Advanced automation or conventional construction process?

Ž .Automation in Construction 7 1998 299–314

Advanced automation or conventional construction process?

Makarand Hastak )

Department of CiÕil and EnÕironmental Engineering, Polytechnic UniÕersity, Six Metrotech Center, Brooklyn, NY 11201, USA

Abstract

The rapidly developing area of construction automation leads construction managers to critically evaluate the feasibilityof replacing conventional construction processes by automated systems. This decision requires careful analysis of tangibleand intangible factors such as need-based criteria, economic criteria, technological criteria, project specific criteria, and

Ž .safetyrrisk criteria. This paper presents a decision making model and a decision support system DSS to assist constructionmanagers in systematically evaluating whether to opt for a conventional construction process or an automated system for a

Ž .given project. The proposed DSS, called AUTOCOP AUTomation Option evaluation for COnstruction P rocesses ,Ž .utilizes the Analytical Hierarchy Method AHP to analyze the tangible and the intangible set of criteria involved in the

decision problem. q 1998 Elsevier Science B.V. All rights reserved.

Keywords: Decision support system; Process selection; Construction automation; Decision support model; Decision analysis; Groupdecision

1. Introduction

With the availability of automated systems forvarious construction operations, the decision tochoose between conventional or automated systemplays a significant role in construction project plan-ning. Some examples of situations where advancedtechnologies are evaluated to potentially replace con-ventional procedures include open-cut trench vs. mi-crotunneling, hand troweling vs. automated trowel-ing, manual bridge painting vs. automated painting,or conventional construction vs. modularization. Asevery construction project is unique, it is necessaryto evaluate the feasibility of replacing a conventionalconstruction process with an automated system on aproject to project basis.

) Corresponding author. Tel.: q1-718-260-3989; fax: q1-718-260-3433; e-mail: [email protected].

This paper presents a decision support model forautomation option evaluation for construction pro-cesses and its implementation in a decision supportsystem called AUTOCOP. The decision supportmodel and AUTOCOP have been designed to assistconstruction managers in systematically evaluating

Žthe two options i.e., conventional vs. automated.system with respect to five groups of criteria. The

various aspects that should be considered under eachgroup of criteria have been explored and a decisionmodel is proposed to determine the suitability of anautomated system for a particular construction pro-ject. The decision algorithm behind AUTOCOP hasbeen explained with examples.

In process selection, decision makers have toconsider the various project implications and bene-fitsrdrawbacks of one process vs. another. Manytangible and intangible criteria are considered whileevaluating these options and to arrive at a justifiable

0926-5805r98r$19.00 q 1998 Elsevier Science B.V. All rights reserved.Ž .PII S0926-5805 98 00047-8

( )M. HastakrAutomation in Construction 7 1998 299–314300

decision. This decision process is handled on a rou-tine basis on construction projects, however, compli-cations arise when the alternative processes underconsideration are new and not enough data is avail-able to effectively evaluate all pros and cons. Thelack of sufficient historical data constrains a decisionmaker to carefully analyze the tangible and intangi-ble impact of the advanced technology on projectperformance.

While the clearly identifiable tangible conse-quences such as initial investment, operating costs,productivity improvement, and savings in labor costare relatively easy to evaluate, the evaluation ofintangible criteria however poses a difficulty. Intan-gible criteria such as need, technological require-ments, project specific requirements, and safetyrriskhave a considerable impact on the tangible criteriaand thus one or the other can not be considered inisolation. These groups of criteria include varioussubcriteria such as labor intensiveness, repetitive-ness, critical to productivity, quality requirement,labor savings, health hazards, and physical hazards.There is a high possibility of arriving at a suboptimalsolution without a methodical approach for analyzingthe multi-attributes involved. To encompass the sig-nificance of all relevant criteria and subcriteria, anappropriate decision support model is required. Themodel should utilize a systematic approach to ac-count for the available data and the preference of onecriterion over another. It is anticipated that a multi-attribute decision model that analyzes the impact ofalternative processes on project performance mightbe useful for decision makers with limited experi-ence in process selection. A systematic approachwould be particularly useful when evaluating ad-vanced automated processes.

