scenario-planningmethodforcostestimationusing...

Research ArticleScenario-Planning Method for Cost Estimation UsingMorphological Analysis

Sae-Hyun Ji1 and Joseph Ahn 2

1Institute of Construction and Environmental Engineering Seoul National University Seoul 08826 Republic of Korea2Division of Architecture Civil and Environmental Engineering Hoseo University Asan 31499 Republic of Korea

Correspondence should be addressed to Joseph Ahn josephahnhoseoedu

Received 31 October 2018 Revised 9 January 2019 Accepted 17 January 2019 Published 18 February 2019

Academic Editor Behzad Esmaeili

Copyright copy 2019 Sae-Hyun Ji and Joseph Ahn is is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Early cost estimates are emphasized repeatedly in the initial decision-making process to set a direction for the success ofconstruction projectserefore alternatives need to be examined and the consequences for the cost should be analyzed carefullyis study proposes a scenario-planning method that uses morphological analysis for the estimation of construction cost A casestudy was conducted using public data on 102 apartment buildings from 10 housing complex projectse results show estimationaccuracy of 423 to 486 and an average stability enhancement of 139 to 173 e proposed process can produce adaptablescenarios and evaluate the impact of the scenarios in a complicated decision-making process with limited information providedFurthermore this method can provide a contingency plan to cushion against uncertainties

1 Introduction

Ackoff [1] defines three levels of a complex problem a messa problem and a puzzle Amess is a complex issue that is notdefined or structured concretely so it is hard to find out thefocal problem A problem has a defined form with variablesand shows how these variables interact but it does not have aclear solution A puzzle is a well-defined and well-structuredproblem with a specific solution us a path is needed tomake a complex problem into a puzzle In this regardmorphological analysis works at the level of messes andproblems and turns messes into problems is approach iswidely applied to build scenarios that can outline entireissues or impact factors related to the problems

A scenario can draw rich and detailed portraits ofplausible futures or future states of a system Scenario-basedapproaches can be viewed as strategic methodologies thatcan help decision makers in uncertain environments thatrequire rapid response A scenario-based method for sup-porting decision-making could be used to prepare diversecombinations of causes and effects with convincing expla-nations rather than a prediction from a specific point ofview erefore a scenario-based method could respondwith predictive measures according to different demands

Decision-making in the early stages of a project mainlyaim to set a direction for the projectrsquos success [2] which isclosely related to the fluctuations in project cost us earlycost estimates are emphasized repeatedly in the initialdecision-making process for construction projects Manyalternatives should be examined and then the consequencesfor the cost should be analyzed However this issue is rarelydealt with in previous research which focuses on the di-versification or development of estimation methods and theenhancement of accuracy To deal with this challenge thisstudy proposes a scenario-planning methodology for costestimation of a construction project using a morphologicalanalysis method A case study was conducted using publicdata from 102 apartment buildings from 10 housing com-plex projects e suggested method that applied cost es-timation outputs was compared with a previous case-basedreasoning (CBR) cost model [3] using the same conditions

2 Literature Review

21 CostModels in Construction A cost model is defined asa symbolic representation of a system with contents thatare expressed by factors that impact the systemrsquos cost [4] Acost model has a crucial role of supporting and facilitating

HindawiAdvances in Civil EngineeringVolume 2019 Article ID 4962653 10 pageshttpsdoiorg10115520194962653

decision-making by simulating current or future situations[6] e overall success of a construction project is oftenmeasured by how well the actual cost compares to the earlycost estimates [5] and numerous cost estimations are donerepeatedly especially in the early stages Cost models canprovide more reliable cost advice A good cost modelshould be simple accurate unbiased timely and stableenough to easily integrate it into the cost system Ac-cordingly the significance of cost estimation of a con-struction project for the whole life cycle cannot beunderestimated

Recent studies apply various methods to increase theaccuracy or confidence of cost estimation with focus on theinitial stages Conventionally a parametric method thatutilizes representative variables is widely used because it isquick and easy to iterate e method can reflect changes intime locations and productivity changes if the data arereliable (Barrie and Paulson) However the prediction ac-curacy is relatively low since the method does not reflectvarious factors that affect the construction cost

Artificial intelligence (AI) approaches are also beingemployed Previous research has used artificial neural net-works (ANNs) to predict the cost of highway construction[7] school buildings [8] and the structural system of abuilding [9] CBR has also received much attention as analternative method for estimating cost CBR is a process ofsolving problems by recognizing their similarity to past cases[10] CBR is more flexible than ANN in updating the systemand it is more successful in handling missing information

In recent years numerous studies have been conductedfor CBR cost estimation Yau and Yang [12] developed aCBR estimation method for the preliminary design stageDogan et al [13] proposed a CBR cost model for astructural system An et al [14] developed a CBR costmodel for a residential building using an analytic hierarchyprocess (AHP) Koo et al [15 16] developed a CBR-basedhybrid model for predicting the duration and cost of aconstruction project Ji et al [4 17 18] developed a CBRcost model and suggested a CBR cost estimation system forpublic projects

It is important to note that cost estimation is aknowledge-intensive engineering work and requires ex-pertise of the human professionals [18 20] However thereare difficulties in utilizing engineersrsquo expertise since expe-rienced knowledge is not often documented and thiseventually leads to subjectivity [18] erefore further re-search is required to compensate subjectivity of experts andsupport decision-making regarding cost estimation ofplausible alternatives in a quantitative manner

22 Morphological Analysis Morphology is the study of theshape and arrangement of parts of an object and how theseparts conform to create a whole or Gestalt [21] Morpho-logical analysis is a nonquantified modeling method forstructuring and analyzing technological organizational andsocial problems [21 22] Morphological analysis is used torepresent a problem using a matrix that has combinations ofparameter conditions e parameters on the horizontal axisrepresent components of the targeted object and the

conditions on the vertical axis describe the nature of eachparameter As shown in Figure 1 each of the parameters isshown in a column with the possible conditions as boxes inthe column In a given combination conditions are assignedto a parameter by highlighting the relevant conditions Forexample X1-Y3-Z3-W2 is one of the possible configurationsof this four-by-four matrix

Morphological analysis is used to construct scenarios byoutlining issues with identified driving forces One of the mostpowerful and intuitive ways to incorporate the uncertainties inthe planning stage is to use scenarios [23] Scenario planning isan imagined sequence of future events and can be used as a toolto help make more effective decisions To develop a scenarioSchwartz [24] proposed an eight-step scenario-planning pro-cess that focuses on identifying key factors their relationshipsand their impacts on future events Morphological analysiscould thus help to produce well-organized scenarios made upof parameters and conditions and to estimate the implicationsIn this study building elements were used as parameters andthe grades of finishing materials were used as conditions

3 Scenario-Planning Method

31 Framework Scenarios can be defined as an imaginedcombination of facts and relevant results and scenarioplanning can help to make robust strategic choices [25]Figure 2 shows the scenario-planning framework and ex-pected function in design process

e design process plays an important role in decision-making As shown in Figure 2 the cost estimate is usuallymade by the design stage Decision-making is being madethrough the design process and cost estimates are conductedin each design phases in general (Figure 2) In addition if theerror range of the estimated cost for each design phaseexceeds the budget it is necessary to review the scope de-fining in stage 1 in the worst case Under these circum-stances it is not easy to determine which stage to go back toand review Since the content and scope to be reviewed andthe amount of work to be required are significantly differentaccording to each design stage it is very important to makeproper decisions about to which stage to return If stage 5documentation is to be returned to stage 1 scope definingdue to huge mistakes that had not been discovered beforethen the costs and time required for redesign would beimmense Damages due to subsequent delays in constructionwill also be greater

As a complement to this the scenario-planning methodproposed in this study can present the criteria of themanageable limit of the cost overrun for each design stageis should be considered separately from the designcontingency Contingency is a pure countermeasure againstuncertainty in project progress whereas the scenario matrixof this study can be regarded as a response strategy because itmakes uncertainty a predictable risk that can be dealt with

For example there is a project manager Suppose that theproject heshe is managing is in the detailed design stage andthe cost estimate result has recently exceeded +10 At thispoint the project manager will be troubled It will be difficult todecide whether to go back to the scope definition stage and

