,
consistency ratiocan be computed. If the ratio is less than 0.1
human judgments is ac-ceptable. In the last step, local weights at
various levels are aggregatedto obtain nal weight of alternatives.
The nal weights represent therating of the alternatives in
achieving the aim of the multi-criterion de-cision making problem.
Further information about AHP can be found innumbers and so-called
as fuzzy extended AHP (FEAHP) [30].
-
Fuzzy set theory has proven advantageswithin vague, imprecise
anduncertain contexts and it resembles human reasoning in its use
of ap-proximate information and uncertainty to generate decisions
[31]. Itwas specially designed to mathematically represent
uncertainty andvagueness and provide formalized tools for dealing
with the impreci-sion intrinsic tomany decision problems. The FEAHP
is the fuzzy exten-sion of AHP to efciently handle the fuzziness of
the data involved in thedecision of selecting building materials
based on their sustainability. Itis easier to understand and it can
effectively handle both qualitativeand quantitative data in the
multi-attribute decision making problems.In this approach
triangular fuzzy numbers are used for the preferencesof one
criterion over another and then by using the extent analysismethod,
the synthetic extent value of the pairwise comparison is
calcu-lated. Based on this approach, the weight vectors are decided
and nor-malized, thus the normalized weight vectors will be
determined. As aresult, based on the different weights of criteria
and attributes thenal priorityweights of the alternative
sustainablematerial are decided.
Fuzzy numbers are intuitively easy to use in expressing
thedecision-maker's qualitative assessments. A fuzzy number can
alwaysbe given by its corresponding left and right representation
of each de-gree of membership [33]:
M M1 y ;Mr y l ml y;u mu y
; y 0;1 2
where l(y) and r(y) denote the left side representation and the
rightside representation of a fuzzy number, respectively. The
algebraic op-erations with fuzzy numbers can be found in
[30,33].
TFNs M1, M3, M5, M7 and M9 are used to represent the
pairwisecomparison of decision variables from Equal to
Extremelypreferred, and TFNs M2, M4, M6 and M8 represent the middle
prefer-ence values between them. Fig. 2 shows the membership
functions ofthe TFNs,Mi=(mi1,mi2,mi3), where i=1, 2,, 9
andmi1,mi2,mi3 arethe lower, middle and upper values of the fuzzy
number Mi respec-tively. Higher the value of (mi3mi1) or (mi1mi3)
signies the greaterfuzziness of the judgment.
115P.O. Akadiri et al. / Automation in Construction 30 (2013)
113125The highest priority would be given to the material with
highest sus-tainability weight.
2.2. Establishment of triangular fuzzy numbers
Saaty [17] contended that the geometric mean accurately
repre-sents the consensus of experts and is the most widely used in
practi-cal applications. Here, geometric mean is used as the model
fortriangular fuzzy numbers. Zadeh [32] introduced the fuzzy set
theoryto deal with the uncertainty due to imprecision and
vagueness.A major contribution of fuzzy set theory was its
capability ofrepresenting vague data. The theory also allowed
mathematical oper-ators and programming to apply to the fuzzy
domain. A fuzzy set is aclass of objects with a continuum of grades
of membership. Such a setis characterized by a membership function,
which assigns to each ob-ject a grade of membership ranging between
0 and 1. In this set thegeneral terms such as large, medium, and
small will each beused to capture a range of numerical values. A
triangular fuzzy num-ber (TFN), M is shown in Fig. 1. A TFN is
denoted simply as (l, m, u).The parameters l, m, and u denote the
smallest possible value, themost promising value and the largest
possible value that describe afuzzy event [33]. When l=m=u, it is a
non-fuzzy number by con-vention [30]. Each TFN has linear
representations on its left andright side such that its membership
function can be dened as [33];
x 0; xbl;xl = ml ; lxm;ux = um ;mxu;0; x > u:
8>>>:
9>>=>>;: 1
Ml(y) Mr(y)
M
M
1.0
0.0l m u
Fig. 1. A triangular fuzzy number, M [31].2.3. Calculation of
priority weights at different level of hierarchy
In crisp AHP, a discrete scale of one to nine is used to decide
thepriority of one decision variable over another whereas in fuzzy
AHPfuzzy numbers or linguistic variables are used. In practice,
decisionmakers usually prefer triangular or trapezoidal fuzzy
numbers.
Since fuzzy numbers are used in fuzzy AHP, the solution
methodsdifferentiate from crisp AHP. The most common method used in
thesolution of fuzzy AHP applications is the extent analysis method
pro-posed by Chang [34]. The extent analysis method is used to
considerthe extent of an object to be satised for the goal, that
is, satised ex-tent. In the method, the extent is quantied by using
a fuzzy num-ber. On the basis of the fuzzy values for the extent
analysis of eachobject, a fuzzy synthetic degree value can be
obtained, which is de-ned as follows.
In a material selection problem, let p={p1, p2,, pn} represent
theelements of the alternatives as an object set and let Q={q1,
q2,, qm}represent the elements of the material selection criteria
as a goal set.According to the concept of Chang's [34] extent
analysis, each object istaken and extent analysis for each goal,
Oi, is performed respectively.Therefore them extent analysis values
for each object can be obtained,with the following signs:
M1oi;M2oi; :::::::;M
moi ; where i 1;2; :::::::;n 3
where all theMoij (j=1, 2,........ m) are triangular fuzzy
numbers.
M1 M2 M3 M4 M5 M6 M8
equallyimportant
moderatelyimportant
stronglyimportant
very strongly important
extremelyimportant
1 3 5 7 9 X
M(x)
1
0 2 4 6 8
M7 M9Fig. 2. The membership functions of the triangular fuzzy
numbers.
-
The steps of Chang's extent analysis can be given as in the
follow-ing [33]:The value of fuzzy synthetic extent with respect to
ith objectis dened as
Fi Xmj1
MjoiXni1
Xmj1
Mjoi
24
351: 4
The value ofj=1m Moij can be found by performing the fuzzy
addi-tion operation of m extent analysis values from a particular
matrixsuch that
116 P.O. Akadiri et al. / Automation in Construction 30 (2013)
113125Xmj1
Mjoi Xmj1
lj;Xmj1
mj;Xmj1
uj
0@
1A 5
and the value of [i=1n j=1m Moij ]1 can be obtained by
performingthe fuzzy addition operation of Moij (j=1, 2,....... m)
such that
Xni1
Xmj1
Mjoi Xni1
li;Xni1
mi;Xni1
ui
!6
and then compute the inverse of the vector in equation above Eq.
