Effective prevention at pre-construction stage
3.4 ISO 14001 EMS adoption (EMS)
Table 3.8Environmental indicators and corresponding values of plan alternatives for the ANP model
Classification Environmental indicators Unit EIi Plan alternatives Plan
A
Plan B
Plan C 1 EA Factors 1.1 Fuel consumption
amount (FCA)
Mjoule −8 36k 45k 49k
1.2 Construction duration (COD)
day −8 500 560 450
1.3 Construction cost (COC)
M$ −8 30 31 29
1.4 Public health and safety risk (PHS)
% −6 10 20 25
1.5 Transportation time (TRT)
hour −5 4.0k 4.5k 4.8k
1.6 Earthquake affection risk (EAR)
% −5 0.01 0.01 0.01
1.7 Electricity consumption amount (ECA)
kWh −4 30k 45k 50k
1.8 Water consumption amount (WCA)
ton −4 3.1k 3.8k 4.1k
1.9 Waste generating rate (WGR)
% −4 1.2 3.0 3.5
1.10 Public traffic disruptions (PTD)
day −4 39 60 70
1.11 Cargo transportation burden (CTB)
ton-mile −4 450k 500k 550k 1.12 Construction delay risk
(CDR)
hour −3 150 200 220
1.13 Temperature affection risk (TAR)
% −3 10.0 8.9 8.7
1.14 Storm affection risk (SAR)
% −3 2.0 1.8 1.8
2 EU Factors 2.1 Constructability (COB) % 0 100 100 100 2.2 Generative material use
ratio (GMU)
% 0 20 10 8
2.3 ISO 9001 QMS
Effective prevention 53
3.5 Cooperativity/Unionization risk (COP)
% +8 100 80 60
3.6 Site layout suitability (SLS)
% +8 80 60 50
3.7 Waste disposal price (WDP)
M$ +8 0.10 0.25 0.29
3.8 Legal and responsibility risk (LRR)
% +8 0.10 0.23 0.32
3.9 Health and safety risk to staff (HSR)
% +4 0.10 0.21 0.28
3.10 Wastewater treatment/
re-use ratio (WTR)
% +3 90 50 40
3.11 Material durability (MAD)
% +3 100 80 80
3.12 Cargo packaging recycling ratio (CPR)
% +3 100 50 0
3.13 Waste re-use and recycling ratio (WRR)
% +2 90 30 35
3.14 Required skills on staff (RSS)
% +2 80 60 60
3.15 Material serviceability (MAS)
% +1 100 80 80
Notes
1 EIivalue equals toEIij(refer to Table 3.7);
2 EA Factors means environmental-adverse factors, EF Factors means environmental-friendly factors, and EU Factors means environmental-uncertainty factors;
3 The corresponding value of plan alternatives is calculated based on relative information and data in each construction plan alternative and no formulas and details have been provided for these calculations in this chapter.
ratio scales that represent relative measurements of the influence of elements that interact with respect to control criteria. The ANP is a coupling of two parts: one is a control hierarchy or network of criteria and subcriteria that con- trol the interactions (interdependencies and feedback), another is a network of influences among the nodes and clusters. Moreover, the control hierarchy is a hierarchy of criteria and subcriteria for which priorities are derived in the usual way with respect to the goal of the system being considered. The crite- ria are used to compare the components of a system, and the subcriteria are used to compare the elements of a component. Steps of the ANP analysis for the environmental-conscious construction planning are laid out from Step A to Step D:
3.3.3.1 Step A: ANP model construction
This step aims to construct an ANP model for evaluation based on determining the control hierarchies such as benefits, costs, opportunities, and risk, as well
54 Effective prevention
as the corresponding criterion for comparing the components (clusters) of the system and sub-criteria for comparing the elements of the system, together with a determination of the clusters with their elements for each control criterion or subcriterion.
The env.Plan model is outlined in Figure 3.10. The decision environment con- sists of external environment and internal environment. In the exterior env.Plan environment, the downward arrow indicates the process of transferring data required by the ANP, the upward arrow indicates the process of feedback with evaluation results from the ANP, and the feedback process (loop) between the external environment and the internal environment indicates a circulating pipe for environmental priority evaluation of alternative construction plans. In the internal env.Plan environment, connections among four clusters and 35 nodes are modelled by two-way and looped arrows to describe the existing interde- pendencies. The four clusters are Plan Alternatives (C1, EA Factors (C2, EU Factors (C3, and EF Factors (C4. In correspondence with the four clusters, there are 35 nodes including 3 nodes in C1 (N11∼3), 14 nodes in C2 (N21∼14), 3 nodes in C3 (N31∼3) and 15 nodes in C4 (N41∼15). Figure 3.10 illustrates the
Figure 3.10The env.Plan ANP environment.
Effective prevention 55 env.Plan model implemented using an ANP with all interior clusters and nodes, and exterior related participators.