2 Advances in Civil Engineering

review again or move on to the next level with affordablemagnitude However if there are applicable scenario matricesof finishing material changes which analyze the cost variationcompared to the project total cost and indicate plusmn 10 vari-ations can be allowed in this case the project manager will bedirected to the next step with very certain confidence If theopposite is the case it is possible to provide a concrete in-dication as to whether the upgrade of the finishing material ispossible Based on this it will be possible to support decision-making of establishment or modification of ownerrsquos salesstrategy In short the proposed scenario-planning methoddiagnoses feasibility test results confirming whether this ismanageable at the certain design stage or not is enablesaccurate and fast decision-making support

If there is a high demand of comparable types of buildingsestablishment of sales strategies is crucial to anyone whowants to initiate a new construction project e strategiesshould be balanced between the customer-oriented qualitylevel and developer-oriented selling price because increase ofprice has a negative effect on the attractiveness of theproducts us multiple and integrated examinations re-garding optimal combinations of the quality levels and theprices should be considered that would be different dependingon the given environments In this context contingency costis a conventional strategy in the beginning stage to respondunexpected budget shortage However it is limited to supportthe decision-making on complex and dynamic circumstancebecause this method cannot provide detailed and easily se-lectable solutions in response to various situations but showsan amount of money that can be spent However a decisionmaker who can only utilize limited information at initialstages can analyze an elementrsquos influence on project costvariance by following the suggested research process As anexample this research illustrated how to apply the proposedapproach to real projects using Korean apartment projects

Basically the scenario-planning method is based onthe cost analysis of project data As diagramed in the rightside of Figure 2 the scope defining which reflect theproject goal and objectives is the beginning of the pro-posed planning method What is the goal and objectiveof a project It would be buyersrsquo benefit good reputationof a development company sellersrsquo benefit and so onIf a companyrsquos determined goal is buyersrsquo benefit then thesales strategy is entirely focused on elevating the quality

level If the project is a multihousing construction-like casestudy the interior work quality level can be controlled byupgrading interior finishing materials en key decisionfactors should be identified by analyzing cost data of con-struction projects which are expressed by cost proportions ofa project total cost e factors can be changed according tothe formerly determined objectives In a case study we foundkey decision factors which affect the interior finishing level bycalculating cost proportion Since the level would dependupon the quality level of finishing materials it would bediscovered and described as elements

e project cost can be segregated into work types suchas preliminaries site work interior work stone and tilework and so forth Furthermore the work types are con-sisted of elemental works like living room wall finishing andceiling in the interior finishing and bathroom floor tile worksand entrance stone in the stone and tile

e work is divided into elements each consisting of acombination of material cost and labor cost erefore eachelementrsquos impact and variability on a projectrsquos total cost can becalculated when bills of quantities of the projects are analyzed

Based on this analysis we can move to the next stepparameters and subparameter selection is step findsmajor cost variance elements (ie parameter) in the keydecision factors and gets these divided into subelements(ie subparameter) e framework includes methods ofcategorizing and grouping items because the cost of anelement is not a single but a combined price of materials andlabor works or combinations of the related works ere-after conditions of each subparameter which are the con-figurable alternatives of materials are developed

In accordance with the combinations of these condi-tions the numericalized influence on the project cost can bedeveloped that is a kind of reflection on the required orcurrent trends of construction projects at is to say thescenario matrix expresses selectable options using the al-ternative parameters and conditions

32 Process Development e scenario-planning processbegins by defining the scope of the work Many scenarios areiteratively applied in every production construction processespecially in the initial stages As shown in Figure 3 theearlier the phase of a project is the more frequent the use of ascenario-planning method to predict the consequences(eg a cost or schedule) in response to the changes of theproject scope or business objectives When the projectprogresses to the detail phases the application of detailedscenarios is required to support decision-making which isdescribed by combinations of design variables As shown inFigure 2 many alternatives based on certain scenarios atevery decision-making point are used and can be customizedcontinuously depending on the circumstances e oppor-tunities to apply scenarios increase according to the level ofthe projectrsquos development

We examined ownersrsquo opinions about a CBR costmodel without the scenario module We interviewed skilledpersonnel in cost estimation from eight public enterprises inhousing development in South KoreaMost of them expressed

X Y Z W

X1 Y1 Z1 W1

X2 Y2 Z2 W2

X3 Y3 Z3 W3

X4 Y4 Z4 W4

ParametersC

ondi

tions

Figure 1 Scenario-planning process

Advances in Civil Engineering 3

Phases

Decision-makingpoint 1


A



Decision-makingpoint




10

Arsquo

11

A

B

10

Arsquo

Brsquo

11

A

1

B

10

Arsquo

1rsquoBrsquorsquo

11

-2-

Decision-makingpoints

Chronologically editable andcomparable

scenarios

Conceptual design Schematic design Detailed design Documentation

Most plausible scenarios and user-customized

scenarios

Figure 3 Opportunities to apply scenarios

Stage 1 scope defining(i) Number of households and floors

(ii)

(i)(ii)

(i)

(i)

(i)

(i)

(i)

(i)

(i)

(ii)

Unit household type and areaProject goal and objectives

WT denotes the cost proportion of work types

Project

Interior finishing

Wall

Livi

ng ro

om

Bath

floo

r

Bedr

oom

Entr

ance

Wall

Stone amp bathroomWT1 WT2 WTnndash1 WTn

WnkWn

kndash1Wn

1ndash1W2

kW21W1

1

W denotes the cost proportion of works

Major cost variance elements analysis

Configurable alternatives of materials develop

Available cost combinations develop

Interior standardGross floor area and each floor area

User requirements and trendsConstraints and major considerations

Design code and regulations

Bills of quantities and prices

No Feasibilitytest

Yes

No Feasibilitytest

Yes

No Feasibilitytest

Yes

No Feasibilitytest

YesConstruction starts

Stagemanageable

Stagemanageable

Stagemanageable

Stagemanageable

Step 1 scope defining

Step 2 identifying key decision factors (cost proportion analysis)

Step 3 parameter and subparameter selection

Step 4 defining conditions

Step 5 scenario matrix development

Stage 2 conceptual design

Stage 3 schematic design

Stage 4 detailed design

Stage 5 documentation

Design process Scenario planning framework

Figure 2 Scenario-planning framework and expected function in design process


an affirmative response to our cost model but they pointedout a deficiency related to making minor revisions If majoritems such as the structural system gross floor area ornumber of floors are determined the outputs retrieved by thecost model should be customizable Similar cases whosesimilarities are calculated based on impact factors are in-sufficient to represent a solution of a given problem ere-fore these cases need to be revised in response to changingconditions of lower elements such as the grade of finishingmaterials is minor revision feature would allow the CBRcost model to analyze the impact on the results efficientlyaccording to the variation of the elements without additionalmodel runs

In this situation a scenario-planning method is essentialto develop combinations of conditions and their conse-quences Furthermore the addition of a scenario componentin the CBR cost model will magnify its advantages of quickresponse and high precision e interviewees wanted tosimulate or identify the impact on the fluctuation of totalcost such as in accordance with the alteration of finishingmaterialsey especially wanted to evaluate their designs bycomparing them to 4-level finishing material standard of theKorea Land and Housing Corporation which is regarded asa government marker of grades erefore we analyzed thespectrum of construction cost elements and selected themost influential ones to develop a scenario

4 Case Study

41 Identifying Key Decision Factors e construction costcan be affected by combinations of many elements so it iscrucial to collect data and identify key decision factorserefore we collected data on 102 apartment buildings from10 housing complex projects in South Korea from publiccorporations e data of each building cost are organized bywork types To examine the impact of work types the averagecost portions of trades were analyzed as summarized inTable 1 e highest cost is from reinforced concrete work(4094) followed by interior finishing work (886)

In South Korea most apartment buildings are built withreinforced concrete wall structures so structural work maynot be considered as a design alternative in the designprocess [3] However seven trades are related to finishingwork such as interior finishing stone and tile and windowworks eir impact on the cost varies significantly Gen-erally an apartment building is composed of manyhousehold units Accordingly changes in items with lowprice differences can have an amplified impact on the costvariance because of their quantity Consequently weidentified that finishing work should be treated as a keydecision factor to develop a scenario We selected thewindows interior finishing and stone and tile works as thetargets for scenario planning which have a higher influenceon decision-making than other finish work