(6)such that
Xni1
Xmj1
Mjoi
24
351 1Xn
i1ui
;1Xn
i1mi
;1Xn
i1li
0BBBB@
1CCCCA: 7
1 The degree of possibility of M2=(l2, m2, u2)M1=(l1, m1,u1)
isdened as
V M2M1 supxy
min M1 x ; M2 y h i
8
when a pair (x,y) exists such that xy and M1 x M2 y 1;then we
have V(M1M2)=1. Since M1 and M2 are convex fuzzynumbers so,
V(M1M2)=1 if m11m21 and
V M2M1 hgt M1M2 M2 d ; 9
where d is the ordinate of the highest intersection point D
betweenM1 and M2 : Fig. 3 shows the intersection between M1 and M2.
Tocompare M1 and M2, we need both the values of V(M1M2)
andV(M2M1).
2 The degree of possibility for a convex fuzzy number to be
greaterthan k convex fuzzy numbers Mi (i=1, 2,, k) can be dened
by
V MM1;M2; :::::;Mk V MM1 and MM2 and ::::and MMk minV MMi ; i
1;2; ::::::k:
10Fig. 3. The intersection between M1 and M2 [35].If
m Pi minV FiFk ; 11
for k=1, 2,......, n; k i, then the weight vector is given
by
Wp m P1 ;m P2 ; ::::;m Pn T ; 12
where Pi(i=1, 2,..... n) are n elements.3 After normalizing Wp,
we get the normalized weight vectors as
W w P1 ;w P2 ; :::::;w Pn T ; 13
whereW is a non-fuzzy number and this gives the priority
weightsof one alternative over the other.
3. Development of sustainable assessment criteria
One of the main objectives of this paper is to develop a
holisticsustainable assessment criteria (SAC) set to assist design
team mem-bers in the selection of sustainable building materials
for buildingproject. A wide scope review of literature revealed
that there wasno comprehensive list of assessment criteria that
covers the princi-ples of sustainability, developed specically for
material selection inbuilding projects. In trying to develop a set
of criteria, Foxon et al.[36] proposed the consideration of two key
factors. What use willbe made of this set of criteria? To what
extent can any set of criteriaencompass the range of issues to be
considered under the headingof sustainability? Some of these issues
have been considered in ap-proaches developed by other researchers
[37,12,24,7,38,39]. The fol-lowing set of guidelines has been
developed to aid the choice ofcriteria to assess the options under
consideration:
(1) ComprehensivenessThe criteria chosen should cover the four
categories of eco-nomic, environmental, social and technical, in
order to ensurethat account is being taken of progress towards
sustainabilityobjectives. The criteria chosen need to have the
ability to dem-onstrate movement towards or away from
sustainability,according to these objectives.
(2) ApplicabilityThe criteria chosen should be applicable across
the range of op-tions under consideration. This is needed to ensure
the compa-rability of the options.
(3) TransparencyThe criteria should be chosen in a transparent
way, so as tohelp stakeholders to identify which criteria are being
consid-ered, to understand the criteria used and to propose
anyother criteria for consideration.
(4) PracticabilityThe set of criteria chosenmust form a
practicable set for the pur-poses of the decision to be assessed,
the tools to be used and thetime and resources available for
analysis and assessment. Clearly,the choice of sustainability
criteria will inuence the outcome ofthe decision beingmade, aswill
themethod of comparison or ag-gregation chosen. The above factors
provide initial guidance inthe choice of criteria. Using a pool of
existing criteria, combinedwith sustainable concerns and
requirements of project stake-holders, a list of assessment
criteria (see Table 1) was developed.These criteria are identied
under three categories: Environmen-tal, Technical and
Socio-economic.
These categories aim to encapsulate the economic,
environmentaland social principles of sustainability, together with
technical criteria,which relate primarily to the ability of
buildings and its componentsystem to sustain and enhance the
performance of the functions for
which it is designed. For any decision process, the selected
criteria
-
must be broadly applicable to allmaterial options if comparative
eval-uation is to be achieved.
3.1. Importance of derived criteria
Based on the derived criteria in Table 1, an industry
questionnairesurvey was designed to investigate the perspective of
constructionprofessionals on the importance of the criteria for
material selection.Ninety experts comprising of architects,
building designers, contractorsand structural engineers that
inuence material selection were thusasked to rate the level of
importance of the derived criteria based on a
index analysis was selected in this study to rank the criteria
according totheir relative importance. The following formula is
used to determine therelative index (RI) [5962]:
RI w=AxN 14
where w, is the weighting as assigned by each respondent on a
scale ofone to ve with one implying the least and ve the highest. A
is thehighestweight (i.e. 5 in our case) andN is the total number
of the sample.Five important levels are transformed from Relative
Index values: High(H) (0.8RI1), HighMedium (HM) (0.6RIb0.8), Medium
(M)
Table 1Sustainable assessment criteria for building material
selection.
Source Environmental criteria Social-economic criteria Technical
criteria
Literature review, existing assessmentmethods and focus
ofconstruction stakeholders
E1: Potential for recycling and reuse [40]E2: Availability of
environmentally sounddisposal options [41]E3: Impact of material on
air quality [19]E4: Ozone depletion potential [42]E5: Environmental
Impact duringmaterial harvest [43]E6: Zero or low toxicity [42]E7:
Environmental statutory compliance [44]E8: Minimize pollution e.g.
air, land [19]E9: Amount of likely wastage in useof material
[45,42]E10: Method of raw material extraction [43]E11: Embodied
energy within material [46,47]
S1: Disposal cost [39,48]S2: Health and safety [42]S3:
Maintenance cost [7,49]S4: Esthetics [50]S5: Use of local material
[44]S6: Initial-acquisition cost [51]S7: Labor availability
[52,21]
T1: Maintainability [53,42]T2: Ease of Construction
(buildability) [52,54]T3: Resistance to decay [55]T4: Fire
resistance [42,56]T5: Life expectancy of material(e.g. strength,
durability etc.)[7,57]T6: Energy saving and thermalinsulation
[58]
1.63.73.38.63.1
117P.O. Akadiri et al. / Automation in Construction 30 (2013)
113125scale of 15,where 1 is least important, 2 fairly important, 3
important,4 very important, and 5 extremely important. Respondents
were alsoencouraged to provide supplementary criteria that they
consider to inu-encebuildingmaterial selectionbutwerenot listed in
the survey. Relative
Table 2Rank of sustainable assessment criteria for building
material selection.