Concerning the interdependencies between any two clusters and any two nodes, the env.Plan model structured here is a simple ANP model containing feedback and self-loops among the clusters but with no control structure because there is an implicit control criterion with respect to which all judgements (paired comparisons) are made in this model: environmental impact. For example, when comparing the cluster EA Factors (C2) to cluster EF Factors (C4), the latter is obviously more important for reducing negative environmental impacts, and similarly when the node comparisons are made (see Step B), relative importance of the nodes can be decided in the same way. Table 3.7 provides a list of 32 envi- ronmental indicators used in constructing the ANP model and the corresponding references from which the indicator is retrieved.
3.3.3.2 Step B: Paired comparisons
This step aims to perform pairwise comparisons among the clusters, as well as pairwise comparisons between nodes, as they are interdependent. On completing the pairwise comparisons, the relative importance weight (denoted as aij of interdependence is determined by using a scale of pairwise judgement, where the relative importance weight is valued from 1 to 9 (Saaty 1996). The fundamental scale of pairwise judgement is given in Table 3.9. The weight of interdependence is determined by a human decision-maker who is abreast with professional experience and knowledge in the application area. In this study, it is determined subjectively as the objective of this study is mainly to demonstrate the usefulness of the ANP model in evaluating the potential environmental impact due to the execution of a construction plan.
Weights for all interdependencies for a particular construction plan are then aggregated into a series of submatrices. For example, if the cluster of plan alter- natives includes Plans A, B, and C, and each of the plans is connected to nodes in Table 3.9Pairwise judgements of indicatori
Pairwise judgement 1 2 3 4 5 6 7 8 9
Indicatori Plan A x x x x x x x x
Plan B x x x x x x x x
Plan C x x x x x x x x
IndicatorIi IndicatorIj x x x x x x x x
Notes
1 The symbolxdenotes item under selection for pairwise judgement, and the symboldenotes selected pairwise judgement.
2 Scale of pairwise judgement: 1 equal, 2 equally to moderately dominant, 3 moderately dominant, 4 moderately to strongly dominant, 5 strongly dominant, 6 strongly to very strongly dominant, 7 very strongly dominant, 8 very strongly to extremely dominant, 9 extremely dominant.
56 Effective prevention
Table 3.10Formulation of supermatrix and its submatrix for env.Plan
Supermatrix Submatrix
W=
⎡
⎢⎢
⎢⎣
W11 W12 W13 W14 W21 W22 W23 W24 W31 W32 W33 W34 W41 W42 W43 W44
⎤
⎥⎥
⎥⎦
Cluster C1 C2 C3 C4 Node N11∼3 N21∼14 N31∼3 N41∼15
WIJ=
⎡
⎢⎢
⎢⎢
⎢⎢
⎣
w1 IJ · · · w1 IJ w2 IJ · · · w2 IJ
· · · · wi IJ · · · wi IJ
· · · · wNI
1
IJ · · · wN
In IJ
⎤
⎥⎥
⎥⎥
⎥⎥
⎦
Notes
Iis the index number of rows; andJis the index number of columns; bothIandJ correspond to the number of cluster and their nodesI J∈12 35 NIis the total number of nodes in clusterI nis the total number of columns in cluster I. Thus a 35×35 supermatrix is formed.
the cluster of EF Factors, pairwise judgements of the cluster will result in relative weights of importance between each plan alternative and each EF Factor. The aggre- gation of the weights thus forms a 3×14 submatrix located at “W21” in Table 3.10.
It is necessary to note that pairwise comparisons are necessary to all connections (clusters and nodes) in the ANP model to identify the level of interdependencies which are fundamental in the ANP procedure. The series of submatrices are then aggregated into a supermatrix which is denoted as supermatrixAin this study, and it will be used to derive the initial supermatrix in the later calculation in Step C.
Table 3.9 gives a general form for pairwise judgement among environmental indicators and construction plan alternatives, which is adopted in this study. For example, for the environmental indicator 1.1 Fuel consumption amount (FCA) (EA Factor 1), the pairwise judgements are as given in Table 3.9, as the fuel consumption in Plan A is the least among the three plan alternatives, whilst the fuel consumption in Plan C is the highest; in addition to this judgement in property, quantitative pairwise judgements are also made in order to define plan alternatives’ priorities. After finishing a series of pairwise judgements, from environmental indicator i to environmental indicator n, the calculation of the ANP can thus be conducted following the Step C to the Step D. Besides the pairwise judgement between an environmental indicator and a construction plan, the developed env.Plan model contains all other pairwise judgements between each of the environmental indicators (indicatorIi and indicatorIj in Table 3.9) and this essential initialization is set up based on the quantitative attribute of each plan alternative which has been given in Table 3.8.