42 Parameter Extraction and Condition Definition eitems of stone and tile works are categorized by their ele-ments as shown in Table 2 Despite the high cost of balcony

flooring that element is excluded in the parameter selectionbecause the balcony flooring material is used for only oneitem of ceramic tile in our data is means that the elementis only installed using a sort of economical considerationFor the same reason we also exclude the corridor lobby anda small part of buildingrsquos exterior design as parametersAccordingly the entrance floor kitchen and bathroom arechosen as parameters for stone and tile works Furthermorethe selected parameters are separated into subparameters bytheir elements the kitchen has a wall finish the bathroomhas a wall and floor finish and the entrance floor has a floorand joists Different finishing materials can be usedaccording to their elemental attributes (ie their condition)

e parameters in the interior finishing work trade(Table 3) are divided into fixed items and selectable items tomake a scenario e base materials of other finishing workcannot be changed such as gypsum boards ceiling boardsand insulation materials erefore we regard these as fixedparameters and exclude them As a result three changeableitems were chosen for further development flooring boardswall finishes and ceiling finishes e parameters werecategorized by their elements as the living room (whichincludes the kitchen) the bedroom and the main roomAccordingly the wall ceiling and floor finishing have thesame subparameters that have their own conditions wherealterations affect the project cost When compared to theformer analysis the cost proportions of elements and spaces

Table 1 Building cost proportion analysis and finish-work re-latedness check

Work types Costproportion

Percentagerank

Finished-workrelatedness check

Preliminaries 86 4Ground works 318 9Reinforce concrete 4094 1Steel frames 016 17Masonry 209 10Blocks 047 14Stone and tile 445 6 radicPlastering 332 8 radicCarpentry 090 13 radicWaterproofing 161 12Painting 204 11 radicInterior finishing 886 3 radicMetal construction 385 7Doors and windows 1134 2 radicRoof 034 15Miscellaneous 120 16Furniture 665 5 radic

Table 2 Cost proportion analysis (stone and tile works)

Stone and tile worksElements Cost proportion Percentage rankEntrance floor 70 5Kitchen 59 4Bathroom 390 1Balcony 195 3Other parts 287 2


in this parameter are analyzed in reverse because the bills ofquantities in South Korea do not consist of the elementalcost However the values of elemental cost proportions inthe matrix of parameters are not different from the figure ofldquostone and tile workrdquo

Door and window work (Table 4) is composed of in-stallation locations for each household balcony andcommon-use area ese elements are divided into framingand glazing e conditions of subparameters are differen-tiated by the material used Generally customers are sensitiveto the quality of windows in a household whereas the grade ofthe windows installed in lobbies and stair halls is not se-lectable For this reason the items of the common-use areaand miscellaneous items are excluded from the examination

43 Scenario Matrix Development e condition of thesubparameters should be decided is study refers to a tableof combination levels of interior materials developed by theKorea Land and Housing Corporation (LH Corporation) todevelop a scenario matrix e corporation is the largesthousing supplier in South Korea and this grade table is widelyused as a standard for housing complex projects Conse-quently the concepts are tabulated using the parameters todescribe the building elements and the material substitutionsas shown in Tables 5ndash7 Acronyms are used according tocombinations of the initial letters of each material

44 ImplicationAnalysis To find out the degree of influenceon the cost of each condition the bills of quantities of threetypical apartment building projects supported by the LHCorporation were analyzed We analyzed the selected sub-parameterrsquos material quantity under the conditions of fixedprices and quantities in a project to prevent the negativeinfluence of other subparameters e influence of a sub-parameter is calculated by multiplying its price and quantity

ere are many combinations of conditions but thisstudy applies four representative combinations developed bythe LH Corporation In this classification various materialsare designated differently from interior grades which meansthe conditions of the interior work elements are decided(Tables 5ndash7) With the combination tables the average

influences of each subparameter are calculated by applyingthe prices according to the grades To quantify the degree ofinfluence on the total cost and the elemental cost thecondition weight (CW) and base condition weight (BCW)are defined as the ratio of the elemental cost to the totalbuilding cost which can be selected as a subcondition of aparameter to make a scenario In this concept the basecondition weight can be defined for a base grade whichmeans the condition weight of each element to make thebasic scenario combination

conditionweight(CW) elemental cost

total cost()

base conditionweight(BCW)

elemental cost of a base grade

total cost()

(1)

Consequently the scenario impact is measured by ac-cumulating a combination of condition weights We definethe scenario impact (SI) and the base scenario impact (BSI)as the sum of parameter condition weights of a scenario asfollows

scenario impact(SI) 1113944n

i1CWi

base scenario impact(BSI) 1113944n

i1BCW

(2)

As a result a scenario matrix for the case study wasdeveloped (Table 8) e scenario impacts of this case studyrange from 754 to 1468 of the total project cost whichmeans the matrix can provide a 677 contingency on theproject total cost e sum of the variance of the top sixelementsrsquo condition is 744 which is over 97 of thescenario impact variance (766) e condition weightvariance is 233 for the living room floor boards 108 forthe bedroom and 091 for the main room In the door andwindow work the frame of the balcony has a conditionweight variance of 089 that of the window frame is 077and that of the glazing is 056

5 Results and Discussion

To evaluate the approach we compared it with a previousmodel [4] e difference between these two CBR processes

Table 3 Cost proportion analysis (interior finishing works)

Interior finishing workItems Cost proportion Percentage rankGypsum boards 01 11Ceiling boards 70 4Floorboards 542 1Wall finishes 82 3Ceiling finishes 36 8Moldings 37 7Dry partitioning 24 9Insulation board 48 5Internal insulation 39 6Acoustic board 14 10CRC boardlowast 107 2lowastCellulose fiber-reinforced concrete board

Table 4 Cost proportion analysis (door and window works)

Door and window workItems Cost proportion Percentage rankHousehold doors 108 4Household window frames 216 2Household window glazing 137 3Balcony window frames 280 1Balcony window glazing 88 6Other doors and windows(lobbies and stair halls) 95 5

Miscellaneous 76 7


is that one has a scenario-based add-on module Wecompared their predictions with the original model out-comes which are divided by the 1-NN 5-NN and 10-NNadaptation methods To evaluate the performance of eachmodel the absolute error ratios (AERs) were calculated

AER() CA minusCE gt 1 CA minusCE( 1113857minus 11113858 1113859 times 100

Otherwise 1minus CA minusCE( 11138571113858 1113859 times 1001113896 (3)

where CA and CE are the actual cost and estimated costrespectively

e experiment results (Table 9) show that the costmodels with the scenario module achieved higher estimationaccuracy and stability compared with the previous modele proposed model achieved an average accuracy of 247and stability of 554 when the 10-NN adaptation methodwas applied which yielded the highest accuracy among theadaption methods

In terms of accuracy by using the scenario module thenegative influence of the errors within the AER of 677 can

be absorbed (asterisk in Table 9) In other words the esti-mation results can be adjusted to zero error by applying thescenario module because it allows a 667 contingency andprovides chances for plausible alternatives Consequentlyestimation accuracies of 423 to 486 and stability en-hancements of 139 to 173 were observed

In terms of fast decision-making generally in planningor design stage a modification provoked by cost overrun isan examination of planning or design alternatives eearlier it happens the less information can be givenerefore a decision maker only has a macroperspectiveoption such as gross floor area or number of householdsHowever the suggested approach can provide a kind ofmicroperspective option such as finishing material changeswhich can confront more rapidly than conventional is isbecause a change of scenario or an examining of repre-sentative scenarios can make the given problem be simple orexcluded from decision-making issues For example a casestudy shows that a problem regarding cost overrun notexceeding 677 over total cost cannot be a decision-making

Table 5 Combination matrix (stone and tile work)

Stone and tile workBathroom Entrance floor Kitchen

Floor Wall Floor Joist WallGlazed ceramic tile(small size) SBF1

Glazed earthenware tile(small size) SBW1

Glazed ceramic tile(small size) SEF1 BMC plastic SEJ1 Glazed earthenware tile

(small size) SKW1Glazed ceramic tile(medium size) SBF2

Glazed earthenware tile(medium size) SBW2

High-strength mosaic tile(rectangle) SEF2 Mock marble SEJ2 Glazed earthenware tile

(rectangle) SKW2

Mock marble SEF3 Natural marble SEJ3 Glazed earthenware tile(medium size) SKW3

Natural marble SEF4

Table 6 Combination matrix (interior finishing work)

