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    Conceptual Estimation of Construction Costs Using the Multistep Ahead Approach

    Source: Journal of Construction Engineering and Management:;2016:;Volume ( 142 ):;issue: 009
    Author:
    Onur Dursun
    ,
    Christian Stoy
    DOI: 10.1061/(ASCE)CO.1943-7862.0001150
    Publisher: American Society of Civil Engineers
    Abstract: Providing accurate forecasts of construction costs at the conceptual phases of building projects is vital since they form an objective benchmark for the subsequent evaluation of project performance. Previous works adopted a conventional approach in which a restricted set of macro project determinants, which are available in the preplanning phase, was employed towards direct estimation of construction costs. Aiming to reduce the prediction error in conceptual estimates, the current study adopts a novel approach from the domain of forecasting. This multistep ahead (MSA) approach relies on the idea of using several cascaded estimations to predict future values. Accordingly, building element quantities were estimated as the first step. In the second step, estimated quantities were combined with the existing set of inputs to achieve a higher accuracy in construction cost prediction. In order to test the hypotheses of interest, 657 building projects from Germany were analyzed using linear regression and artificial neural network methods. Conclusive evidence suggests that the MSA approach significantly outperforms the prediction accuracy of the conventional practice. To the best of authors’ investigation, the current study is the first to offer such a cascaded estimation approach. Therefore, further empirical evidence is necessary prior to generalizing applicability of the MSA approach in construction cost estimation.
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      Conceptual Estimation of Construction Costs Using the Multistep Ahead Approach

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    https://yetl.yabesh.ir/yetl1/handle/yetl/83310
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    contributor authorOnur Dursun
    contributor authorChristian Stoy
    date accessioned2017-05-08T22:35:53Z
    date available2017-05-08T22:35:53Z
    date copyrightSeptember 2016
    date issued2016
    identifier other51322919.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/83310
    description abstractProviding accurate forecasts of construction costs at the conceptual phases of building projects is vital since they form an objective benchmark for the subsequent evaluation of project performance. Previous works adopted a conventional approach in which a restricted set of macro project determinants, which are available in the preplanning phase, was employed towards direct estimation of construction costs. Aiming to reduce the prediction error in conceptual estimates, the current study adopts a novel approach from the domain of forecasting. This multistep ahead (MSA) approach relies on the idea of using several cascaded estimations to predict future values. Accordingly, building element quantities were estimated as the first step. In the second step, estimated quantities were combined with the existing set of inputs to achieve a higher accuracy in construction cost prediction. In order to test the hypotheses of interest, 657 building projects from Germany were analyzed using linear regression and artificial neural network methods. Conclusive evidence suggests that the MSA approach significantly outperforms the prediction accuracy of the conventional practice. To the best of authors’ investigation, the current study is the first to offer such a cascaded estimation approach. Therefore, further empirical evidence is necessary prior to generalizing applicability of the MSA approach in construction cost estimation.
    publisherAmerican Society of Civil Engineers
    titleConceptual Estimation of Construction Costs Using the Multistep Ahead Approach
    typeJournal Paper
    journal volume142
    journal issue9
    journal titleJournal of Construction Engineering and Management
    identifier doi10.1061/(ASCE)CO.1943-7862.0001150
    treeJournal of Construction Engineering and Management:;2016:;Volume ( 142 ):;issue: 009
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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