A decision issue of this nature has been defined asa semi-structured problem: there is a structured ele-ment of collecting and evaluating the data withrespect to productivity improvement, cost etc., andthere is an unstructured element which requires sub-jective input from the primary decision maker inevaluating the preference among criteria, subcriteria,

w xand the alternatives involved 1–4 . Considerablethought and analysis are required before arriving at adecision as the outcome of this decision is likely tohave a significant impact on the strategic planning ofthe project as well as for the organization on the

long-term. Additionally, the decision support modelto be developed for addressing this problem shouldalso allow sensitivity analysis of the selections made.This paper presents such a model and its implemen-tation in a decision support system called AUTO-

Ž .COP. Decision support systems DSS are computer-ized tools that assist managers in improving theeffectiveness of a decision-making process, particu-

w xlarly in semi-structured tasks 4,5 .

2. Decision criteria

The proposed decision support model takes intoaccount five important criteria and also the varioussubcriteria associated with them. The five criteriaare: need-based criteria, technological criteria, eco-nomic criteria, project specific criteria, and

Ž .safetyrrisk criteria. Factors subcriteria that are mostlikely to govern the decision between automationand conventional process have been grouped under

Ž .these five criteria refer to Fig. 1 . The list of criteriaand subcriteria is not an all inclusive list, but arepresentative and generic sample of factors thathold importance in various process selection prob-lems. The list of criteria and subcriteria was gener-

w xated from available literature 3,6–16 . The criteriaand subcriteria are arranged in a hierarchy to estab-lish their interdependencies and facilitate their analy-

Ž .sis through the Analytical Hierarchy Process AHP .w xSkibniewski and Chao 6 have also analyzed the

decision problem of selecting conventional processvs. advanced construction technology utilizing theAHP. They have considered two criteria namely,cost factors and benefit factors. Although these twofactors are important, other factors such as need-based criteria, safetyrrisk criteria, technological cri-teria, and project specific criteria are equally impor-tant and should be considered for a comprehensive

Ž .analysis of the decision problem refer to Fig. 1 . Inorder to maintain sensitivity to change in weights,AHP does not require the hierarchy to be exhaustive.It does, however, require the hierarchy to includesufficient details for the results to be significant andresponsive to all the requirements for the decisionw x17 .

w xKangari and Halpin 7 have identified the impor-tance of need-based criteria, technological criteria

( )M. HastakrAutomation in Construction 7 1998 299–314 301

Fig. 1. Sample hierarchy for analyzing the decision problem.

and economic criteria for potential robotics utiliza-tion in construction. In addition to these criteria, theyhave also identified various subcriteria, identifiedafter consulting with several contractors in the At-lanta and Washington areas, that should be consid-ered in evaluating a roboticsrautomation option. The

w xcriteria identified by Kangari and Halpin 7 did nothowever include the safetyrrisk related issues suchas investment risk, operating hazards, performancereliability, and health hazards. Additionally, projectspecific issues such as site constraints, social con-straints, and constructability constraints were notconsidered in sufficient detail. In the present model,safetyrrisk criteria and project specific criteria havebeen included in the decision process in addition to

w xthe criteria identified by Kangari and Halpin 7 asw x Ž .well as Skibniewski and Chao 6 refer to Fig. 1 .

As shown in Fig. 1, process characteristics definethe need for adopting automation over a conven-tional method, particularly if a process is labor inten-sive such as floor tiling, pipe laying, and painting.Also, if high skill, dexterity, precision, as well ashigh productivity are key requirements, process au-tomation is desirable over conventional process to

reduce the possibility of human error. Additionalergonomic and process related factors, such aswhether the activity is tedious and boring, repetitive,andror unpleasant and dirty, also governs the deci-sion to select automation over conventional methodsw x7–10,12 . The hierarchy of Fig. 1 shows a linkagebetween all the subcriteria and the available alterna-tives to illustrate that the relevance of each subcrite-rion in the hierarchy should be evaluated with re-

Žspect to each alternative i.e., automation and con-.ventional for a given situation.