3.3.3.3 Step C: Supermatrix calculation
This step aims to form a synthesized supermatrix to allow for the resolution of the effects of the interdependencies that exist between the elements (nodes and
Effective prevention 57 clusters) of the ANP model. The supermatrix of the env.Plan model is a two- dimensional partitioned matrix consisting of 16 submatrices (refer to Table 3.10).
In order to obtain useful information for construction plan selection, the calcu- lation of the supermatrix is to be done following three substeps which transform an initial supermatrix to a weighted supermatrix, and then to a synthesized supermatrix.
At first, an initial supermatrix of the ANP model is created. The initial super- matrix consists of local priority vectors obtained from the pairwise comparisons among clusters and nodes. A local priority vector is an array of weight priorities containing a single column (denoted aswT=w1 w2 wi wn), whose components (denoted aswiare derived from a judgement comparison matrixA and deduced by Equation 3.8 (Saaty 2001).
wi IJ= I i=1
⎛
⎝Jaij
j=1aij
⎞
⎠
J (3.8)
where wi IJ is the weighted/derived priority of node i at row I and column J; aij is a matrix value assigned to the interdependence relationship of nodei to nodej. The initial supermatrix is constructed by substituting the submatrices into the supermatrix as indicated in Table 3.10. A detail of the initial supermatrix is given in Table 3.11.
After the formation of the initial supermatrix, it is transformed into a weighted supermatrix. This process involves multiplying every node in a cluster of the initial supermatrix by the weight of the cluster, which has been established by pairwise comparison among the four clusters. In the weighted supermatrix, each column is stochastic, i.e. sum of the column amounts to 1 (Saaty 2001) (refer to Table 3.12).
The last substep is to compose a limiting supermatrix, which is to raise the weighted supermatrix to powers until it converges/stabilizes, i.e. when all the columns in the supermatrix have the same values. Saaty (1996) indicated that as long as the weighted supermatrix is stochastic, a meaningful limiting result can be obtained for prediction. A limiting supermatrix can be arrived at by taking repeatedly the power of the matrix, i.e. the original weighted supermatrix, its square, its cube, etc., until the limit is attained (converges), in which case all the numbers in each row will become identical. Calculus-type algorithm is employed in the software environment of Super Decisions, designed by Bill Adams and the Creative Decision Foundation, to facilitate the formation of the limiting supermatrix, and the calculation result is listed in Table 3.12.
The formulations of supermatrices and submatrices used in the env.Plan model are illustrated in Table 3.11, and calculation results of the initial supermatrix, the weighted supermatrix, and the limiting supermatrix are given in Tables 3.11 and 3.12. As the limiting supermatrix is set up, the next step is to select a proper plan alternative using results from the limiting supermatrix.
Table3.11Thesupermatrixforthecomplicatedenv.Planmodel:initialsupermatrix Super matrix NodePlan CPlan BPlan AFCACODCOCPHSTRTEARWCAWGRPTDCTBCDRTARSARCOBGMUQMSCTAPCAEMSSLSWDPLRRHSRWTRRSSMAS Plan A 0.591730.67381 0.669420.581550.76116 0.717380.611850.57143 0.700970.707350.658650.661200.657070.690960.742910.772020.733380.248830.406480.59891 0.653080.412610.331940.725740.250000.493390.650030.674910.718810.709050.758250.740740.744460.778580.70735 Plan B 0.333220.22554 0.242640.309000.16623 0.194200.178900.28571 0.192880.170030.156180.271780.196310.217640.186710.173440.199070.156060.260240.12619 0.250690.327490.138790.212210.250000.195800.174430.223850.223350.211840.090510.185180.149900.142820.17003 Plan C 0.075060.10065 0.087950.109450.07261 0.088420.209250.14286 0.106150.122620.185170.067030.146630.091400.070380.054550.067540.595110.333280.27491 0.096230.259900.529260.062050.500000.310810.175530.101240.057830.079110.151250.074070.105640.078600.12262 FCA 0.105340.11897 0.080270.000000.10956 0.066930.058520.08534 0.080510.121420.041870.108480.163340.062900.080180.085530.128530.107170.069760.09477 0.090900.091820.126930.110940.109050.074350.095440.088390.079430.107990.094290.080130.085650.116270.12823 COD 0.085750.09606 0.069230.066140.00000 0.071550.067680.07937 