Sustainable performance criteria Valid percentage of score of
(%)
1 2 3 4 5
Environmental criteriaE7: Environmental statutory compliance 4.4
1.1 13.2 29.7 5E8: Minimize pollution 1.1 1.1 18.0 46.1 3E6:
Zero/low toxicity 3.3 2.2 22.2 38.9 3E4: Ozone depletion potential
3.3 8.8 19.8 39.6 2E1: Recyclable/reusable material 1.1 7.7 29.7
38.5 2
E9: Amount of likely wastage in use 3.3 7.7 29.7 39.6 19.8E11:
Embodied energy in material 1.1 9.9 28.6 47.3 13.2E2: Environmental
sound disposal options 1.1 10.1 36.0 34.8 18.0E3: Impact on air
quality 4.4 8.8 35.2 39.6 12.1E5: Impact during harvest 4.4 15.4
31.9 37.4 11.0E10: Methods of extraction of raw materials 5.5 19.8
45.1 20.9 8.8
Technical criteriaT1: Maintainability 0.0 0.0 3.3 47.3 49.5T6:
Energy saving and thermal insulation 0.0 0.0 3.2 50.4 46.2T5: Life
expectancy (e.g. durability) 0.0 0.0 4.4 50.5 45.1T4: Fire
resistance 0.0 0.0 13.2 44.0 42.9T3: Ease of
construction/buildability 0.0 0.0 9.9 53.8 36.3T2: Resistance to
decay 1.1 1.1 28.6 48.4 20.9
Socio-economic criteriaS4: Esthetics 0.0 0.0 10.1 30.3 59.6S3:
Maintenance cost 0.0 0.0 12.1 56.0 31.9S2: Health and safety 1.1
3.4 15.9 40.9 38.6S6: First cost 0.0 5.5 14.3 49.5 30.8S1: Disposal
cost 1.1 0.0 22.0 47.3 29.7S5: Use of local materials 3.3 5.5 23.1
48.4 19.8S7: Labor availability 5.5 16.5 39.6 29.7 8.8(0.4RIb0.6),
MediumLow (ML) (0.2RIb0.4), and Low (L)(0RIb0.2). A cut-off value
of 0.4 is used and identied those criteriaas relevant for which the
values are greater than or equal to 0.4. Fromthe results in Table
2, an interesting observation is that none of the
Relative index Ranking by category Overall ranking Importance
level
0.846 1 7 H0.820 2 10 H0.793 3 13 MH0.763 4 15 MH0.749 5 17
MH
0.729 6 18 MH0.723 7 19 MH0.717 8 20 MH0.692 9 21 MH0.670 10 22
MH0.615 11 24 MH
0.892 1 2 H0.886 2 3 H0.881 3 4 H0.859 4 5 H0.853 5 6 H0.774 6
14 MH
0.898 1 1 H0.839 2 8 H0.825 3 9 H0.810 4 11 H0.808 5 12 H0.752 6
16 MH0.639 7 23 MH
-
with rotated factor loading matrix, the percentage of variance
attrib-utable to each factor and the cumulative variance values are
shown inTable 5. From the table, it can be seen that the three
factors accountedfor 71.3% of the total variance of the eleven
environmental criteria.
Following the results of the survey, the 24 criteria identied
asbeing important components of sustainable material selection
are
Table 3Factor loadings for socio-economic criteria after varimax
rotation.
Observed socio-economic variable Latent socio-economic
factors
Life cycle cost Social benet
S3: Maintenance cost 0.757S6: First cost 0.693S1: Disposal cost
0.576S4: Esthetics 0.830S5: Use of local material 0.759S2: Health
and safety 0.579S7: Labor availability 0.556Eigenvalues 1.556
2.205Percentage of variance (%) 22.234 31.502
Table 5Factor loadings for environmental criteria after varimax
rotation.
Observed environmental variable Latent environmental factors
Environmentalimpact
Resourceefciency
Wasteminimization
E7: Environmental statutorycompliance
0.882
E6: Zero or low toxicity 0.824E4: Ozone depletion potential
0.719E8 : Minimize pollution(e.g. water, land)
0.586
E3: Impact of material on air quality 0.557E10: Method of
rawmaterial extraction
0.893
E9: Amount of likely wastage in useof material
0.773
E11: Embodied energywithin material
0.588
E5: Environmental Impactduring material harvest
0.546
E2: Availability of environmentally 0.912
Table 6Summary of roong options for the proposed project.
Description Option A Option B Option C
118 P.O. Akadiri et al. / Automation in Construction 30 (2013)
113125criteria fall under the medium and other lower importance
level. Thisclearly shows how important the criteria are to building
designers inevaluating sustainable building materials. All criteria
were rated withHigh or HighMedium importance levels, and all were
used in theassessment process.
3.2. Factor analysis
Factor analysis was employed to analyze the structure of
interrela-tionships among the criteria. Although the most signicant
criteriawere identied using ranking analysis, some of them are
likely to beinter-related with each other through an underlying
structure of pri-mary factors. Factor analysis was used to obtain a
concise list of SACs.It is conducted through a two-stage process:
factor extraction and factorrotation. Before the factor analysis,
validity test for factors is conductedaccording to themethod by
Kaiser [63]. By KaiserMethod, a value calledeigenvalue under 1 is
perceived as being inadequate and therefore un-acceptable for
factor analysis. For the socio-economic criteria, the anal-ysis
results showed that the KaiserMeyerOlkin [KMO] measure ofsampling
adequacy was 0.606, larger than 0.5, suggesting that the sam-plewas
acceptable for factor analysis. The Bartlett Test of
Sphericitywas96.100 and the associated signicance level was 0.000,
indicating thatthe population correlation matrix was not an
identity matrix. Both ofthe tests showed that the obtained data in
socio-economic categorysupported the use of factor analysis and
that these could be groupedinto a smaller set of underlying
factors. Using principal component anal-ysis, the factor analysis
extracted two latent factors with eigenvaluesgreater than 1.0 for
the 7 socio-economic criteria, explaining 53.7% ofthe variance. The
rotated factor loadingmatrix based on the varimax ro-tation for the
two latent factors is shown in Table 3.