Interior finishing workFloorboards Wall finish Ceiling finish

Living room Main room Bedroom Living room Main room Bedroom Living room Main room BedroomLinoleumIFL1 Linoleum IFM1 Linoleum

IFB1 Silk IWL1 Silk IWM1 Silk IWB1 Ecofriendlypaper ICL3

Ecofriendlypaper ICM3 Paper ICB1

Laminatefloor IFL2

Laminatefloor IFM2

Laminatefloor IFB2

High-qualitysilk IWL2

High-qualitysilk IWM2

High-qualitysilk IWB2

Ecofriendlypaper ICL3

Ecofriendlypaper ICM3 Silk ICB2

Engineeredwood IFL3

Engineeredwood IFM3

Engineeredwood IFB3

Ecofriendlypaper IWL3

Ecofriendlypaper IWM3

Ecofriendlypaper IWB3 Silk ICL2 Silk ICM2 Ecofriendly

paper ICB3High-qualitysilk ICL4

High-qualitysilk ICM4

High-qualitysilkICB4

Table 7 Combination matrix (doors and windows work)

Doors and windows work

Household doorsHousehold windows Balcony windows

Glazing Frame Glazing FramePolyvinyl chlorideplastic DHD1 16mm pair glass DHG1 Single window DHF1 16mm pair glass DBG1 Single window DBF1

Natural wood DHD2 18mm pair glass DHG2 Double window DHF2 22mm pair glass DBG2 Double window DBF222mm pair glass DHG3 24mm low-E pair glass DBG3

22mm pair glass DBG4


Tabl

e8

Scenario

matrixforan

apartm

entprojectin

SouthKorea

Parameters

Scenario

impact

Trades

Ston

eandtilework

Interior

finish

ingwork

Doo

rsandwindo

wswork

Elem

ents

Bathroom

Entrance

Kitchen

Floo

rboards

Wallfi

nish

Ceilin

gfin

ishDoo

rsWindo

wBa

lcon

y

Floo

rWall

Floo

rJoist

Wall

Living

room

Main

room

Bedroom

Living

room

Main

room

Bedroom

Living

room

Main

room

Bedroom

Doo

rsGlazing

Fram

eGlazing

Fram

e

BCW

016

121

004

001

001

117

046

054

011

007

008

003

001

002

016

046

119

052

137

763

Cond

itions

ASubcon

ditio

nSB

F1SB

W1

SEF1

SEJ1

SKW1

IFL1

IFM1

IFB1

IWL3

IWM3

IWB3

ICL1

ICM1

ICB1

DHD1

DHG1

DHF1

DBG

1DBF

1CW

016

121

004

001

001

117

046

054

011

007

008

003

001

002

016

046

119

052

137

763

BSubcon

ditio

nSB

F2SB

W2

SEF2

SEJ2

SKW2

IFL2

IFM2

IFB2

IWL3

IWM3

IWB3

ICL2

ICM2

ICB2

DHD2

DHG2

DHF2

DBG

2DBF

2CW

018

130

004

002

002

284

111

131

014

008

010

006

003

003

018

054

195

066

226

1285

CSubcon

ditio

nSB

F2SB

W3

SEF3

SEJ3

SKW3

IFL3

IFM3

IFB3

IWL1

IWM1

IWB1

ICL3

ICM3

ICB3

DHD2

DHG3

DHF2

DBG

3DBF

2CW

018

129

009

002

001

350

137

162

007

004

005

004

002

002

018

058

195

109

226

1440

DSubcon

ditio

nSB

F2SB

W3

SEF4

SEJ3

SKW3

IFL3

IFM3

IFB3

IWL2

IWM2

IWB2

ICL4

ICM4

ICB4

DHD2

DHG3

DHF2

DBG

4DBF

2CW

018

129

015

002

001

350

137

162

007

004

005

008

003

004

018

058

195

066

226

1409

Analysis

Min

016

121

004

001

001

117

046

054

007

004

005

003

001

002

016

046

119

052

137

754

Max

018

130

015

002

002

350

137

162

014

008

010

008

003

004

018

058

195

109

226

1468

Variance

002

009

011

001

000

233

091

108

006

004

005

005

002

002

001

012

077

056

089

677


subject anymoreus when the proposedmethod is used ithelps decision-making by decreasing the number of prob-lems and removing them

6 Conclusions

We proposed the scenario-planning method for cost esti-mation and tested its feasibility by conducting a case studyMorphological analysis was introduced as an effectivemethodology to analyze the significance of parameters toclassify the parameters according to the elements and toquantify the conditions of the influence on the project costAccordingly we identified 11 elements of materials dividedinto 19 parameters that had an influence of about 15 on theproject cost in the case study of a public apartment in SouthKorea

We analyzed the combinations of available conditions ofparameters which were expressed by their conditionweights according to the elements e concept of thescenario impact was suggested to calculate the consequencesof each of the scenario combinations which can play a keyrole in prioritizing crucial factors that can be configurable ifa scenario must be changed To test the applicability wedeveloped a scenario matrix for the apartment project andcompared the estimation accuracy to a previous case studyAs a result both the accuracy and estimation stability wereimproved

e suggested process can produce adaptable scenariosand evaluate their impact in a complicated decision-makingprocess with limited information Furthermore it canprovide a kind of contingency plan to cushion against

uncertainty erefore this methodology can show a richand detailed portrait of plausible future results or a futurestate of a system that focuses on causal processes and de-cision points e research outcomes could support andfacilitate decision-making related to cost estimation forbeginners and experts in both academia and industryHowever it is necessary to verify the generalization byapplying the methodology to various kinds of constructionprojects besides apartment projects Future research willrequire the development and comparison of various tradessuch as structural work plastering and furniture work aswell as comparative studies with other methodologies be-sides CBR such as ANNs

Data Availability

edata generated or analyzed during the study are availablefrom the corresponding author upon request

Conflicts of Interest

e authors declare that they have no conflicts of interest

Acknowledgments

is work was supported by the National Research Foun-dation (NRF) grant funded by the Korean government andby the Technology Innovation Program (10077606) fundedby the Ministry of Trade Industry and Energy Republic ofKorea is research was also supported by the Institute ofConstruction and Environmental Engineering at SeoulNational University

References

[1] R L Ackoff 8e Effect of Reasoning Logics on Real-TimeDecision Making Redesigning the Future a Systems Approachto Societal Problems Wiley New York NY USA 1974

[2] G E G Beroggi and W A Wallace ldquoe effect of reasoninglogics on real-time decision makingrdquo IEEE Transactions onSystems Man and Cybernetics-Part A Systems and Humansvol 27 no 6 pp 743ndash749 1997

[3] S M Trost and G D Oberlender ldquoPredicting accuracy ofearly cost estimates using factor analysis and multivariateregressionrdquo Journal of Construction Engineering and Man-agement vol 129 no 2 pp 198ndash204 2003

[4] S-H Ji M Park and H-S Lee ldquoCost estimation model forbuilding projects using case-based reasoningrdquo CanadianJournal of Civil Engineering vol 38 no 5 pp 570ndash581 2011

[5] R J Kirkham Ferry and Brandonrsquos Cost Planning of BuildingsBlackwell publishing Hoboken NJ USA 8th edition 2007

[6] P Teicholz ldquoForecasting final cost and budget of constructionprojectsrdquo Journal of Computing in Civil Engineering vol 7no 4 pp 511ndash529 1993

[7] G D Oberlender and S M Trost ldquoPredicting accuracy ofearly cost estimates based on estimate qualityrdquo Journal ofConstruction Engineering and Management vol 127 no 3pp 173ndash182 2001

[8] T Hegazy and A Ayed ldquoNeural network model for para-metric cost estimation of highway projectsrdquo Journal ofConstruction Engineering and Management vol 124 no 3pp 210ndash218 1998

