Estimating the running costs of commercial buildings: artificial neural network modeling

Hemba Geekiyanage, MD ORCID: https://orcid.org/0000-0002-7892-8445 and Ramachandra, T 2019, Estimating the running costs of commercial buildings: artificial neural network modeling , in: 10th International Structural Engineering and Construction Conference, ISEC 2019, 20 May 2019 - 25 May 2019, Chicago, USA.

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Abstract

Running costs of a building is a substantial share of its total life-cycle cost (LCC) and it ranges between 70-80% in commercial buildings. Despite its significant contribution to LCC, investors and construction industry practitioners tend to mostly rely on construction cost exclusively. Though the early stage estimation of running costs is limited due to the unavailability of historical cost data, several efforts have been taken to estimate the running costs of buildings using different cost estimation techniques. However, the prediction accuracy of those models is still challenged due to less quality and amount of data employed. This study, therefore, developed an artificial neural network (ANN) model for running costs estimation of commercial buildings with the use of building design variables. The study was quantitively approached and running costs data together with 13 building design variables were collected from 35 commercial buildings. The ANN model developed resulted in a 96.6% perfect correlation between the running cost and building design variables. The testing and validation of the model developed indicate that there is greater prediction accuracy. These findings will enable industry practitioners to make informed cost decisions on implications of running costs in commercial buildings at its early stages, eliminating excessive costs to be incurred during the operational phase.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Schools: Schools > School of the Built Environment
Journal or Publication Title: ISEC 2019 - 10th International Structural Engineering and Construction Conference
Publisher: ISEC Press
Funders: Senate Research Committee of University of Moratuwa
Depositing User: Devindi Geekiyanage
Date Deposited: 12 Oct 2022 08:12
Last Modified: 12 Oct 2022 08:12
URI: https://usir.salford.ac.uk/id/eprint/65204

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