ŽOften, material handling e.g., in hazardous waste.cleanup and precision work such as in grading and

troweling work, requires technological advancementto effectively perform the job. Additionally, pro-cesses that require quality and high production suchas in painting and in underground pipe rehabilitationwork also support development and use of advanced

w xautomation 8,11,13–16 .Economic criteria that influence the decision be-

tween automation and conventional process includeproductivity and quality improvement, savings inlabor cost, initial investment, operating costs, andoverall savings in project cost. Additionally,


safetyrrisk criteria should also be considered such asŽinvestment risk, operating hazards e.g., impact of an

.open-cut trench on the adjoining structures , equip-ment reliability, performance reliability, health haz-ards, and physical hazards. The effects of investmentin technology for a company have been considered

Žunder two subcriteria namely initial investment un-. Žder economic criteria and investment risk under

.safetyrrisk criteria . Certain project specific criteriaalso govern the process selection decision such as

Žsite constraints e.g., hazardous waste cleanup, ac-.cessibility, and space constraints , social constraints

Že.g., business disruption and social inconvenience.during utility maintenancerrehabilitation , scheduleŽconstraints, and constructability constraints e.g., sit-

uations where preassembly, prefabrication, andror.modularization are some of the considerations .

3. Decision framework

AUTOCOP, the proposed decision support systemfor automation option evaluation for constructionprocesses, includes two models: an analytical model

Ž .and a group decision model refer to Fig. 2 . Theobjective of the analytical model is to assist the userin evaluating the decision problem through hierar-

chic assessment of the various criteria and subcrite-ria. The input required for the assessment is obtainedfrom the user. It might sometimes be important, in adecision problem of this nature, to collect and syn-thesize the opinion of other team members and ex-perts who are familiar with the various aspects of theproject and are in a position to evaluate the benefitsand drawbacks of replacing a conventional construc-tion process by an automated process. The groupdecision model was developed to assist the primary

Ž .decision maker PDM in collecting and evaluatingthese opinions. This model assists the user in synthe-sizing the information obtained from other teammembers into a group decision that is then used inthe analytical model to process the various criteriaand subcriteria. The group decision model has beenexplained in more detail later in this paper. Bothmodels in this decision framework utilize Analytical

Ž .Hierarchy Process AHP to determine the prefer-ence among various criteria, subcriteria, and alterna-tives.

The group decision aspect is particularly impor-tant for this research because of the subjectivityinvolved in analyzing the intangible as well as thetangible criteria that is more accentuated due to thelack of adequate historical information for a newtechnology with respect to performance and effec-tiveness. Even though the tangible criteria such as

Fig. 2. Framework of the DSS.


cost, productivity, and savings in labor cost can beevaluated for each option separately prior to compar-ing them in the decision framework, the amount ofinformation available would vary from older to newertechnology. Therefore, it is important to have a teamconsensus in comparing the two options and also toperform a more responsive analysis of the decisionproblem.

( )4. Analytical hierarchy process AHP

Ž .The Analytical Hierarchy Process AHP hasfound a wide application in various decision prob-lems such as conflict resolution, technological prob-lems, and economicrmanagement problemsw x18,19,12 . This evaluation process has been definedas a theory of measurement with a capacity to handleboth tangible and intangible sets of criteria. Theprinciple behind AHP is that in decision making, theuse of both data and experiencerknowledge play anequally important role. AHP allows the user to estab-lish criteria for decision making in a hierarchy andanalyzes the complex decision problem by incorpo-rating the user’s knowledge based preference. Thehierarchy is arranged in a descending order from theoverall focus to criteria, subcriteria, and alternativesŽ .refer to Fig. 1 .

The importance of establishing a hierarchy in adecision problem is to properly account for the vari-ous factors involved in the decision making processand to establish their interdependencies. Although itis important that a hierarchy should represent allmajor criteria and subcriteria, it is not necessary for

the hierarchy to be exhaustive. Furthermore, it is notnecessary that all criteria in one level be related to

w xall the subcriteria in the next lower level 20 .This hierarchy is then systematically evaluated by

using the AHP to determine the decision maker’spreference order among the various alternatives. Theprocess of determining the preference order amongthe alternatives is based on matrix computations andinvolves pairwise comparison of the various criteriaŽ . Ž .refer to Fig. 3 . A matrix As a where i, jsi j

1, . . . , n, is established for evaluation of criteria andeach criterion, a , is compared with another crite-i

rion, a . The importance of one criterion over thej

other is established by utilizing a predeterminedŽ .scale refer to Table 1 , thus defining a sw rw ,i j i j

Ž .where w rw signifies the importance or weight ofi j

criterion a over criterion a and i, js1, . . . , n. Alli j

entries in this matrix are positive and by definitionsatisfy the condition for reciprocal matrix, a s1raji i jw x17 . For example, as shown in Fig. 4, LEVEL AŽ .Criteria matrix, the evaluation of element a s414

by the primary decision maker signifies strong im-Ž .portance of ‘Technical Knowledge A1 ’ over

Ž .‘Knowledge about the firm A4 ’ while evaluating ateam member.