Table 9 Absolute error ratio (AER) comparison

AdaptPrevious model [3] Scenario-complemented

model1-NN 5-NN 10-NN 1-NN 5-NN 10-NN

Case 1 469 699 725 lowast lowast 011Case 2 908 679 658 194 lowast lowastCase 3 891 098 133 177 lowast lowastCase 4 458 987 924 lowast 273 210Case 5 415 801 333 lowast 087 lowastCase 6 667 165 205 lowast lowast lowastCase 7 4001 3001 2158 3287 2287 1444Case 8 213 071 271 lowast lowast lowastCase 9 490 111 102 lowast lowast lowastCase 10 2377 206 124 1663 lowast lowastCase 11 015 887 924 lowast 173 210Case 12 948 618 630 234 lowast lowastCase 13 187 180 079 lowast lowast lowastCase 14 596 052 012 lowast lowast lowastCase 15 075 396 613 lowast lowast lowastCase 16 1170 1699 1944 456 985 1230Case 17 1037 055 355 323 lowast lowastCase 18 107 593 174 lowast lowast lowastCase 19 311 245 569 lowast lowast lowastCase 20 2708 2592 2551 1994 1878 1837Mean 902 707 674 416 284 247SD 1014 829 727 874 658 554lowaste results can be modified to zero error by applying the scenario module


[9] T M S Elhag and A H Boussabaine ldquoAn artificial neuralsystem for cost estimation of construction projectsrdquo inProceedings of 14th ARCOM Annual Conference pp 219ndash226Reading UK September 1998

[10] H M Gunaydın and S Z Dogan ldquoA neural network ap-proach for early cost estimation of structural systems ofbuildingsrdquo International Journal of Project Managementvol 22 no 7 pp 595ndash602 2004

[11] C K Riesbeck and R C Schank Inside Case-Based ReasoningLawrence Earlbaum Hillsdale NJ USA 1989

[12] N J Yau and J B Yang ldquoCase-based reasoning in con-struction managementrdquo Computer-Aided Civil and Infra-structure Engineering vol 13 no 2 pp 143ndash150 1998

[13] S Z Dogan D Arditi and H M Gunaydın ldquoDeterminingattribute weights in a CBR model for early cost prediction ofstructural systemsrdquo Journal of Construction Engineering andManagement vol 132 no 10 pp 1092ndash1098 2006

[14] S-H An G-H Kim and K-I Kang ldquoA case-based reasoningcost estimating model using experience by analytic hierarchyprocessrdquo Building and Environment vol 42 no 7pp 2573ndash2579 2007

[15] C Koo T Hong C Hyun and K Koo ldquoA CBR-based hybridmodel for predicting a construction duration and cost basedon project characteristics in multi-family housing projectsrdquoCanadian Journal of Civil Engineering vol 37 no 5pp 739ndash752 2010

[16] C-W Koo T Hong C-T Hyun S H Park and J-o Seo ldquoAstudy on the development of a cost model based on theownerrsquos decision making at the early stages of a constructionprojectrdquo International Journal of Strategic Property Man-agement vol 14 no 2 pp 121ndash137 2010

[17] S-H Ji M Park and H-S Lee ldquoCase adaptation method ofcase-based reasoning for construction cost estimation inKoreardquo Journal of Construction Engineering and Manage-ment vol 138 no 1 pp 43ndash52 2012

[18] S-H Ji M Park H-S Lee J Ahn N Kim and B SonldquoMilitary facility cost estimation system using case-basedreasoning in Koreardquo Journal of Computing in Civil Engi-neering vol 25 no 3 pp 218ndash231 2011

[19] A O Elfaki S Alatawi and E Abushandi ldquoUsing intelligenttechniques in construction project cost estimation 10-yearsurveyrdquo Advances in Civil Engineering vol 2014 Article ID107926 11 pages 2014

[20] L Holm J E Schaufelberger D Griffin and T Cole Con-struction Cost Estimating Process and Practices PearsonEducation Upper Saddle River NJ USA 2005

[21] T Ritchey ldquoNuclear facilities and sabotage using morpho-logical analysis as a scenario and strategy development lab-oratoryrdquo in Proceedings of 44th Annual Meeting of theInstitute of Nuclear Materials Management Phoenix AZUSA July 2003

[22] T Ritchey ldquoFritz Zwicky ldquomorphologierdquo and policy analysisrdquoin Proceedings of 16th Euro Conference on OperationalAnalysis Brussels Belgium May 1998

[23] D Kang and K Lansey ldquoScenario-based multistage con-struction of water supply infrastructurerdquo in Proceedings ofWorld Environmental and Water Resources Congress Albu-querque NM USA May 2012

[24] P Schwartz 8e Art of the Long View DoubledayCurrencythe University of Michigan MI USA 1991

[25] C Zegras J Sussman and C Conklin ldquoScenario planning forstrategic regional transportation planningrdquo Journal of UrbanPlanning and Development vol 130 no 1 pp 2ndash13 2004


International Journal of

AerospaceEngineeringHindawiwwwhindawicom Volume 2018

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VLSI Design



Shock and Vibration


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Journal ofEngineeringVolume 2018

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Modelling ampSimulationin EngineeringHindawiwwwhindawicom Volume 2018


Chemical EngineeringInternational Journal of Antennas and

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Navigation and Observation


Hindawi

wwwhindawicom Volume 2018

Advances in

Multimedia

Submit your manuscripts atwwwhindawicom

decision-making by simulating current or future situations[6] e overall success of a construction project is oftenmeasured by how well the actual cost compares to the earlycost estimates [5] and numerous cost estimations are donerepeatedly especially in the early stages Cost models canprovide more reliable cost advice A good cost modelshould be simple accurate unbiased timely and stableenough to easily integrate it into the cost system Ac-cordingly the significance of cost estimation of a con-struction project for the whole life cycle cannot beunderestimated

Recent studies apply various methods to increase theaccuracy or confidence of cost estimation with focus on theinitial stages Conventionally a parametric method thatutilizes representative variables is widely used because it isquick and easy to iterate e method can reflect changes intime locations and productivity changes if the data arereliable (Barrie and Paulson) However the prediction ac-curacy is relatively low since the method does not reflectvarious factors that affect the construction cost

Artificial intelligence (AI) approaches are also beingemployed Previous research has used artificial neural net-works (ANNs) to predict the cost of highway construction[7] school buildings [8] and the structural system of abuilding [9] CBR has also received much attention as analternative method for estimating cost CBR is a process ofsolving problems by recognizing their similarity to past cases[10] CBR is more flexible than ANN in updating the systemand it is more successful in handling missing information

In recent years numerous studies have been conductedfor CBR cost estimation Yau and Yang [12] developed aCBR estimation method for the preliminary design stageDogan et al [13] proposed a CBR cost model for astructural system An et al [14] developed a CBR costmodel for a residential building using an analytic hierarchyprocess (AHP) Koo et al [15 16] developed a CBR-basedhybrid model for predicting the duration and cost of aconstruction project Ji et al [4 17 18] developed a CBRcost model and suggested a CBR cost estimation system forpublic projects

It is important to note that cost estimation is aknowledge-intensive engineering work and requires ex-pertise of the human professionals [18 20] However thereare difficulties in utilizing engineersrsquo expertise since expe-rienced knowledge is not often documented and thiseventually leads to subjectivity [18] erefore further re-search is required to compensate subjectivity of experts andsupport decision-making regarding cost estimation ofplausible alternatives in a quantitative manner

22 Morphological Analysis Morphology is the study of theshape and arrangement of parts of an object and how theseparts conform to create a whole or Gestalt [21] Morpho-logical analysis is a nonquantified modeling method forstructuring and analyzing technological organizational andsocial problems [21 22] Morphological analysis is used torepresent a problem using a matrix that has combinations ofparameter conditions e parameters on the horizontal axisrepresent components of the targeted object and the

conditions on the vertical axis describe the nature of eachparameter As shown in Figure 1 each of the parameters isshown in a column with the possible conditions as boxes inthe column In a given combination conditions are assignedto a parameter by highlighting the relevant conditions Forexample X1-Y3-Z3-W2 is one of the possible configurationsof this four-by-four matrix

Morphological analysis is used to construct scenarios byoutlining issues with identified driving forces One of the mostpowerful and intuitive ways to incorporate the uncertainties inthe planning stage is to use scenarios [23] Scenario planning isan imagined sequence of future events and can be used as a toolto help make more effective decisions To develop a scenarioSchwartz [24] proposed an eight-step scenario-planning pro-cess that focuses on identifying key factors their relationshipsand their impacts on future events Morphological analysiscould thus help to produce well-organized scenarios made upof parameters and conditions and to estimate the implicationsIn this study building elements were used as parameters andthe grades of finishing materials were used as conditions