0.090470.098910.081210.134330.114980.114080.078790.067440.102160.123030.070930.06816 0.103670.087080.086690.058870.105060.076300.107060.105230.086400.055560.070380.105520.083860.078240.09811 COC 0.109670.06625 0.087150.095910.10821 0.000000.050060.05355 0.075830.129650.061550.070630.118840.063090.079530.061750.095940.115930.076270.06524 0.079060.115740.069630.086760.074710.120420.085100.096690.068150.115600.087760.066630.068720.170510.07477 PHS 0.058960.08657 0.072030.278220.09669 0.084620.000000.11144 0.080370.147630.069680.039930.113630.039040.070550.043120.056550.073270.083980.08717 0.084760.068180.086470.100970.075800.078330.069130.095190.094870.068830.074140.081940.095100.156730.09756 TRT 0.075900.08339 0.067350.062140.07140 0.071350.073580.00000 0.130440.086270.107380.068860.091800.090520.070670.087380.077080.075820.081280.05877 0.061380.081130.083100.073170.105130.060100.094300.096270.072800.094790.100980.082210.106560.091610.07868 EAR 0.060530.08280 0.059760.000030.05976 0.071500.057910.08777 0.000000.052050.137260.058210.056740.072600.054180.066420.057310.050760.074470.07723 0.072110.064210.055520.056380.070400.081610.047630.074130.082210.069400.089150.077340.046260.077560.05108 ECA 0.055750.05925 0.071080.049190.07821 0.076400.076850.08411 0.076980.000000.039690.055730.051690.078860.148180.090970.068260.049050.080100.06232 0.089280.054900.054640.049900.043800.089700.074230.056970.048820.076080.078640.080770.064740.071020.05837 WCA 0.093420.05505 0.065450.097510.08669 0.067840.090350.09319 0.078810.072120.000000.116840.051950.072310.065870.078320.104900.060090.073010.07579 0.063510.094420.054820.072910.069600.062870.072950.057260.078280.070110.081520.081790.057270.061050.06977 WGR 0.030590.04846 0.065210.128760.06777 0.101280.079910.05533 0.074140.049600.050870.000000.054650.096080.074970.090680.043070.070870.056520.06354 0.055160.088940.062620.073610.081560.085710.096310.066340.050540.054360.051440.038100.052160.053650.05570 PTD 0.061840.06204 0.071640.116270.06849 0.069400.057800.04722 0.094450.057660.042200.061520.000000.053540.071840.136660.094640.064930.070410.07286 0.041930.053520.095050.057130.054220.069410.043720.069280.091030.071180.058150.053640.057670.044010.06551 CTB0.066830.06364 0.092140.000030.06091 0.079600.102980.08735 0.069280.068460.073810.058530.037810.000000.074950.079880.074650.072850.084150.08009 0.084920.055770.056530.047030.037800.045020.076920.057530.077240.059530.065750.070650.096140.025020.05047 CDR 0.062870.04898 0.064760.105760.08005 0.087400.103150.04911 0.079980.052190.089870.060210.045100.064850.000000.064760.054920.043460.065900.06627 0.052380.033810.060260.095370.068650.044200.038960.051170.077660.065780.043690.061120.075150.007750.04076 TAR 0.074240.07131 0.071870.000030.06490 0.084470.075360.09023 0.037230.035650.098980.112970.054140.078150.064870.000000.041980.046440.060520.06369 0.053170.091090.047900.067070.054480.049090.039410.043930.037560.038350.054240.086420.057120.035260.04732 SAR 0.058300.05724 0.062050.000030.04736 0.067650.105870.07599 0.031510.028380.105640.053770.045330.113990.065420.047090.000000.046330.052710.06409 0.067780.019400.059840.049890.049740.062900.058830.041630.055010.052430.049870.033740.053610.011330.08368 COB0.146630.65481 0.327490.333330.25992 0.284880.576910.59567 0.644220.634840.510200.307690.210380.636990.237710.459950.549810.000000.666670.83333 0.211840.678160.428570.690960.433060.075580.358430.093620.281500.268370.549810.217640.391460.650650.27056 GMU 0.196310.09534 0.259900.333330.41260 0.217390.081100.30848 0.085220.077960.333810.076920.423850.104730.607190.221130.368060.333330.000000.16667 0.079110.142420.142860.091400.100500.695210.308850.624110.174940.117220.082130.690960.278410.126830.64422 QMS 0.657070.24986 0.412610.333330.32748 0.497730.342000.09585 0.270560.287200.156000.615390.365770.258280.155100.318920.082130.666670.333330.00000 0.709050.179420.428570.217640.466440.229220.332720.282270.543550.614410.368060.091400.330130.222520.08522 CTA 0.069780.13581 0.097470.085410.12058 0.073960.072000.05038 0.069350.090110.087480.070740.051650.083250.174830.079790.050760.089720.094660.08421 0.000000.067910.097260.079050.079950.083980.095850.075520.117890.084790.115620.072460.074930.089470.05433 PCA 0.067650.10166 0.068980.072130.10517 0.074330.068630.07873 0.113420.055640.081120.064530.064740.060520.071220.067460.068900.091500.081750.04338 