It is important to use a relative scale that has beenpredetermined rather than using a standard scalebecause of the intangible nature of the criteria in-volved. A standard scale such as a temperature scaleor a distance scale is not unique and thus requiresinterpretation to understand the significance of themeasurements taken. A relative scale on the otherhand is unique for a specific situation and is particu-larly useful in subjective measurements where the

Fig. 3. Sample comparison matrix.


Table 1Ž w x.Comparison scale source, Ref. 17

Degree of importance Definition

1 Equal importance of elements3 Weak importance of one element over another5 Strong importance of one element over another7 Demonstrated or very strong importance of one element over another9 Absolute importance of one element over another2,4,6,8 Intermediate values between two adjacent degrees of importance

purpose is to establish priority or importance amongw xcriteria 20 . The scale shown in Table 1 is a relative

w xscale and was defined by Saaty 17,20,21 to map aset of subjective assessments with numbers. In theabsence of a predetermined scale, it is likely thatsubjective measurements are erroneously used in de-cision making without taking into account the differ-ence in scales. For example, consider two differentscales used to compare three criteria A, B, and C,where scale-1 interprets strong importance of criteriaA over criteria B as 50 while scale-2 interpretsstrong importance of B over C as 5. This set ofsubjective measurements based on different scales, ifnormalized, appear to lie within the same intervalŽ .0,1 . However, the use of these normalized mea-surements in a decision making situation would cer-tainly lead to an erroneous judgment.

The pairwise comparison of criteria based on thepredetermined scale and its organization in a matrixform facilitates further analysis of the information.

The relative comparisons of various criteria representthe elements, a , of the upper triangle of the com-i j

parison matrix. The lower triangle of the matrix isestablished by taking the corresponding reciprocalsfrom the upper triangle. The relative importance ofcriteria or the priority vector is established by com-puting the eigen vector corresponding to the maxi-mum eigen value of the comparison matrix.

It is important that consistency should be main-tained during the pairwise comparisons, that is, ajk

sa ra where i, j, ks1, . . . , n. For example, ifi k i j

comparing X, Y, and Z, it was found that X is twiceas important as Y and three times as important as Z,then while comparing Y and Z, Y should compare inclose proximity of one and a half times as importantas Z. Analytical Hierarchy Process allows for rea-sonable deviations in consistent comparison and doesnot require the consistency to be exact mathematical

Ž .proportions. The consistency of n=n matrix canbe established by computing the consistency ratio

Fig. 4. Establishing the basis for group member evaluation using AHP.


Ž . Ž .CR for the matrix, defined as shown in Eqs. 1Ž .and 2 .

Consistency Ratio CR sCI%RCI, 1Ž . Ž .

Consistency Index CI s l yn % ny1 ,Ž . Ž . Ž .max

2Ž .

Ž .where, Random Consistency Index RCI is definedas the average CI for a large number of reciprocalmatrices of the same order with random entries andl is the maximum eigen value of the matrixmax

under consideration. Empirical studies conducted bySaaty have indicated that a deviation in consistencyratio of less than 10% is acceptable without ad-

w xversely affecting the results 21,22 . If the consis-tency ratio for a matrix is greater than 0.1 then,either the values in the matrix should be rejected orelse, steps should be taken to modify the pairwisecomparisons till an acceptable consistency ratio isobtained.

At every level in the hierarchy, a similar pairwiseanalysis is conducted for each subcriteriaralternativeof that level. For each criterion of the precedinglevel, a pairwise comparison is performed betweenall the subcriteria related to it in the next lower level.The comparison matrices are evaluated to establishthe priority vectors, i.e., eigen vectors corresponding

w xto the maximum eigen values 17 . These priorityvectors are weighted by multiplying them with theweight of the corresponding criteria from the preced-ing level, thus defining weighted priority vectors.Similar procedure is employed at each level of thehierarchy.

Aggregate vectors are computed by adding theweighted priority vectors obtained at the last level in

Ž .the hierarchy i.e., the alternative level with respectto each criterion. An aggregate matrix is definedwith the rows formed by the aggregate vectors ob-tained in the previous step. The final priority vectoris computed by adding the column entries of theaggregate matrix. The final priority vector definesthe preference among the alternatives with respect toall the criteria and subcriteria. The hierarchy shownin Fig. 1 was evaluated using this procedure for ahypothetical example situation. The analysis and the

results have been illustrated in the following para-graphs.