The component matrix identies the relationship between the
ob-served variables and the latent factors. The relationships are
referredto as factor loadings. The higher the absolute value of the
loading, themore the latent factor contributes to the observed
variable. Small fac-tor loadings with absolute values less than 0.5
were suppressed to
Cumulative of variance (%) 22.234 53.736help simplify Table 3.
For further interpretation, the two latent factorsunder the
socio-economic category (shown in Table 3) are givennames as:
Factor 1: life cycle cost; and Factor 2: social benet. Similar
Table 4Factor loadings for technical criteria after varimax
rotation.
Observed technical variable Latent technical factors
Performance capability
T4: Fire resistance 0.799T3: Resistance to decay 0.740T6: Energy
saving and thermal insulation 0.724T5: Life expectancy of material
0.712T2: Ease of construction 0.658T1: Maintainability
0.604Eigenvalues 3.016Percentage of variance (%) 50.264factor
analyses were performed to identify the underlying structuresfor
technical and environmental categories. In the technical
category,the results for the factor analysis showed that the KMO
measure was0.804 and the Bartlett's test (p=0.000) was also
signicant, whichindicated that the factor analysis was also
appropriate in identifyingthe underlying structure of the technical
category. The results of theanalysis are presented in Table 4 Just
one factor named Factor 6: per-formance capability was extracted,
explaining 50.3% of the total vari-ance of the six technical
criteria.
For Environmental category, both the KMO measure of
samplingadequacy test (0.801) and Bartlett's sphericity (p=0.000)
were sig-nicant, which indicated that factor analysis was also
appropriate.Three factors under environmental category were
extracted fromthe factor analysis, namely, Factor 3: environmental
impact; Factor4: Resource efciency; and Factor 5: waste
minimization. Along
sound disposal optionsE1: Potential for recycling and reuse
0.871Eigenvalues 5.505 1.216 1.116Percentage of variance (%) 50.048
11.057 10.149Cumulative of variance (%) 50.048 61.105
71.254Elementtype
Pitched Roof TimberConstruction
Pitched Roof TimberConstruction
Pitched Roof TimberConstruction
Buildingtype
Residential Residential Residential
Element Timber trussedrafters and joists withinsulation,
roongunderlay, counterbattens, battens andUK produced con-crete
interlockingtiles
Structurally insulatedtimber panel systemwith OSB/3 each
side,roong underlay,counter battens,battens and UK pro-duced
reclaimed claytiles
Structurallyinsulated timberpanel system withplywood
(temperateEN 6362) deckingeach side, roongunderlay, counterbattens,
battens andUK produced Fibercement slates
Size of tileor slate
420 mm330 mm 420 mm330 mm 420 mm330 mm
Pitch ofroof
22.5 22.5 22.5
-
analyzed and ranked according to expert's opinions. The 24
criteriawere further compressed into 6 assessment criteria factors
of envi-ronmental impact, resource efciency, waste minimization,
life cyclecost, performance capability and social benet for easy
evaluation.Since these criteria are derived from the survey through
expertopinion, they symbolize the sustainable criteria that promote
socio-economic, technical and environmental consideration in
building ma-terial assessment and selection. Consideration of these
six criteria inmaterial selection will ensure sustainable
development in buildingdesign and construction. In the next
section, the mathematicalmulti-criteria decision-making model used
for this study is brieypresented.
4. Implementation of the FEAHP selection model
The worked example for elucidating the application of the
modelin practice involves the application to a realistic scenario
of a buildingmaterial selection problem. The case study used
intends to provide anindication of the use of the FEAHP
multi-criteria decision-makingmodel for the problem analyzed (i.e.,
the selection of sustainablebuilding materials). The case study
involves the design of a singlefamily home located in a light
residential area of Wolverhampton,United Kingdom. An architectural
rm is working with a client to se-lect materials (in this case
roong elements) for a residential build-ing. The rm wants to take
into account all the possible importantcriteria which determine
sustainability of roong elements. A
decision-making group is formed which consists of ve
constructionexperts from each strategic decision area. Detailed
discussion onevery criteria, sub-criteria and alternative materials
has beenconducted and based on expert rating and opinion, six
sustainable as-sessment criteria have been identied. The discussion
has been fur-ther prolonged to decide the twenty four sub-criteria
with threepotential roong elements.
Table 6 summarizes the details for the three options of roong
el-ements for the proposed project. The description of the three
optionsis based on the standard practices and construction details
commonlyused in the United Kingdom.
These 3 roong elements described above will be analyzed for
theselection of sustainable option among alternatives. In other
words,this section will analyze, through the use of the
mathematicalmulti-criteria decision-making model described in
Section 2, whichone is the most sustainable roong material for this
scenario.
4.1. Fuzzy AHP procedure for the sustainable building
materialsselection problem
In sustainable material selection problem, the relative
importanceof different decision criteria involves a high degree of
subjective judg-ment and individual preferences. The linguistic
assessment of humanfeelings and judgments are vague and it is not
reasonable to representthem in terms of precise numbers. It feels
more condent to give in-terval judgments. Therefore triangular
fuzzy numbers were used in
SELECTING SUSTAINABLE
MATERIAL
Environmental impact
Life cycle cost Resource efficiency
Waste minimization
Performance capability
Social benefit
Environmental statutory
Initial cost Method of raw material
ex
Amwas
Em
Envimp
h
Environmental sound disposal
Fire resistance Use of local material
Goal
Criteria
119P.O. Akadiri et al. / Automation in Construction 30 (2013)
113125compliance
Zero /low toxicity
Ozone depletion
Minimize pollution
Impact on air quality
Maintenance cost
Disposal costSub criteria
Alternatives Option A Fig. 4. Hierarchy of thetraction
ount of tage in use
bodied energy
ironmental act during arvest
option
Recycling and Reuse
Resistance to decay
Energy saving and thermal insulation
Life expectancy
Ease of construction
Maintainability
Aesthetics
Health and safety
Material availability
Option B Option Cdecision problem.