0.081620.000000.077060.057640.100590.085130.132920.082690.085430.107800.095120.075260.107190.104020.07714 ECC 0.045280.07058 0.072800.066530.08054 0.068980.060850.05897 0.115260.069280.081420.068530.071470.070390.137980.052090.063170.096520.077320.08372 0.089850.124820.000000.100930.094520.061090.074330.069400.066060.084570.100030.072610.102680.102180.08153 EMS 0.073030.09716 0.047550.076440.07295 0.061550.079300.06325 0.065950.079340.069520.056220.065390.062420.112920.059240.099400.084950.078070.05746 0.061970.053880.081360.000000.067720.072170.059600.039930.074160.110410.086780.071070.069240.079540.07248 COP 0.073250.05540 0.089610.070720.07120 0.083420.054280.07395 0.051020.075490.095500.097230.055470.065350.097210.083510.052950.063800.072410.10434 0.056130.075410.060790.074370.000000.091360.114580.077630.089080.100880.065980.069650.091440.077240.04398 SLS 0.072730.09930 0.079440.050450.04997 0.045300.098400.05725 0.054020.078480.061710.073280.091600.072190.084420.098740.083000.064770.052900.08149 0.068850.090600.075020.085430.055010.000000.083880.100750.092650.075540.096170.087620.056890.079700.06888 WDP 0.067010.04032 0.039820.083860.05507 0.024910.068180.07712 0.081930.046840.108620.077930.078880.087960.060490.082560.068580.060050.038840.07142 0.072380.052820.053020.040190.076740.065760.000000.046350.059990.086800.056770.093580.060930.080330.08480 LRR 0.050200.04022 0.072420.030470.06676 0.079380.044080.05815 0.067660.037720.028360.065580.054550.081470.034340.056740.066520.066690.065380.06280 0.052170.076900.045720.083600.089200.084400.050260.000000.104950.066050.065530.058510.055350.082280.07084 HSR 0.090170.07464 0.086310.042930.05494 0.077250.074690.05417 0.065950.069570.065160.047500.051510.088440.023920.076610.060370.047450.061020.05742 0.076790.094560.071960.056250.065530.068440.052540.126120.000000.076280.077450.046340.059960.047190.06779 WTR 0.049070.03931 0.050850.055520.06893 0.043670.058820.08600 0.047910.056780.025350.049180.082390.053090.022900.067160.091080.055670.048480.04056 0.089480.076010.084360.067500.082230.058800.054840.093330.038970.000000.064450.101720.077370.061650.08999 MAD 0.088510.03819 0.078710.068370.06154 0.075900.061500.08811 0.065000.068160.080730.067910.072430.038510.034290.055930.074470.058490.064840.07627 0.064750.038500.090500.057270.054640.063950.041920.078550.073800.076040.000000.086340.100360.051050.05797 CPR 0.075070.03948 0.025640.079720.05084 0.057270.039130.05334 0.057220.059400.020410.051370.081640.065580.026480.062480.048960.065640.059720.06524 0.083170.077620.077520.101300.058180.077100.087360.058610.041360.027140.050390.000000.053950.046000.05540 WRR 0.069550.06796 0.074460.081910.04750 0.076150.073720.07670 0.059710.108700.081190.063940.078290.043810.052780.047680.065320.072410.064960.06964 0.072780.052890.094200.052690.054670.079670.035960.050470.070560.039300.043600.069860.000000.046990.06954 RSS 0.051140.03281 0.046090.078770.04541 0.090090.049650.05091 0.050220.031300.097310.097450.053230.052400.034550.065090.061750.043630.060130.05468 0.065230.061270.030050.068010.057920.062250.070390.034550.043720.064410.042550.034050.052940.000000.07032 Initial supermatrix
Plans EA factors EU
factors EF factors
MAS 0.057560.06716 0.069840.056770.04858 0.067820.096770.07297 0.035390.073200.016130.048600.046750.074630.031660.044930.044770.038710.079540.04738 0.064820.056820.061180.075770.063110.045890.045600.066110.041400.000000.039560.060920.036780.052350.03500
ClusterPlan alternativesEA factorsEU factorsEF factors WRRCPRMADCOPECCECA
Table3.12Thesupermatrixforthecomplicatedenv.Planmodel:weightedandlimitingsupermatrices Super matrixClusterPlan alternativesEA factorsEU factorsEF factors NodePlan APlan BPlan CFCACODCOCPHSTRTEARECAWCAWGRPTDCTBCDRTARSARCOBGMUQMSCTAPCAECCEMSSLSWDPLRRHSRWTRMADCPRRSSMAS Plan A0.14793 0.168450.167350.145390.190290.179340.15296 0.142860.175240.176840.164660.165300.164270.172740.185730.193000.183350.062210.101620.149730.011960.007550.006080.013290.004580.009030.011900.012360.013160.012980.013880.013560.013630.014250.01295 Plan B 0.08330 0.056380.060660.077250.041560.048550.04473 0.071430.048220.042510.039050.067940.049080.054410.046680.043360.049770.039020.065060.031550.004590.006000.002540.003890.004580.003580.003190.004100.004090.003880.001660.003390.002740.002610.00311 Plans