5. Example situation

Consider a project situation in a business districtŽ .that involves rehabilitation of 22 000 LF ;6.7 km.

Ž .of 8 in. 20.32 cm. collector sewers and 3000 LFŽ . Ž .;914 m of 6 in. 15.24 cm. laterals. The cityengineers are considering four process alternatives:Ž . Ž . Ž .i open-cut conventional , ii use of an automatedpipe laying equipment, and two microtunneling op-

Ž . Ž .tions iii pipe bursting, and iv cured-in-place pipeŽ .CIPP . Each alternative presents several advantagesand disadvantages and the city engineers are inter-ested in evaluating the four options to select the bestprocess.

The Open-cut or the conventional method is wellpracticed in the construction industry and it would berelatively easy to identify a suitable firm to under-take the job. However, there are several drawbackswhen compared to the other options that requirecareful analysis. The open-cut method would requirelonger duration and would create social disruptionand inconvenience for the businesses in the projectpath. Additionally, the open-cut method is laborintensive which therefore enhances safety concernsfor the workers inside the trench as well as pedestri-ans, requiring additional insurance to safeguard

w xagainst possible accidents 8 . Due to space limita-tions, careful planning would be required to coordi-nate between material supplyrhandling and otherproject activities.

Ž .The automated pipe laying equipment APLE isa new technology with no case history to date.However, simulation studies have indicated possibleincrease in productivity by over 18% per day when

w xcompared to the conventional open-cut method 8 .ŽThe primary objective of developing APLE Auto-

.mated Pipe Laying Equipment was to eliminate theneed for a worker inside the trench, while achievingincreased productivity and reduction in operationcosts. The APLE includes an excavator with a spe-cially designed manipulator attachment for the pipelifting, pipe lowering, positioning, and fixing opera-tions. The equipment is designed to be track mountedwith two sets of rigid legs to help in excavation and


pipe laying operation. In addition to enhanced workersafety, APLE offers better productivity and quality.Although, initial cost would be high, improved pro-ductivity will reduce the project duration as well associal disruption and loss of business for the affectedarea.

Pipe bursting is a trenchless pipe replacementtechnology developed by British Gas that is widelyused in Europe and is fast gaining acceptance inNorth America. Using this technology, the existingpipe is burst in place and pushed into the adjoiningsoil while the replacement pipe is pulled in its place.It has limited cut and cover requirements and signifi-cant improvement in productivity can be achievedparticularly in a business district where location ofother utility pipes is a major concern for cut and

w xcover operations 13 . Although this technology ischeaper and faster than the conventional method,there are only a limited number of contractors in thecountry who are licensed to operate this technologyw x14 .

Ž .Cured-in-place pipe CIPP is also a trenchlesstechnology with wide acceptance in the construction

industry. In this technology, a non-woven polyestermaterial laminated to a layer of polyurethane is usedas the CIPP material. The polyester felt tube isinjected with a blend of polyester resin and a catalystand is passed between calibrated sizing rollers. Thetube is then inverted in the existing pipe and curedby using water under pressure and elevated tempera-

w xtures 15 . The technology offers several advantagessuch as reduced duration, minimum social disrup-tion, and improved productivity. However, it alsopresents some disadvantages such as if the CIPPrequirements are not designed properly, then, rein-forcing the pipe in one location only increases theinfiltration at some other point of least resistancealong the length of the pipe. Also, product reliabilityis a concern in the CIPP industry where new prod-ucts and processes frequently appear, disappear, andreappear under different names creating confusion

w xand loss for the contractor 16 .The project situation described above is analyzed

with respect to the hierarchy shown in Fig. 1 and theŽ .two models associated with AUTOCOP: i group

Ž .decision model and ii analytical model. The analy-

Fig. 5. Sample group member evaluation and weight determination.


sis and the procedure are explained in the followingparagraphs.

6. Group decision model

Ž .The objective of the group decision model GDMis to assist the PDM in collecting and evaluating theopinion of other team members leading to a groupdecision that establishes the relative preferenceamong criteria, subcriteria, and alternatives. TheGDM also allows the primary decision maker toevaluate each member of the team to establish the

Ž .importance or weight of individual input for deci-sion making. The evaluation of the team members isthen used to weigh the input provided by each teammember and to arrive at a group decision.