-
this problem to decide the priority of one decision variable
over an-other. The triangular fuzzy numbers were determined from
reviewingliterature [33]. Then synthetic extent analysis method was
used to de-cide the nal priority weights based on triangular fuzzy
numbers andso-called as fuzzy extended AHP. In the following
sections, the mainsteps of the method will be explained in
detail.
Step 1. Dene the main criteria and sub-criteria for material
selectionto design the fuzzy analytical hierarchy process tree
structure.
First the overall objective of the material selection problem
has
0:28 0:41 0:33 0:23 V F2F3 1;V F23F4 1;V F2F6 1:
efciency Waste minimization Performance capability Social benet
Wo
4 1/6,1/5,1/4 1/8,1/7,1/6 1/6,1/5,1/4 0.1552 1,1,2 1/4,1/3,1/2
1/4,1/3,1/2 0.310
2,3,4 1/4,1/3,1/2 1,1,2 0.0792 1,1,1 1/6,1/5,1/4 1/4,1/3,1/2
0.058
4,5,6 1,1,1 1,1,2 0.3542,3,4 1/2,1,1 1 0.044
Table 7The linguistic variables and their corresponding fuzzy
numbers.
Intensity ofimportance
Fuzzynumber
Denition Membershipfunction
9 ~9 Extremely more importance (EMI) (8, 9,10)7 ~7 Very strong
importance (VSI) (6, 7, 8)5 ~5 Strong importance (SI) (4, 5, 6)3 ~3
Moderate importance (MI) (2, 3, 4)1 ~1 Equal importance (EI) (1, 1,
2)
120 P.O. Akadiri et al. / Automation in Construction 30 (2013)
113125been identied which was selection of sustainable materials
forbuilding project. In selecting sustainable materials, a lot of
criteriashould be taken into account. All of the possible important
criteriawhich could affect the sustainability of building materials
have beendiscussed with experts in the Construction sector. Also
other materialselection studies in the literature were reviewed
with the expert. Bycombining the criteria determined by the expert
and the criteriaused in the literature, the main criteria and the
sub-criteria in thestudy were determined and validated.
After the main criteria, sub-criteria and alternatives were
deter-mined, the hierarchy of the material selection problem was
struc-tured. Fig. 4 shows the structuring of the material selection
problemhierarchy of four levels. The top level of the hierarchy
representsthe ultimate goal of the problem which is to choose a
sustainableroong element among options for the project described
previously.The goal is placed at the top of the hierarchy. The
hierarchy descendsfrom the more general criteria in the second
level to sub-criteria inthe third level to the alternatives at the
bottom or fourth level. Thegeneral criteria level involved six
major criteria: environmental im-pact, life cycle cost, resource
efciency, waste minimization, perfor-mance capability and social
benet. The decision-making teamconsidered three roong elements for
the decision alternatives, andlocated them on the bottom level of
the hierarchy.
Step 2. Calculate the weights of the main attributes,
sub-attributesand alternatives.
After the construction of the hierarchy, the different
priorityweights of each main criteria, sub-criteria and
alternatives were cal-culated using the fuzzy extended AHP
approach. The comparison ofthe importance of one main criteria,
sub-criteria and alternativeover another were achieved by the help
of the questionnaire. Thequestionnaires facilitate the answering of
pair-wise comparison ques-tions. The preference of one measure over
another was decided by theavailable research and the experience of
the experts.
First the expert compared themain criteria with respect to
themaingoal; then the expert compared the sub-criteria with respect
to themain criteria. At the end, the expert compared the roong
material op-tions with respect to each sub-criteria. The expert
used the linguisticvariables to make the pair-wise comparisons.
Then the linguistic vari-ables were converted to triangular fuzzy
numbers. Table 7 shows thelinguistic variables and their
corresponding triangular fuzzy numbers.Owing to the limited space,
the pair-wise comparison matrix of themain criteria with respect to
the goal will be presented here.
After the pair-wise comparison matrices were formed, the
consis-tency of the pair-wise judgment of each comparison matrix
waschecked using the calculation method of consistency index
and
Table 8The Fuzzy evaluation of criteria with respect to the
overall objective.
Main criteria Environmental impact Life cycle cost Resource
C1: Environmental impact 1,1,1 1/6,1/5,1/4 1/6,1/5,1/C2: Life
cycle cost 4,5,6 1,1,1 1/4,1/3,1/C3: Resource efciency 4,5,6 2,3,4
1,1,1C4: Waste minimization 4,5,6 1/2,1,1 1/4,1/3,1/C5: Performance
capability 6,7,8 2,3,4 2,3,4C6: Social benet 4,5,6 2,3,4
1/2,1,1consistency ratios in crisp AHP discussed in [64]. Each
triangular fuzzynumber, M=(l, m, u) in the pair-wise comparison
matrix wasconverted to a crisp number using M_crisp=(4m+ l+u)/6.
Afterthe fuzzy comparison matrices were converted into crisp
matrices, theconsistency of each matrix was checked by the method
in crisp AHP[64]. After calculation, the consistency ratio of each
comparison matrixwas found to be under 0.10. So we can conclude
that the consistency ofthe pair-wise judgments in all matrices is
acceptable. Then the priorityweights of eachmain criteria,
sub-criteria and alternativewere calculat-ed using FAHP method. As
an example the calculation of the priorityweights of the main
criteria will be explained in detail below.
By using the values in Table 7, the linguistic variables in a
compar-ison matrix can be converted to triangular fuzzy numbers.
The fuzzyevaluation matrix with respect to the goal with triangular
fuzzy num-bers can be seen in Table 8. In order to nd the priority
weights of themain criteria, rst the fuzzy synthetic extent values
of the attributeswere calculated by using Eq. (4). The different
values of fuzzy syn-thetic extent with respect to the six different
criteria are denoted byF1, F2, F3, F4, F5, and F6,
respectively.