Plan C0.01876 0.025160.021990.027360.018150.022110.05231 0.035710.026540.030650.046290.016760.036660.022850.017600.013640.016890.148780.083320.068730.001760.004760.009690.001140.009150.005690.003210.001850.001060.001450.002770.001360.001930.001440.00225 FCA 0.02634 0.029740.020070.000000.027390.016730.01463 0.021340.020130.030350.010470.027120.040840.015720.020050.021380.032130.026790.017440.023690.057350.057930.080080.069990.068800.046910.060210.055760.050110.068130.059490.050550.054040.073350.08090 COD 0.02144 0.024010.017310.016530.000000.017890.01692 0.019840.022620.024730.020300.033580.028750.028520.019700.016860.025540.030760.017730.017040.065400.054940.054690.037140.066280.048130.067540.066390.054500.035050.044400.066570.052910.049360.06190 COC 0.02742 0.016560.021790.023980.027050.000000.01251 0.013390.018960.032410.015390.017660.029710.015770.019880.015440.023980.028980.019070.016310.049880.073010.043930.054730.047130.075970.053690.061000.043000.072930.055370.042040.043350.107570.04717 PHS 0.01474 0.021640.018010.069560.024170.021160.00000 0.027860.020090.036910.017420.009980.028410.009760.017640.010780.014140.018320.020990.021790.053470.043010.054550.063700.047820.049420.043610.060050.059850.043420.046780.051700.060000.098880.06155 TRT 0.01897 0.020850.016840.015540.017850.017840.01840 0.000000.032610.021570.026850.017220.022950.022630.017670.021850.019270.018950.020320.014690.038730.051190.052430.046160.066330.037910.059490.060730.045930.059800.063700.051860.067220.057790.04963 EAR 0.01513 0.020700.014940.000010.014940.017880.01448 0.021940.000000.013010.034310.014550.014180.018150.013550.016610.014330.012690.018620.019310.045490.040510.035030.035570.044420.051480.030050.046770.051860.043780.056240.048790.029190.048930.03222 ECA 0.01394 0.014810.017770.012300.019550.019100.01921 0.021030.019250.000000.009920.013930.012920.019720.037050.022740.017070.012260.020030.015580.056330.034630.034470.031480.027630.056590.046830.035940.030800.048000.049610.050950.040840.044800.03682 WCA 0.02335 0.013760.016360.024380.021670.016960.02259 0.023300.019700.018030.000000.029210.012990.018080.016470.019580.026220.015020.018250.018950.040070.059570.034580.046000.043910.039660.046020.036120.049390.044230.051430.051600.036130.038520.04401 WGR 0.00765 0.012120.016300.032190.016940.025320.01998 0.013830.018530.012400.012720.000000.013660.024020.018740.022670.010770.017720.014130.015890.034800.056110.039500.046440.051450.054070.060760.041850.031880.034300.032450.024040.032900.033850.03514 PTD 0.01546 0.015510.017910.029070.017120.017350.01445 0.011810.023610.014420.010550.015380.000000.013390.017960.034170.023660.016230.017600.018220.026450.033770.059960.036040.034210.043790.027580.043700.057430.044910.036680.033840.036380.027770.04133 CTB 0.01671 0.015910.023040.000010.015230.019900.02575 0.021840.017320.017110.018450.014630.009450.000000.018740.019970.018660.018210.021040.020020.053570.035180.035660.029670.023850.028410.048530.036290.048730.037560.041480.044570.060650.015780.03184 CDR 0.01572 0.012240.016190.026440.020010.021850.02579 0.012280.020000.013050.022470.015050.011280.016210.000000.016190.013730.010870.016470.016570.033040.021330.038020.060170.043310.027890.024580.032280.048990.041500.027570.038560.047410.004890.02571 TAR 0.01856 0.017830.017970.000010.016220.021120.01884 0.022560.009310.008910.024750.028240.013530.019540.016220.000000.010500.011610.015130.015920.033540.057460.030220.042310.034370.030970.024860.027710.023690.024200.034220.054520.036030.022240.02986 EA factors
SAR 0.01458 0.014310.015510.000010.011840.016910.02647 0.019000.007880.007100.026410.013440.011330.028500.016350.011770.000000.011580.013180.016020.042760.012240.037750.031470.031380.039680.037110.026260.034710.033080.031460.021290.033820.007150.05279 COB0.03666 0.163700.081870.083330.064980.071220.14423 0.148920.161060.158710.127550.076920.052600.159250.059430.114990.137450.000000.166670.208330.011460.036680.023180.037370.023420.004090.019380.005060.015230.014510.029740.011770.021170.035190.01463 GMU 0.04908 0.023830.064980.083330.103150.054350.02027 0.077120.021310.019490.083450.019230.105960.026180.151800.055280.092020.083330.000000.041670.004280.007700.007730.004940.005430.037600.016700.033750.009460.006340.004440.037370.015060.006860.03484 EU