Ž .In the group decision model GDM , the primaryŽ .decision maker PDM evaluates each team member

Ž .with respect to four criteria i their technical knowl-

Ž . Ž .edge, ii experience, iii current project knowledge,Ž .and iv knowledge about the firm. This evaluation is

performed in two stages. In the first stage, the PDMperforms a pairwise comparison of the four criteriato determine his or her value based preference among

Ž .the four criteria refer to Fig. 4 . The comparisonmatrix is evaluated based on the AHP to determinethe priority vector as explained earlier. Each criteriahas four associated intensities or subcriteria namely:extensive, significant, moderate, and low. Pairwisecomparison of the subcriteria with respect to eachcriterion establishes the distinction between the fourintensities. The priority vector established for eachcomparison matrix determines the weight for thesubcriteria associated with a particular criterion, e.g.,with respect to technical knowledge, one may notconsider much difference between significant andmoderate, whereas in the case of experience, onemight distinguish significant experience from moder-ate experience. This analysis has been illustrated in

Fig. 6. User interface for group member evaluation.


Fig. 4 for the hypothetical project situation describedearlier.

In evaluating the group members based on thecriteria mentioned above, an absolute scale is used asopposed to a relative scale. An absolute scale isfavored in situations where the user’s preferenceamong criteria and subcriteria are independent of theavailable alternatives and each alternative is mea-sured on its own merit. In absolute scale measure-ments the user establishes the preference amongvarious criteria and subcriteria by using the sameprocedure of pairwise comparison as used in therelative scale. However, the alternatives are notranked by pairwise comparisons but only on theirindividual merit. The academic grading of A, B, C,D, and F is an example of absolute scale measure-

w xment 20 . The procedure for group member evalua-tion has been illustrated in Fig. 5.

In the second stage of the group member evalua-Ž .tion, the PDM evaluates or grades each group

member’s technical knowledge, experience, currentproject knowledge, and knowledge about the firm asextensive, significant, moderate, or low. The scoreobtained by each group member is the sum of the

Žweighted score obtained under each category refer.to Figs. 5 and 6 . The scores obtained by each group

member are normalized to determine the weight foreach group member’s input for the decision problem.Fig. 6 illustrates the user interface for group memberevaluation.

ŽAs shown in Fig. 7, the decision hierarchy refer.to Fig. 1 is evaluated by each group member in

reference to the example project situation describedearlier to establish relative importance of the variouscriteria, subcriteria, and alternatives with respect to

Žtheir knowledge based preference. For example re-. Žfer to Fig. 7 , group member 1 compares A1 need-.based criteria to have the same importance as A2

Ž . Ž .technological criteria and A4 safetyrrisk criteria .Whereas A1 is evaluated to be 1r4 as important as

Fig. 7. Sample group decision and user interface.


Ž .A3 economic criteria and three times as importantŽ .as A5 project specific criteria . Similarly, other

comparisons are made by the group member whilemaintaining the consistency of the input provided.The input provided by each group member isweighted according to the group member evaluation

Ž .performed earlier refer to Figs. 5 and 6 . The sum ofthe weighted input from team members provides thegroup decision. The group decision thus obtained isused in the analytical model for further analysisŽrefer to Fig. 7: group decision and Fig. 8: Level A,

.Criteria .

7. Analytical model

The objective of the analytical model is to evalu-ate the input provided by the group members andestablish the group’s preference among the variouscriteria, subcriteria, and alternatives. As explainedearlier, the decision process starts by identifying thevarious criteria, subcriteria, and alternatives relevantto the decision task. These criteria and subcriteria arethen arranged in a hierarchy as shown in Fig. 1. TheAnalytical Hierarchy Process is used for establishing

Ž .the comparison or weight matrix for each level of

Fig. 8. Sample comparison matrices for criteria, subcriteria, and alternatives.


the hierarchy and for computing the priority vectorsas explained earlier.

Fig. 8 shows sample comparison matrices and thecorresponding weight vectors established for thehypothetical project situation evaluating the optionbetween the Automated Pipe Laying EquipmentŽ .APLE and open-cut trench method. The compari-son matrices of Fig. 8 utilize the five decision crite-ria as well as the associated subcriteria as illustratedin Fig. 1.