F1 9;11;13 1=39:62;1=33:32;1=27:52 0:23;0:33;0:47 ;
F2 7:9;9:5;11:17 1=39:62;1=33:32;1=27:52 0:20;0:28;0:41 ;
F3 4:8;6;7:34 1=39:62;1=33:32;1=27:52 0:12;0:18;0:27 ;
F4 3:46;4:16;4:97 1=39:62;1=33:32;1=27:52 0:09;0:12;0:18 ;
F5 2:36;2:66;3:14 1=39:62;1=33:32;1=27:52 0:06;0:08;0:11
F6 2:34;3;4 1=39:62;1=33:32;1=27:52 0:05;0:10;0:15 :
The degree of possibility of Fi over Fj (I j) can be determined
byEqs. (8) and (9).
V F1F2 1;V F1F3 1;V F1F4 1V F1F5 1;V F1F6 1
V F2F1 0:23 0:41 0:78
-
Similarly,
V F3F1 0:21;V F3F2 0:41;V F3F4 1;V F3F5 1;V F3F6 1;
V F4F1 0:31;V F4F2 0:14;V F4F3 0:5;V F4F5 1;V F4F6 1;
V F5F1 0:92;V F5F2 0:82;V F5F3 0:11;V F5F4 0:33;V F5F6 1
V F6F1 0:82;V F6F2 0:71;V F6F3 0:34;V F6F4 0:44;V F6F5 0:31:
With the help of Eq. (11), the minimum degree of possibility
canbe stated as below:
M C1 minV F1F2; F3; F4; F5; F6 min 1;1;1;1;1 1:
Similarly,m (C2)=0.78,m (C3)=0.21,m (C4)=0.14,m (C5)=0.11,and m
(C6)=0.44.
Therefore theweight vector is given asWc=(1, 0.78,
0.21.0.14,0.11,0.44)T and after the normalization process, the
weight vector with re-spect to decision criteria C1, C2, C3, C4,
C5, and C6 can be expressed asfollows:Wo=(0.155 environmental
impact, 0.310 life cycle cost, 0.079
resource efciency, 0.058wasteminimization, 0.354 performance
capability, 0.044 social benet)T. We can conclude that the most
importancriteria in the sustainable material selection process for
the projecunder consideration is Performance capability because it
has thehighest priority weight. Life cycle cost is the next
preferred criteria.
The complete result is shown in Table 8.Now the different
sub-criteria are compared under each of the cri
terion separately by following the same procedure as
discussedabove. Whenever the value of (n11n23)>0, the elements
of the matrix must be normalized and then do the same process to nd
theweight vector of each criteria. The fuzzy comparison matrices of
thesub-criteria and the weight vectors of each sub-criterion are
shownin Table 9.
Similarly the fuzzy evaluation matrices of decision
alternativeand corresponding weight vector of each alternative with
respect tocorresponding sub-criteria are determined. The priority
weights omaterials with respect to each criterion are given by
adding theweights per materials multiplied by weights of the
correspondingcriteria. The priority weights of each main criteria,
sub-criteria, andalternative can be found in Table 10.
Step 3. Synthesizing the results.After computing the normalized
priority weights for each pair
wise comparison judgment matrices (PCJM) of the AHP hierarchythe
next phase is to synthesize the rating for each criterion. The
nor
Table 9Fuzzy evaluation of the attributes with respect to
sub-criteria.
Environmental impact Environmental statutory compliance Zero/low
toxicity Ozone depletion Minimize pollution Impact on air quality
Priority vector
Environmental statutory compliance 1,1,1 3/2,2,5/2 3/2,2,5/2
5/2,3,7/2 5/2,3,7/2 0.517Zero/low toxicity 2/5,1/2,2/3 1,1,1
3/2,2,5/2 5/2,3,7/2 5/2,3,7/2 0.137Ozone depletion 2/5,1/2,2/3
2/5,1/2,2/3 1,1,1 3/2,2,5/2 3/2,2,5/2 0.219Minimize pollution
2/7,1/3,2/5 2/7,1/3,2/5 2/5,1/2,2/3 1,1,1 3/2,2,5/2 0.68Impact on
air quality 2/7,1/3,2/5 2/7,1/3,2/5 2/5,1/2,2/3 2/5,1/2,2/3 1,1,1
0.60C.I.=0.01, R.I.=1.12, C.R.=0.009
Life cycle cost Purchase cost Disposal cost Maintenance cost
Priority vector
Purchase cost 1,1,1 1,1,2 4,5,6 0.69Disposal cost 1/2,1,1 1,1,1
2,3,4 0.22Maintenance cost 1/6,1/5,1/4 1/4,1/3,1/2 1,1,1
0.09C.I.=0.02, R.I.=0.58, C.R.=0.03
Lifeexp
5/23/23/21,12/3
2/5
121P.O. Akadiri et al. / Automation in Construction 30 (2013)
113125Resource efciency Embodied energy Amount of wastage
Embodied energy 1,1,1 1,1,2Amount of wastage 1/2,1,1 1,1,1Method
of extraction 1/2,1,1 1/2,1,1Impact during harvest 1/2,1,1
1/2,1,1C.I.=0.025, R.I.=0.90, C.R.=0.028
Waste minimization Recycling and reuse
Recycling and reuse 1,1,1Environmentally sound disposal
1/4,1/3,1/2C.I.=0.00, R.I.=0.00, C.R.=0.00
Performancecapability
Fireresistance
Maintainability Resistance todecay
Fire resistance 1,1,1 3/2,2,5/2 2/3,1,3/2Maintainability
2/5,1/2,2/3 1,1,1 2/3,1,3/2Resistance to decay 2/3,1,3/2 2/3,1,3/2
1,1,1Life expectancy 2/7,1/3,2/5 2/5,1/2,2/3 2/5,1/2,2/3Energy
saving andthermal insulation
2/5,1/2,2/3 3/2,2,5/2 3/2,2,5/2
Buildability 2/5,1/2,2/3 2/5,1/2,2/3 3/2,2,5/2C.I.=0.00821,
R.I.=1.24, C.R.=0.007
Social benet Local material Esthetics
Local material 1,1,1 2/3,1,3/2Esthetics 2/3,1,3/2 1,1,1Health
and safety 2/3,1,3/2 2/5,1/2,2/3Material availability 2/5,1/2,2/3
2/5,1/2,2/3C.I.=0.025, R.I.=0.90, C.R.=0.03Method of extraction
Impact during harvest Priority vector
1,1,2 1,1,2 0.2891,1,2 1,1,2 0.4751,1,1 1/4,1/3,1/2 0.0812,3,4
1,1,1 0.155
Environmental sound disposal Priority vector
2,3,4 0.671,1,1 0.33
ectancyEnergy saving and thermalinsulation
Buildability Priorityvector
,3,7/2 3/2,2,5/2 2/3,1,3/2 0.183,2,5/2 2/7,1/3,2/5 3/2,2,5/2
0.258,2,5/2 2/7,1/3,2/5 2/5,1/2,2/3 0.47,1 2/3,1,3/2 7/2,4,9/2
0.204,1,3/2 1,1,1 2/7,1/3,2/5 0.212
,1/2,2/3 2/5,1/2,2/3 1,1,1 0.095
Health and Safety Material availability Priority vector
2/3,1,3/2 2/3,1,3/2 0.1933/2,2,5/2 3/2,2,5/2 0.3681,1,1
3/2,2,5/2 0.3682/5,1/2,2/3 1,1,1 0.070-tt
-
-
s
f
-,-
-
Table 10Priority weights for sustainability criteria and sub
criteria used in the case study.