factors QMS 0.16427 0.062460.103150.083330.081870.124430.08550 0.023960.067640.071800.039000.153850.091440.064570.038770.079730.020530.166670.083330.000000.038350.009700.023180.011770.025230.012400.018000.015270.029400.033230.019910.004940.017850.012040.00461 CTA 0.01744 0.033950.024370.021350.030150.018490.01800 0.012590.017340.022530.021870.017690.012910.020810.043710.019950.012690.022430.023660.021050.000000.020150.028860.023460.023720.024920.028440.022410.034980.025160.034310.021500.022230.026550.01612 PCA 0.01691 0.025420.017240.018030.026290.018580.01716 0.019680.028360.013910.020280.016130.016190.015130.017810.016860.017220.022880.020440.010840.024220.000000.022870.017100.029850.025260.039440.024540.025350.031990.028230.022330.031810.030870.02289 ECC 0.01132 0.017640.018200.016630.020130.017240.01521 0.014740.028810.017320.020350.017130.017870.017600.034500.013020.015790.024130.019330.020930.026660.037040.000000.029950.028050.018130.022060.020590.019600.025090.029680.021550.030470.030320.02419 EMS 0.01826 0.024290.011890.019110.018240.015390.01982 0.015810.016490.019840.017380.014060.016350.015600.028230.014810.024850.021240.019520.014360.018390.015990.024140.000000.020100.021420.017680.011850.022010.032760.025750.021090.020550.023600.02151 COP 0.01831 0.013850.022400.017680.017800.020850.01357 0.018490.012760.018870.023880.024310.013870.016340.024300.020880.013240.015950.018100.026090.016660.022380.018040.022070.000000.027110.034000.023030.026430.029930.019580.020670.027130.022920.01305 SLS 0.01818 0.024820.019860.012610.012490.011330.02460 0.014310.013510.019620.015430.018320.022900.018050.021100.024690.020750.016190.013220.020370.020430.026880.022260.025350.016320.000000.024890.029900.027490.022420.028540.026000.016880.023650.02044 WDP 0.01675 0.010080.009950.020970.013770.006230.01705 0.019280.020480.011710.027150.019480.019720.021990.015120.020640.017140.015010.009710.017860.021480.015670.015730.011930.022770.019510.000000.013750.017800.025760.016850.027770.018080.023840.02516 LRR 0.01255 0.010060.018110.007620.016690.019850.01102 0.014540.016910.009430.007090.016400.013640.020370.008590.014190.016630.016670.016340.015700.015480.022820.013570.024810.026470.025050.014910.000000.031140.019600.019450.017360.016420.024410.02102 HSR 0.02254 0.018660.021580.010730.013730.019310.01867 0.013540.016490.017390.016290.011880.012880.022110.005980.019150.015090.011860.015250.014360.022790.028060.021350.016690.019450.020310.015590.037430.000000.022630.022980.013750.017790.014000.02012 WTR 0.01227 0.009830.012710.013880.017230.010920.01471 0.021500.011980.014190.006340.012300.020600.013270.005720.016790.022770.013920.012120.010140.026550.022550.025030.020030.024400.017450.016270.027690.011560.000000.019130.030190.022960.018290.02670 MAD 0.02213 0.009550.019680.017090.015380.018980.01538 0.022030.016250.017040.020180.016980.018110.009630.008570.013980.018620.014620.016210.019070.019210.011420.026850.016990.016210.018980.012440.023310.021900.022560.000000.025620.029780.015150.01720 CPR 0.01877 0.009870.006410.019930.012710.014320.00978 0.013340.014300.014850.005100.012840.020410.016400.006620.015620.012240.016410.014930.016310.024680.023030.023000.030060.017260.022880.025920.017390.012270.008050.014950.000000.016010.013650.01644 WRR 0.01739 0.016990.018620.020480.011880.019040.01843 0.019170.014930.027170.020300.015980.019570.010950.013200.011920.016330.018100.016240.017410.021600.015700.027950.015640.016220.023640.010670.014980.020940.011660.012940.020730.000000.013950.02064 RSS 0.01278 0.008200.011520.019690.011350.022520.01241 0.012730.012550.007820.024330.024360.013310.013100.008640.016270.015440.010910.015030.013670.019360.018180.008920.020180.017190.018470.020890.010250.012970.019110.012630.010100.015710.000000.02087
Weighted supermatrix
EF factors
MAS 0.01439 0.016790.017460.014190.012150.016960.02419 0.018240.008850.018300.004030.012150.011690.018660.007920.011230.011190.009680.019890.011840.019230.016860.018160.022480.018730.013620.013530.019620.012290.000000.011740.018080.010910.015540.01039 Limiting supermatrix0.11231 0.035430.032960.030010.030510.02979 0.027790.022710.022800.025510.022220.022710.022680.020910.020480.018790.080420.045200.073000.021960.020190.019910.018590.019420.018990.016580.015620.017250.015130.017490.015460.017210.014160.01428TiN
COPWRR 0.04149 Note Nistandsforanyofthe35nodesinvolvedinthefourclustersincludingPlanalternatives,EAfactors,EUfactors,andEFfactors.