Once the weight vector for the set of criteria hasbeen established, the analysis can proceed to the nextlevel and comparison matrices can be defined foreach criterion by comparing the subcriteria related toit. As shown in Fig. 1, each criterion is related to aspecific set of subcriteria and thus the comparisonmatrix is prepared by only considering those subcri-teria that are related to a specific criterion. For

Ž .example, need-based criterion A1 is related to sevenŽ .subcriteria refer to Fig. 1 . To define the relative

importance of the seven subcriteria with respect toneed-based criteria, a comparison matrix is definedŽ .refer to Fig. 8, Level B and the correspondingpriority vector is computed as described earlier. Thepriority vector thus established is multiplied by theweight computed for need-based-criteria, i.e., 0.2476Ž w x .refer to Fig. 8, WA , and a new vector called the11

Žweighted priority vector is defined refer to Fig. 8,.Level B . Similarly, comparison matrices are estab-

lished for each criterion and the related subcriteria.These matrices are evaluated to establish their corre-sponding weighted priority vectors.

The procedure for defining the comparison matri-ces and the related weighted priority vectors is re-peated for the next level in the hierarchy. However,this time the comparison matrices are defined bycomparing the associated alternatives with respect toeach subcriterion. A priority vector is computed foreach comparison matrix and it is multiplied with theweight assigned to the associated subcriteria in theweighted priority vector of the previous level. For

� 4example, the priority vector C1, C2 computed withrespect to subcriteria B1.1 is multiplied with the

Žweight assigned to subcriteria B1.1, i.e., 0.0265 refer.to Fig. 8, Level C . Similarly, weighted priority

vectors are computed for each subcriterion and theassociated alternatives. The bottom of Fig. 8 shows

� 4the weighted priority vectors C1, C2 that were

computed for criterion A1 with respect to all theassociated subcriteria.

In the following step, aggregate priority vectorsare computed by adding the weighted priority vec-tors for all subcriteria related to a particular criterion.For example, as shown in Fig. 8, the aggregate

� 4priority vector for criterion A1 is 0.1390, 0.1086 .Similarly, aggregate priority vectors are computedfor each criterion in Level A. An aggregate matrix isdefined with rows representing the aggregate priority

Ž .vectors obtained in the previous step refer to Fig. 9 .This matrix defines the weights for alternatives C1and C2 with respect to each criterion. As a last step,the cumulative weight vector or the final priorityvector is computed by taking the sum of the column

Ž .elements of the aggregate matrix refer to Fig. 9 .The final priority vector represents the preferenceamong the two alternatives with respect to all theestablished criteria and subcriteria. The user canmake the decision between the automation option,

Ž . Ž .APLE C1 and the conventional process C2 basedon the preference established by the final vector. Asimilar analysis is done to establish the preferencebetween the conventional process and the other twooptions: CIPP and Pipe Bursting. The results havebeen shown in Fig. 9. The user can also perform a

Žsensitivity analysis by modifying weights or prefer-.ence in the comparison matrices and then choose

the best representative option based on the modifiedresults. In this example the conventional process is

Žcompared in-turn with each automation option refer.to Fig. 9 . However, the same procedure can also be

used to compare two automation options at a timesuch as APLE vs. pipe bursting or CIPP vs. APLE orpipe bursting.

The present model allows comparison of twooptions at a time, however, with small modificationsthe model can be easily adapted for comparing more

Ž .than two options or alternatives at a time. It can beseen from the results shown in Fig. 9 that for theproject under consideration, CIPP turns out to be thebest technological replacement for the conventionalopen-cut method when compared to APLE and PipeBursting. This analysis is likely to vary from projectto project and might also vary from one user groupto another since the evaluation process incorporatesthe value based judgment of the primary decisionmaker and the team members as well as their knowl-


Fig. 9. Sample aggregate matrix and final priority vectors.

edge and expertise. In other words, the decisionmodel reflects the perception and judgment of theuser group through a more structured, systematic,and careful analysis.

The proposed decision support system provides asystematic method to analyze available options basedon the various criteria and subcriteria established forthe decision task. In process selection situationswhere historical performance data is available for theoptions under consideration a complex analysis maynot be required. However, in the absence of signifi-cant historical performance data it is important thatall important aspects are carefully analyzed and sen-sitivity analysis performed before selecting a con-struction process, particularly advanced automation.To assist the user in this decision problem and forperforming sensitivity analysis, an easy to use userinterface has been designed.