3 Scenario-Planning Method

31 Framework Scenarios can be defined as an imaginedcombination of facts and relevant results and scenarioplanning can help to make robust strategic choices [25]Figure 2 shows the scenario-planning framework and ex-pected function in design process

e design process plays an important role in decision-making As shown in Figure 2 the cost estimate is usuallymade by the design stage Decision-making is being madethrough the design process and cost estimates are conductedin each design phases in general (Figure 2) In addition if theerror range of the estimated cost for each design phaseexceeds the budget it is necessary to review the scope de-fining in stage 1 in the worst case Under these circum-stances it is not easy to determine which stage to go back toand review Since the content and scope to be reviewed andthe amount of work to be required are significantly differentaccording to each design stage it is very important to makeproper decisions about to which stage to return If stage 5documentation is to be returned to stage 1 scope definingdue to huge mistakes that had not been discovered beforethen the costs and time required for redesign would beimmense Damages due to subsequent delays in constructionwill also be greater

As a complement to this the scenario-planning methodproposed in this study can present the criteria of themanageable limit of the cost overrun for each design stageis should be considered separately from the designcontingency Contingency is a pure countermeasure againstuncertainty in project progress whereas the scenario matrixof this study can be regarded as a response strategy because itmakes uncertainty a predictable risk that can be dealt with

For example there is a project manager Suppose that theproject heshe is managing is in the detailed design stage andthe cost estimate result has recently exceeded +10 At thispoint the project manager will be troubled It will be difficult todecide whether to go back to the scope definition stage and












X Y Z W

X1 Y1 Z1 W1

X2 Y2 Z2 W2

X3 Y3 Z3 W3

X4 Y4 Z4 W4

ParametersC

ondi

tions



Phases



A







10

Arsquo

11

A

B

10

Arsquo

Brsquo

11

A

1

B

10

Arsquo

1rsquoBrsquorsquo

11

-2-



scenarios



scenarios



(ii)

(i)(ii)

(i)

(i)

(i)

(i)

(i)

(i)

(i)

(ii)



Project

Interior finishing

Wall

Livi

ng ro

om

Bath

floo

r

Bedr

oom

Entr

ance

Wall


WnkWn

kndash1Wn

1ndash1W2

kW21W1

1









No Feasibilitytest

Yes

No Feasibilitytest

Yes

No Feasibilitytest

Yes

No Feasibilitytest


Stagemanageable

Stagemanageable

Stagemanageable

Stagemanageable















4 Case Study








Percentagerank













total cost()



total cost()

(1)



i1CWi


i1BCW

(2)








Miscellaneous 76 7


















(rectangle) SKW2


Natural marble SEF4






Laminatefloor IFL2

Laminatefloor IFM2

Laminatefloor IFB2






Engineeredwood IFL3

Engineeredwood IFM3

Engineeredwood IFB3














Tabl

e8

Scenario

matrixforan

apartm

entprojectin

SouthKorea

Parameters

Scenario

impact

Trades

Ston

eandtilework

Interior

finish

ingwork

Doo

rsandwindo

wswork

Elem

ents

Bathroom

Entrance

Kitchen

Floo

rboards

Wallfi

nish

Ceilin

gfin

ishDoo

rsWindo

wBa

lcon

y

Floo

rWall

Floo

rJoist

Wall

Living

room

Main

room

Bedroom

Living

room

Main

room

Bedroom

Living

room

Main

room

Bedroom

Doo

rsGlazing

Fram

eGlazing

Fram

e

BCW

016

121

004

001

001

117

046

054

011

007

008

003

001

002

016

046

119

052

137

763

Cond

itions

ASubcon

ditio

nSB

F1SB

W1

SEF1

SEJ1

SKW1

IFL1

IFM1

IFB1

IWL3

IWM3

IWB3

ICL1

ICM1

ICB1

DHD1

DHG1

DHF1

DBG

1DBF

1CW

016

121

004

001

001

117

046

054

011

007

008

003

001

002

016

046

119

052

137

763

BSubcon

ditio

nSB

F2SB

W2

SEF2

SEJ2

SKW2

IFL2

IFM2

IFB2

IWL3

IWM3

IWB3

ICL2

ICM2

ICB2

DHD2

DHG2

DHF2

DBG

2DBF

2CW

018

130

004

002

002

284

111

131

014

008

010

006

003

003

018

054

195

066

226

1285

CSubcon

ditio

nSB

F2SB

W3

SEF3

SEJ3

SKW3

IFL3

IFM3

IFB3

IWL1

IWM1

IWB1

ICL3

ICM3

ICB3

DHD2

DHG3

DHF2

DBG

3DBF

2CW

018

129

009

002

001

350

137

162

007

004

005

004

002

002

018

058

195

109

226

1440

DSubcon

ditio

nSB

F2SB

W3

SEF4

SEJ3

SKW3

IFL3

IFM3

IFB3

IWL2

IWM2

IWB2

ICL4

ICM4

ICB4

DHD2

DHG3

DHF2

DBG

4DBF

2CW

018

129

015

002

001

350

137

162

007

004

005

008

003

004

018

058

195

066

226

1409

Analysis

Min

016

121

004

001

001

117

046

054

007

004

005

003

001

002

016

046

119

052

137

754

Max

018

130

015

002

002

350

137

162

014

008

010

008

003

004

018

058

195

109

226

1468

Variance

002

009

011

001

000

233

091

108

006

004

005

005

002

002

001

012

077

056

089

677



6 Conclusions





Data Availability




Acknowledgments


References


































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia












X Y Z W

X1 Y1 Z1 W1

X2 Y2 Z2 W2

X3 Y3 Z3 W3

X4 Y4 Z4 W4

ParametersC

ondi

tions



Phases



A







10

Arsquo

11

A

B

10

Arsquo

Brsquo

11

A

1

B

10

Arsquo

1rsquoBrsquorsquo

11

-2-



scenarios



scenarios



(ii)

(i)(ii)

(i)

(i)

(i)

(i)

(i)

(i)

(i)

(ii)



Project

Interior finishing

Wall

Livi

ng ro

om

Bath

floo

r

Bedr

oom

Entr

ance

Wall


WnkWn

kndash1Wn

1ndash1W2

kW21W1

1









No Feasibilitytest

Yes

No Feasibilitytest

Yes

No Feasibilitytest

Yes

No Feasibilitytest


Stagemanageable

Stagemanageable

Stagemanageable

Stagemanageable















4 Case Study








Percentagerank













total cost()



total cost()

(1)



i1CWi


i1BCW

(2)








