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dc.contributor.authorAlqahtani, Ayedh
dc.contributor.authorWhyte, Andrew
dc.identifier.citationAlqahtani, A. and Whyte, A. 2016. Estimation of life-cycle costs of buildings: regression vs artificial neural network. Built Environment Project and Asset Management. 6 (1): pp. 30-43.

Purpose – The purpose of this paper is to compare the performance of regression and artificial-neural-networks (ANNs) methods to estimate the running cost of building projects towards improved accuracy. Design/methodology/approach – A data set of 20 building projects is used to test the performance of these two (ANNs/regression) models in estimating running cost. The concept of cost-significant-items is identified as important in assisting estimation. In addition, a stepwise technique is used to eliminate insignificant factors in regression modelling. A connection weight method is applied to determine the importance of cost factors in the performance of ANNs. Findings – The results illustrate that the value of the coefficient of determination=99.75 per cent for ANNs model(s), with a value of 98.1 per cent utilising multiple regression (MR) model(s); second, the mean percentage error (MPE) for ANNs at a testing stage is 0.179, which is less than that of the MPE gained through MR modelling of 1.28; and third, the average accuracy is 99 per cent for ANNs model(s) and 97 per cent for MR model(s). On the basis of these results, it is concluded that an ANNs model is superior to a MR model when predicting running cost of building projects. Research limitations/implications – A means for continuous improvement for the performance of the models accuracy has been established; this may be further enhanced by future extended sample. Originality/value – This work extends the knowledge base of life-cycle estimation where ANNs method has been found to reduce preparation time consumed and increasing accuracy improvement of the cost estimation.

dc.publisherEmerald Group Publishing
dc.titleEstimation of life-cycle costs of buildings: regression vs artificial neural network
dc.typeJournal Article
dcterms.source.titleBuilt Environment Project and Asset Management
curtin.departmentDepartment of Civil Engineering
curtin.accessStatusOpen access

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