8. User interface

AUTOCOP is an MS Excel based DSS. Thechoice of Excel as the development tool was primar-ily based on better availability, use, and understand-ing of a spreadsheet environment in the constructionindustry as well as the ease with which a prototypecould be developed in this environment. The com-plexity of the decision problem mandates a userfriendly interface that would allow easy interactionwith the system. The user interface for AUTOCOP

has been developed using Excel Visual Basic. Seriesof dialog boxes are provided to explain the proce-

Ž .dure and to assist the user in i evaluating the groupŽ . Ž .members, ii computing the group decision, and iii

in performing the pairwise comparison to establishthe preference between criteria, subcriteria, and thealternatives.

The user interface allows easy interaction with thesystem and facilitates sensitivity analysis of the in-puts and the corresponding results. As shown in Fig.7, the user can provide the input with respect to thepairwise comparison by utilizing the drop-downcombo boxes. The values in the combo-boxes repre-sent the comparison scale of Table 1. A button called‘scale’ has been included in the dialog box to pro-vide easy access to the comparison scale. The userinterface not only facilitates data input but also

Žprovides real time feedback in terms of results finalpriority vector, intermediate priority vectors, group

.decision, etc. and also sensitivity analysis. The userinterface also provides a tabulation of results toinclude the final decision and priority vectors estab-

Žlished at each level of the hierarchy refer to Fig..10 . A printout of the results allows the user to

critically assess the evaluation of the criteria, subcri-teria, and alternatives and perform modifications andsensitivity analysis as desired. Following extensivevalidation of the system by various users, it is antici-pated that future improvements in the system wouldinclude development of a stand alone visual basicapplication.


Fig. 10. Sample summary of results.


9. System validation

A system designed to assist a user in a decisionproblem should be validated with respect to three

Ž .main parameters i algorithm or model validation,Ž . Ž .ii concept validation, and iii user interface valida-tion. The first mode of validation refers to the mathe-matical and logical progression of the proposed modelor algorithm. The second mode of validation refersto the usefulness of the proposed concept for thedecision maker and the industry. The third mode ofvalidation refers to the ease with which the end usercan apply the concept and the model to the decisionproblem.

AUTOCOP and the proposed decision model havebeen developed on the basis that in process selectionsituations where sufficient historical performancedata is not available it is important to carefullyanalyze the benefits and drawbacks of the availableoptions before selecting a process, particularly ad-vanced automation. This paper has described at lengththe proposed decision support model, the AHP algo-rithm used for evaluating the criteria, subcriteria, andthe alternatives, and the user interface developed foreasy interaction and sensitivity analysis. Efforts areunderway to solicit assistance from construction firmsas well as academic researchers involved in this areato validate the effectiveness of the proposed modelfor their specific process selection requirements. Aseries of case studies are planned to validate themodel. As it is likely to take some time for thisphase of the research to conclude, this paper hasfocused on elaborating the theoretical validity of theproposed model while the case study results will bereported in a subsequent paper.

10. Conclusion

Development in the domain of construction au-tomation has created the need to critically evaluatethe feasibility of replacing a conventional construc-tion process by an automated or other advancedsystem. However, as every construction project isunique, it is necessary to evaluate the suitability ofan automated system on a project to project basis byconsidering all the criteria and subcriteria importantfor the decision task. This paper presents a frame-

work for a decision support system to assist con-struction managers in this decision process by sys-tematically evaluating five groups of criteria andsubcriteria with respect to the knowledge based pref-erence of the user. The decision support model pro-posed in this paper recommends hierarchic evalua-tion of criteria, subcriteria, and alternatives by utiliz-ing the Analytical Hierarchy Process. The hierarchywas established by carefully identifying the variousfactors that should be considered while evaluatingthe suitability of a construction process, particularlyadvanced automation.

The AUTOCOP system incorporates knowledgebased preference of team members in arriving at agroup decision and establishing priorities among thecriteria, subcriteria, and alternatives. The user inter-face has been designed to facilitate sensitivity analy-sis for what-if scenarios. There are numerous bene-fits of the proposed system namely systematic com-pilation and analysis of various criteria involved inthe decision process, which ensures that all importantcriteria and their relevance in this decision processhave been considered resulting into a more reliabledecision. Additionally, model framework and use ofAHP allow analysis of both quantifiable and intangi-ble decision criteria that is particularly important in adecision process where sufficient past performancedata for automation options are not available. Themodel analysis leads to the identification of the mostsuitable option on the basis of priority.

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Advanced automation or conventional construction process?