Miscellaneous 76 7


















(rectangle) SKW2


Natural marble SEF4






Laminatefloor IFL2

Laminatefloor IFM2

Laminatefloor IFB2






Engineeredwood IFL3

Engineeredwood IFM3

Engineeredwood IFB3














Tabl

e8

Scenario

matrixforan

apartm

entprojectin

SouthKorea

Parameters

Scenario

impact

Trades

Ston

eandtilework

Interior

finish

ingwork

Doo

rsandwindo

wswork

Elem

ents

Bathroom

Entrance

Kitchen

Floo

rboards

Wallfi

nish

Ceilin

gfin

ishDoo

rsWindo

wBa

lcon

y

Floo

rWall

Floo

rJoist

Wall

Living

room

Main

room

Bedroom

Living

room

Main

room

Bedroom

Living

room

Main

room

Bedroom

Doo

rsGlazing

Fram

eGlazing

Fram

e

BCW

016

121

004

001

001

117

046

054

011

007

008

003

001

002

016

046

119

052

137

763

Cond

itions

ASubcon

ditio

nSB

F1SB

W1

SEF1

SEJ1

SKW1

IFL1

IFM1

IFB1

IWL3

IWM3

IWB3

ICL1

ICM1

ICB1

DHD1

DHG1

DHF1

DBG

1DBF

1CW

016

121

004

001

001

117

046

054

011

007

008

003

001

002

016

046

119

052

137

763

BSubcon

ditio

nSB

F2SB

W2

SEF2

SEJ2

SKW2

IFL2

IFM2

IFB2

IWL3

IWM3

IWB3

ICL2

ICM2

ICB2

DHD2

DHG2

DHF2

DBG

2DBF

2CW

018

130

004

002

002

284

111

131

014

008

010

006

003

003

018

054

195

066

226

1285

CSubcon

ditio

nSB

F2SB

W3

SEF3

SEJ3

SKW3

IFL3

IFM3

IFB3

IWL1

IWM1

IWB1

ICL3

ICM3

ICB3

DHD2

DHG3

DHF2

DBG

3DBF

2CW

018

129

009

002

001

350

137

162

007

004

005

004

002

002

018

058

195

109

226

1440

DSubcon

ditio

nSB

F2SB

W3

SEF4

SEJ3

SKW3

IFL3

IFM3

IFB3

IWL2

IWM2

IWB2

ICL4

ICM4

ICB4

DHD2

DHG3

DHF2

DBG

4DBF

2CW

018

129

015

002

001

350

137

162

007

004

005

008

003

004

018

058

195

066

226

1409

Analysis

Min

016

121

004

001

001

117

046

054

007

004

005

003

001

002

016

046

119

052

137

754

Max

018

130

015

002

002

350

137

162

014

008

010

008

003

004

018

058

195

109

226

1468

Variance

002

009

011

001

000

233

091

108

006

004

005

005

002

002

001

012

077

056

089

677



6 Conclusions





Data Availability




Acknowledgments


References


































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia


Phases



A







10

Arsquo

11

A

B

10

Arsquo

Brsquo

11

A

1

B

10

Arsquo

1rsquoBrsquorsquo

11

-2-



scenarios



scenarios



(ii)

(i)(ii)

(i)

(i)

(i)

(i)

(i)

(i)

(i)

(ii)



Project

Interior finishing

Wall

Livi

ng ro

om

Bath

floo

r

Bedr

oom

Entr

ance

Wall


WnkWn

kndash1Wn

1ndash1W2

kW21W1

1









No Feasibilitytest

Yes

No Feasibilitytest

Yes

No Feasibilitytest

Yes

No Feasibilitytest


Stagemanageable

Stagemanageable

Stagemanageable

Stagemanageable















4 Case Study








Percentagerank













total cost()



total cost()

(1)



i1CWi


i1BCW

(2)








Miscellaneous 76 7


















(rectangle) SKW2


Natural marble SEF4






Laminatefloor IFL2

Laminatefloor IFM2

Laminatefloor IFB2






Engineeredwood IFL3

Engineeredwood IFM3

Engineeredwood IFB3














Tabl

e8

Scenario

matrixforan

apartm

entprojectin

SouthKorea

Parameters

Scenario

impact

Trades

Ston

eandtilework

Interior

finish

ingwork

Doo

rsandwindo

wswork

Elem

ents

Bathroom

Entrance

Kitchen

Floo

rboards

Wallfi

nish

Ceilin

gfin

ishDoo

rsWindo

wBa

lcon

y

Floo

rWall

Floo

rJoist

Wall

Living

room

Main

room

Bedroom

Living

room

Main

room

Bedroom

Living

room

Main

room

Bedroom

Doo

rsGlazing

Fram

eGlazing

Fram

e

BCW

016

121

004

001

001

117

046

054

011

007

008

003

001

002

016

046

119

052

137

763

Cond

itions

ASubcon

ditio

nSB

F1SB

W1

SEF1

SEJ1

SKW1

IFL1

IFM1

IFB1

IWL3

IWM3

IWB3

ICL1

ICM1

ICB1

DHD1

DHG1

DHF1

DBG

1DBF

1CW

016

121

004

001

001

117

046

054

011

007

008

003

001

002

016

046

119

052

137

763

BSubcon

ditio

nSB

F2SB

W2

SEF2

SEJ2

SKW2

IFL2

IFM2

IFB2

IWL3

IWM3

IWB3

ICL2

ICM2

ICB2

DHD2

DHG2

DHF2

DBG

2DBF

2CW

018

130

004

002

002

284

111

131

014

008

010

006

003

003

018

054

195

066

226

1285

CSubcon

ditio

nSB

F2SB

W3

SEF3

SEJ3

SKW3

IFL3

IFM3

IFB3

IWL1

IWM1

IWB1

ICL3

ICM3

ICB3

DHD2

DHG3

DHF2

DBG

3DBF

2CW

018

129

009

002

001

350

137

162

007

004

005

004

002

002

018

058

195

109

226

1440

DSubcon

ditio

nSB

F2SB

W3

SEF4

SEJ3

SKW3

IFL3

IFM3

IFB3

IWL2

IWM2

IWB2

ICL4

ICM4

ICB4

DHD2

DHG3

DHF2

DBG

4DBF

2CW

018

129

015

002

001

350

137

162

007

004

005

008

003

004

018

058

195

066

226

1409

Analysis

Min

016

121

004

001

001

117

046

054

007

004

005

003

001

002

016

046

119

052

137

754

Max

018

130

015

002

002

350

137

162

014

008

010

008

003

004

018

058

195

109

226

1468

Variance

002

009

011

001

000

233

091

108

006

004

005

005

002

002

001

012

077

056

089

677



6 Conclusions





Data Availability




Acknowledgments


References


































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia




4 Case Study








Percentagerank













total cost()



total cost()

(1)



i1CWi


i1BCW

(2)








Miscellaneous 76 7


















(rectangle) SKW2


Natural marble SEF4






Laminatefloor IFL2

Laminatefloor IFM2

Laminatefloor IFB2






Engineeredwood IFL3

Engineeredwood IFM3

Engineeredwood IFB3














Tabl

e8

Scenario

matrixforan

apartm

entprojectin

SouthKorea

Parameters

Scenario

impact

Trades

Ston

eandtilework

Interior

finish

ingwork

Doo

rsandwindo

wswork

Elem

ents

Bathroom

Entrance

Kitchen

Floo

rboards

Wallfi

nish

Ceilin

gfin

ishDoo

rsWindo

wBa

lcon

y

Floo

rWall

Floo

rJoist

Wall

Living

room

Main

room

Bedroom

Living

room

Main

room

Bedroom

Living

room

Main

room

Bedroom

Doo

rsGlazing

Fram

eGlazing

Fram

e

BCW

016

121

004

001

001

117

046

054

011

007

008

003

001

002

016

046

119

052

137

763

Cond

itions

ASubcon

ditio

nSB

F1SB

W1

SEF1

SEJ1

SKW1

IFL1

IFM1

IFB1

IWL3

IWM3

IWB3

ICL1

ICM1

ICB1

DHD1

DHG1

DHF1

DBG

1DBF

1CW

016

121

004

001

001

117

046

054

011

007

008

003

001

002

016

046

119

052

137

763

BSubcon

ditio

nSB

F2SB

W2

SEF2

SEJ2

SKW2

IFL2

IFM2

IFB2

IWL3

IWM3

IWB3

ICL2

ICM2

ICB2

DHD2

DHG2

DHF2

DBG

2DBF

2CW

018

130

004

002

002

284

111

131

014

008

010

006

003

003

018

054

195

066

226

1285

CSubcon

ditio

nSB

F2SB

W3

SEF3

SEJ3

SKW3

IFL3

IFM3

IFB3

IWL1

IWM1

IWB1

ICL3

ICM3

ICB3

DHD2

DHG3

DHF2

DBG

3DBF

2CW

018

129

009

002

001

350

137

162

007

004

005

004

002

002

018

058

195

109

226

1440

DSubcon

ditio

nSB

F2SB

W3

SEF4

SEJ3

SKW3

IFL3

IFM3

IFB3

IWL2

IWM2

IWB2

ICL4

ICM4

ICB4

DHD2

DHG3

DHF2

DBG

4DBF

2CW

018

129

015

002

001

350

137

162

007

004

005

008

003

004

018

058

195

066

226

1409

Analysis

Min

016

121

004

001

001

117

046

054

007

004

005

003

001

002

016

046

119

052

137

754

Max

018

130

015

002

002

350

137

162

014

008

010

008

003

004

018

058

195

109

226

1468

Variance

002

009

011

001

000

233

091

108

006

004

005

005

002

002

001

012

077

056

089

677



6 Conclusions





Data Availability




Acknowledgments


References


































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia









total cost()



total cost()

(1)



i1CWi


i1BCW

(2)