Variables in level 1 Local weight (1)a Variables in level 2
Local weight (2) Global weight (3)b Variables in level 3 Local
weight (4)
Environmental impact 0.155 Environmental statutory compliance
0.517 0.07962 Material AMaterial BMaterial C
0.0720.6500.278
Zero/low toxicity 0.137 0.02109c Material AMaterial BMaterial
C
0.2000.4000.400
Ozone depletion 0.219 0.03373 Material AMaterial BMaterial C
0.7470.0600.193
Minimize pollution 0.68 0.10472 Material AMaterial BMaterial
C
0.6740.1010.226
Impact on air quality 0.60 0.0924 Material AMaterial BMaterial
C
0.7960.1250.079
Life cycle cost 0.310 Maintenance cost 0.22 0.07062 Material
AMaterial BMaterial C
0.6910.2180.091
Initial cost 0.69 0.22149 Material AMaterial BMaterial C
0.7700.0680.162
Disposal cost 0.09 0.02889 Material AMaterial BMaterial C
0.7310.0810.188
Resource efciency 0.079 Method of raw material extraction 0.081
0.00624 Material AMaterial BMaterial C
0.4000.2000.400
Amount of wastage 0.475 0.03658 Material AMaterial BMaterial
C
0.1260.4160.458
Embodied energy 0.289 0.02225 Material AMaterial BMaterial C
0.6910.0910.218
Environmental impact during harvest 0.155 0.01194 Material
AMaterial BMaterial C
0.7540.1810.065
Performance capability 0.354 Fire resistance 0.183 0.06405
Material AMaterial BMaterial C
0.8040.0740.122
Resistance to decay 0.47 0.1645 Material AMaterial BMaterial
C
0.4720.0840.444
Energy saving and thermal insulation 0.212 0.0742 Material
AMaterial BMaterial C
0.8020.0750.211
Life expectancy 0.204 0.0714 Material AMaterial BMaterial C
0.1840.5840.232
Ease of construction 0.095 0.03325 Material AMaterial BMaterial
C
0.6910.0910.218
Maintainability 0.258 0.0903 Material AMaterial BMaterial C
0.450.090.46
Social benet 0.044 Use of Local material 0.193 0.00791 Material
AMaterial BMaterial C
0.3000.6000.600
Esthetics 0.368 0.01508 Material AMaterial BMaterial C
0.0820.7000.378
Health and safety 0.368 0.01508 Material AMaterial BMaterial
C
0.7700.0580.162
Material availability 0.070 0.00287 Material AMaterial BMaterial
C
0.7700.0500.170
Waste minimization 0.058 Environmental sound disposal option
0.33 0.01881 Material AMaterial BMaterial C
0.690.090.22
Recycling and reuse 0.67 0.03819 Material AMaterial BMaterial
C
0.450.090.46
1.000 1.000a Local weight is derived from judgment with respect
to a single criterion.b Global weight is derived from
multiplication by the priority of the criterion.c This entry is
obtained as follows: 0.1540.137=0.02109. The global weight of the
sub-criterion is obtained by multiplying the local weight of the
sub-criterion by the weight of
the criterion.
122 P.O. Akadiri et al. / Automation in Construction 30 (2013)
113125
-
st
n
n
123P.O. Akadiri et al. / Automation in Construction 30 (2013)
113125malized local priority weights of dimensions of
sustainability and var-ious SC were obtained and were combined
together in order to obtainthe global composite priority weights of
all SC used in the third levelof the AHP model. In order to shorten
the solution process for the ma-terial selection problem, Expert
Choice 11.5 was used to determinethe global priority weights of
material alternatives based on the
Table 11Overall rating of the three assessed roong material
using AHP technique.
Criterion Local weight (1) Sub-criterion
Environmental impact 0.155 Environmental statutory
complianceZero/low toxicityOzone depletionMinimize pollutionImpact
on air quality
Life cycle cost 0.310 Maintenance costInitial costDisposal
cost
Resource efciency 0.079 Method of raw material extractionAmount
of wastageEmbodied energyEnvironmental impact during harve
Performance capability 0.354 Fire resistanceResistance to
decayEnergy saving and thermal insulatioLife expectancyEase of
constructionMaintainability
Social benet 0.044 Use of local materialEstheticsHealth and
safetyMaterial availability
Waste minimization 0.058 Environmental sound disposal
optioRecycling and reuse
Total 1.000questionnaire used to facilitate comparisons of main
attributes, sub-attributes and alternatives. It provides two ways
of synthesizing thelocal priorities of the alternatives using the
global priorities of theirparent criteria: the distributive mode
and the ideal mode. In the dis-tributive mode the weight of
criteria reects the importance that thedecision maker attaches to
the dominance of each alternative relativeto all other alternatives
under that criterion. In this case, the distribu-tive mode would be
the way to synthesize the results. After derivingthe local
priorities for the criteria and the alternatives throughpair-wise
comparisons, the priorities of the criteria are synthesizedto
calculate the overall priorities for the decision alternatives.