60 Effective prevention 3.3.3.4 Step D: Selection
This step aims to select the best construction plan based on the computation results of the limiting supermatrix of the ANP model. Main results of the ANP model computations are the overall priorities of construction plans obtained by synthesizing the priorities of individual construction plans against different environmental indicators. The selection of the best construction plan, which has the highest environmental priority, can be done using a limiting priority weight, which is defined in Equation 3.9.
Wi=wCPlani
wCPlan=wCPlani
wCPlan1+ · · · +wCPlann (3.9) where Wi is the synthesized priority weight of plan alternative ii=1 n (n is the total number of plan alternatives, n= 3 in this study), and wCPlani is the limited weight of plan alternative i in the limiting supermatrix. Because the wCPlani is transformed from pairwise judgements conducted in Step B, it is reasonable to regard it as the priority of the plan alternative i and thus to be used in Equation 3.9. According to the computation results in the limit- ing supermatrix in Table 3.12, wCPlani=011231004149003543, soWi= 059351021926018723; as a result, the best environmental-conscious con- struction plan is Plan A.
In addition to the complicated env.Plan model developed in Figure 3.10, another ANP model, called simplified env.Plan model for alternative construc- tion plan selection, was developed with 15 nodes selected from the total 35 nodes of the complicated env.Plan model in Figure 3.10. In order to decrease the number of elements in a supermatrix of the simplified env.Plan model, similar subcomponents of EF Factors are combined, including a combination of sub- components 3.1 and 3.2 for environment-friendly construction and management technology (Technology) and a combination of subcomponents 3.3 and 3.4 for environmental control cost (ECC). Finally, the nodes for the simplified env.Plan model include FCA, COD, and COC in EA Factors cluster; COB, GMU, and QMS in the EU Factors cluster; CTA+PCAECC+EMS, COP, SLS, WDP, and LRR in the EF Factors cluster; and Plan A, Plan B, and Plan C in the Plan Alternatives cluster. The rule for selecting nodes for the EA Factors cluster and the EF Factors cluster of the simplified env.Plan model is whether the absolute value of EI is 8. In other words, all factors with a EI value of−8 go to EA cluster, and all factors with a EI of +8 go to EF cluster; all other factors are therefore ignored for the simplified env.Plan model. According to the compu- tation results in the synthesized supermatrix for the simplified env.Plan model, wCPlani=011024300361080042977, soWi=058229019072022700, so Plan A is also selected.
Interestingly, both complicated env.Plan model and simplified env.Plan model led to the same conclusion that Plan A is the best environmental-conscious construction plan. Besides the selected plan, it is also noticed that priority queues of these plan alternatives are also equivalent (refer to Table 3.13). Considering the load of performing pairwise comparisons on the clusters and nodes would be
Effective prevention 61 Table 3.13A comparison between the two env.Plan models using priority weight ANP model No. of nodes Synthesized priority weightWi Selected plan
Plan A Plan B Plan C
Simplified model 15 058229 019072 022700 Plan A
Complicated model 35 059351 021926 018723 Plan A
multiplied many times in a complicated env.Plan model, the simplified env.Plan model appears to be more practicable and efficient.
According to the attributes of plan alternatives listed in Table 3.8, the compar- ison results usingWialso imply that the most preferable plan for environmental- conscious construction is the plan that regulates the construction practice with least consumption on fuel and water, a lowest ratio of wastage, and a maximum ratio of recycle and re-use on materials and packaging, etc. This indicates the env.Plan method can provide a quite reasonable comparison result for environmental- conscious construction and thus can be applied into construction practice.
3.3.4 Recommendations
In summary, in order to apply the env.Plan model in practice, the following steps are recommended:
1. selection of an ANP model between the simplified env.Plan model and the complicated env.Plan model;
2. original assessment of plan alternatives based on all environmental indica- tors, using Table 3.8;
3. pairwise comparisons among all environmental indicators using Table 3.9;
4. supermatrix calculation following the three substeps to transform an initial supermatrix to a limiting supermatrix with reference to Tables 3.11 and 3.12;
5. calculation of limiting priority weight of each plan alternative using limiting supermatrix and decision-making on plan alternatives using Table 3.13;
6. if none of the plan alternatives meets environmental requirements, adjust- ments to the plans are needed and re-evaluation of the plans by repeating the procedure from step 2.