Miscellaneous 76 7


















(rectangle) SKW2


Natural marble SEF4






Laminatefloor IFL2

Laminatefloor IFM2

Laminatefloor IFB2






Engineeredwood IFL3

Engineeredwood IFM3

Engineeredwood IFB3














Tabl

e8

Scenario

matrixforan

apartm

entprojectin

SouthKorea

Parameters

Scenario

impact

Trades

Ston

eandtilework

Interior

finish

ingwork

Doo

rsandwindo

wswork

Elem

ents

Bathroom

Entrance

Kitchen

Floo

rboards

Wallfi

nish

Ceilin

gfin

ishDoo

rsWindo

wBa

lcon

y

Floo

rWall

Floo

rJoist

Wall

Living

room

Main

room

Bedroom

Living

room

Main

room

Bedroom

Living

room

Main

room

Bedroom

Doo

rsGlazing

Fram

eGlazing

Fram

e

BCW

016

121

004

001

001

117

046

054

011

007

008

003

001

002

016

046

119

052

137

763

Cond

itions

ASubcon

ditio

nSB

F1SB

W1

SEF1

SEJ1

SKW1

IFL1

IFM1

IFB1

IWL3

IWM3

IWB3

ICL1

ICM1

ICB1

DHD1

DHG1

DHF1

DBG

1DBF

1CW

016

121

004

001

001

117

046

054

011

007

008

003

001

002

016

046

119

052

137

763

BSubcon

ditio

nSB

F2SB

W2

SEF2

SEJ2

SKW2

IFL2

IFM2

IFB2

IWL3

IWM3

IWB3

ICL2

ICM2

ICB2

DHD2

DHG2

DHF2

DBG

2DBF

2CW

018

130

004

002

002

284

111

131

014

008

010

006

003

003

018

054

195

066

226

1285

CSubcon

ditio

nSB

F2SB

W3

SEF3

SEJ3

SKW3

IFL3

IFM3

IFB3

IWL1

IWM1

IWB1

ICL3

ICM3

ICB3

DHD2

DHG3

DHF2

DBG

3DBF

2CW

018

129

009

002

001

350

137

162

007

004

005

004

002

002

018

058

195

109

226

1440

DSubcon

ditio

nSB

F2SB

W3

SEF4

SEJ3

SKW3

IFL3

IFM3

IFB3

IWL2

IWM2

IWB2

ICL4

ICM4

ICB4

DHD2

DHG3

DHF2

DBG

4DBF

2CW

018

129

015

002

001

350

137

162

007

004

005

008

003

004

018

058

195

066

226

1409

Analysis

Min

016

121

004

001

001

117

046

054

007

004

005

003

001

002

016

046

119

052

137

754

Max

018

130

015

002

002

350

137

162

014

008

010

008

003

004

018

058

195

109

226

1468

Variance

002

009

011

001

000

233

091

108

006

004

005

005

002

002

001

012

077

056

089

677



6 Conclusions





Data Availability




Acknowledgments


References


































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia


















(rectangle) SKW2


Natural marble SEF4






Laminatefloor IFL2

Laminatefloor IFM2

Laminatefloor IFB2






Engineeredwood IFL3

Engineeredwood IFM3

Engineeredwood IFB3














Tabl

e8

Scenario

matrixforan

apartm

entprojectin

SouthKorea

Parameters

Scenario

impact

Trades

Ston

eandtilework

Interior

finish

ingwork

Doo

rsandwindo

wswork

Elem

ents

Bathroom

Entrance

Kitchen

Floo

rboards

Wallfi

nish

Ceilin

gfin

ishDoo

rsWindo

wBa

lcon

y

Floo

rWall

Floo

rJoist

Wall

Living

room

Main

room

Bedroom

Living

room

Main

room

Bedroom

Living

room

Main

room

Bedroom

Doo

rsGlazing

Fram

eGlazing

Fram

e

BCW

016

121

004

001

001

117

046

054

011

007

008

003

001

002

016

046

119

052

137

763

Cond

itions

ASubcon

ditio

nSB

F1SB

W1

SEF1

SEJ1

SKW1

IFL1

IFM1

IFB1

IWL3

IWM3

IWB3

ICL1

ICM1

ICB1

DHD1

DHG1

DHF1

DBG

1DBF

1CW

016

121

004

001

001

117

046

054

011

007

008

003

001

002

016

046

119

052

137

763

BSubcon

ditio

nSB

F2SB

W2

SEF2

SEJ2

SKW2

IFL2

IFM2

IFB2

IWL3

IWM3

IWB3

ICL2

ICM2

ICB2

DHD2

DHG2

DHF2

DBG

2DBF

2CW

018

130

004

002

002

284

111

131

014

008

010

006

003

003

018

054

195

066

226

1285

CSubcon

ditio

nSB

F2SB

W3

SEF3

SEJ3

SKW3

IFL3

IFM3

IFB3

IWL1

IWM1

IWB1

ICL3

ICM3

ICB3

DHD2

DHG3

DHF2

DBG

3DBF

2CW

018

129

009

002

001

350

137

162

007

004

005

004

002

002

018

058

195

109

226

1440

DSubcon

ditio

nSB

F2SB

W3

SEF4

SEJ3

SKW3

IFL3

IFM3

IFB3

IWL2

IWM2

IWB2

ICL4

ICM4

ICB4

DHD2

DHG3

DHF2

DBG

4DBF

2CW

018

129

015

002

001

350

137

162

007

004

005

008

003

004

018

058

195

066

226

1409

Analysis

Min

016

121

004

001

001

117

046

054

007

004

005

003

001

002

016

046

119

052

137

754

Max

018

130

015

002

002

350

137

162

014

008

010

008

003

004

018

058

195

109

226

1468

Variance

002

009

011

001

000

233

091

108

006

004

005

005

002

002

001

012

077

056

089

677



6 Conclusions





Data Availability




Acknowledgments


References


































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia


Tabl

e8

Scenario

matrixforan

apartm

entprojectin

SouthKorea

Parameters

Scenario

impact

Trades

Ston

eandtilework

Interior

finish

ingwork

Doo

rsandwindo

wswork

Elem

ents

Bathroom

Entrance

Kitchen

Floo

rboards

Wallfi

nish

Ceilin

gfin

ishDoo

rsWindo

wBa

lcon

y

Floo

rWall

Floo

rJoist

Wall

Living

room

Main

room

Bedroom

Living

room

Main

room

Bedroom

Living

room

Main

room

Bedroom

Doo

rsGlazing

Fram

eGlazing

Fram

e

BCW

016

121

004

001

001

117

046

054

011

007

008

003

001

002

016

046

119

052

137

763

Cond

itions

ASubcon

ditio

nSB

F1SB

W1

SEF1

SEJ1

SKW1

IFL1

IFM1

IFB1

IWL3

IWM3

IWB3

ICL1

ICM1

ICB1

DHD1

DHG1

DHF1

DBG

1DBF

1CW

016

121

004

001

001

117

046

054

011

007

008

003

001

002

016

046

119

052

137

763

BSubcon

ditio

nSB

F2SB

W2

SEF2

SEJ2

SKW2

IFL2

IFM2

IFB2

IWL3

IWM3

IWB3

ICL2

ICM2

ICB2

DHD2

DHG2

DHF2

DBG

2DBF

2CW

018

130

004

002

002

284

111

131

014

008

010

006

003

003

018

054

195

066

226

1285

CSubcon

ditio

nSB

F2SB

W3

SEF3

SEJ3

SKW3

IFL3

IFM3

IFB3

IWL1

IWM1

IWB1

ICL3

ICM3

ICB3

DHD2

DHG3

DHF2

DBG

3DBF

2CW

018

129

009

002

001

350

137

162

007

004

005

004

002

002

018

058

195

109

226

1440

DSubcon

ditio

nSB

F2SB

W3

SEF4

SEJ3

SKW3

IFL3

IFM3

IFB3

IWL2

IWM2

IWB2

ICL4

ICM4

ICB4

DHD2

DHG3

DHF2

DBG

4DBF

2CW

018

129

015

002

001

350

137

162

007

004

005

008

003

004

018

058

195

066

226

1409

Analysis

Min

016

121

004

001

001

117

046

054

007

004

005

003

001

002

016

046

119

052

137

754

Max

018

130

015

002

002

350

137

162

014

008

010

008

003

004

018

058

195

109

226

1468

Variance

002

009

011

001

000

233

091

108

006

004

005

005

002

002

001

012

077

056

089

677



6 Conclusions





Data Availability




Acknowledgments


References


































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia



6 Conclusions





Data Availability




Acknowledgments


References


































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia






















RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia


scenario-planningmethodforcostestimationusing...

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