Asshown in Table 11, the materials are ranked according to their
overallpriorities. Material option (A) turns out to be the most
preferablematerial among the three materials, with an overall
priority scoreof 0.453.
5. Conclusion
This paper discussed the development of assessment
criteria,computational methods, and analytical models for
sustainable mate-rial selection. The complex tasks of comparing
building materials op-tions based on their sustainability, using
multi-criteria considerationsare regarded as daunting challenges by
many material speciers.Hence developing suitable systematic
approaches and appropriatestructured decision-making frameworks for
sustainable building ma-terial selection was considered in this
research. Decision making for asustainable material alternative,
while considering various criteriathat inuence selection, is
difcult and this difculty is further com-plicated not only when
conicting relationships exist between thecriteria considered, but
also when qualitative criteria are included.To deal with this
difculty effectively, this research proposed a meth-od that focused
on aggregation of criteria that inuence sustainablematerial
selection.
Sustainable assessment criteria (SAC) used in study were
identi-ed based on sustainability triple bottom (TBL) approach,
review ofthe literature in the eld of material selection, combined
with re-
Local weight (2) Local weight (3) Global weight (4)
M (A) M (B) MI M (A) M (B) MI
0.517 0.072 0.650 0.278 0.0057 0.0504 0.07250.137 0.200 0.400
0.400 0.0042 0.0084 0.00840.219 0.747 0.060 0.193 0.0252 0.0020
0.00650.68 0.674 0.101 0.226 0.0306 0.0106 0.02370.60 0.796 0.125
0.079 0.0736 0.0116 0.00730.22 0.691 0.218 0.091 0.0088 0.0054
0.00640.69 0.770 0.068 0.162 0.0705 0.0151 0.03590.09 0.731 0.081
0.188 0.0216 0.0023 0.00540.081 0.400 0.200 0.400 0.0025 0.0012
0.00250.475 0.126 0.416 0.458 0.0046 0.0152 0.01680.289 0.691 0.091
0.218 0.0154 0.0020 0.00490.155 0.754 0.181 0.065 0.0090 0.0027
0.00080.183 0.804 0.074 0.122 0.0515 0.0047 0.00780.47 0.472 0.084
0.444 0.0376 0.0138 0.03300.212 0.802 0.075 0.211 0.0295 0.0056
0.01570.204 0.184 0.584 0.232 0.0131 0.0417 0.01660.095 0.691 0.091
0.218 0.0229 0.0030 0.00720.258 0.45 0.90 0.46 0.0206 0.0313
0.02150.193 0.300 0.600 0.600 0.0024 0.0048 0.00480.368 0.082 0.700
0.378 0.0012 0.0106 0.00570.368 0.770 0.058 0.162 0.0116 0.0009
0.00240.070 0.770 0.050 0.170 0.0022 0.0001 0.00050.33 0.69 0.09
0.22 0.0130 0.0017 0.00410.67 0.45 0.09 0.46 0.0172 0.0034
0.0176Overall priority 0.453 0.249 0.324Rank 1 3 2quirement of
project stakeholders. A questionnaire survey wasemployed to obtain
the perceived importance of the criteria. Follow-ing the results of
the survey, the twenty four criteria identied asbeing important
components of sustainable material selection werefurther compressed
into six assessment criteria factors of environ-mental impact,
resource efciency, waste minimization, life cycle cost,performance
capability and social benet for modeling and evaluat-ing
sustainable performance of building materials. A Fuzzy
extendedanalytical hierarchical process (FEAHP) was used for
aggregatingand assigning numerical values (i.e., weights) to
measure the relativeimportance of these criteria for a given
material alternative. For thispurpose, FEAHP uses simple pairwise
comparisons to determineweights and ratings so that the analyst can
concentrate on just twofactors at one time. This process enables
decision makers to formalizeand effectively solve the complicated,
multi-criteria and vague per-ception problem of building material
alternative selection. An empir-ical case study of three proposed
roong element alternatives for anew building project was used to
exemplify the approach. From theresult of the case study, it can be
concluded that the application ofthe FEAHP in incorporating both
objective and subjective criteriainto the assessment process and
improving the team decision processis desirable. The underlying
concepts applied were intelligible to thedecision maker groups, and
the computation required is straightfor-ward and simple. A
comparison of the result of the traditional deci-sion method and
that of the Fuzzy extended AHP method clearlyshows that qualitative
criteria have a signicant impact on sustain-ability of building
materials. In the case study project, it was discov-ered that some
materials selected by the traditional methods weredropped when the
qualitative criteria were introduced using AHP.However, Fuzzy AHP
is a highly complex methodology and requires
-
124 P.O. Akadiri et al. / Automation in Construction 30 (2013)
113125more numerical calculations in assessing composite priorities
thanthe traditional method and hence it increases the effort. The
fuzzymethodology could also be extended with the other
multi-criteriadecision-making (MCDM) methods such as Analytical
NetworkProcess (ANP), TOPSIS, ELECTRE and DEA techniques in solving
mate-rial selection problems. Finally, the evaluation model and
results canprovide a valuable reference for building professionals
seeking to en-hance the sustainability of construction
projects.
The approach suggested in this research can allow for
assessmentof the other civil infrastructures taking into
consideration a large cri-terion set and an extensive number of
alternatives, especially whereconicting objectives exist. The
authors are convinced that this re-search can assist building
stakeholders in making critical decisionsduring the selection of
sustainable material alternatives.
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Multi-criteria evaluation model for the selection of sustainable
materials for building projects1. Introduction2. Fuzzy analytic
hierarchy process methodology2.1. Background2.2. Establishment of
triangular fuzzy numbers2.3. Calculation of priority weights at
different level of hierarchy
3. Development of sustainable assessment criteria3.1. Importance
of derived criteria3.2. Factor analysis
4. Implementation of the FEAHP selection model4.1. Fuzzy AHP
procedure for the sustainable building materials selection
problem
5. ConclusionReferences