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Data modelling and the application of a neural network approach to the prediction of total construction costs

Author

Listed:
  • Margaret Emsley
  • David Lowe
  • A. Roy Duff
  • Anthony Harding
  • Adam Hickson

Abstract

Neural network cost models have been developed using data collected from nearly 300 building projects. Data were collected from predominantly primary sources using real-life data contained in project files, with some data obtained from the Building Cost Information Service, supplemented with further information, and some from a questionnaire distributed nationwide. The data collected included final account sums and, so that the model could evaluate the total cost to the client, clients' external and internal costs, in addition to construction costs. Models based on linear regression techniques have been used as a benchmark for evaluation of the neural network models. The results showed that the major benefit of the neural network approach was the ability of neural networks to model the nonlinearity in the data. The 'best' model obtained so far gives a mean absolute percentage error (MAPE) of 16.6%, which includes a percentage (unknown) for client changes. This compares favourably with traditional estimating where values of MAPE between 20.8% and 27.9% have been reported. However, it is anticipated that further analyses will result in the development of even more reliable models.

Suggested Citation

  • Margaret Emsley & David Lowe & A. Roy Duff & Anthony Harding & Adam Hickson, 2002. "Data modelling and the application of a neural network approach to the prediction of total construction costs," Construction Management and Economics, Taylor & Francis Journals, vol. 20(6), pages 465-472.
    • Handle: RePEc:taf:conmgt:v:20:y:2002:i:6:p:465-472
    DOI: 10.1080/01446190210151050
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    Cited by:

    1. Hakami Waled & Hassan Awad, 2019. "Preliminary Construction Cost Estimate in Yemen by Artificial Neural Network," Baltic Journal of Real Estate Economics and Construction Management, Sciendo, vol. 7(1), pages 110-122, January.
    2. Phattara Khumprom & Nita Yodo, 2019. "A Data-Driven Predictive Prognostic Model for Lithium-ion Batteries based on a Deep Learning Algorithm," Energies, MDPI, vol. 12(4), pages 1-21, February.
    3. Chou, Jui-Sheng & Tai, Yian & Chang, Lian-Ji, 2010. "Predicting the development cost of TFT-LCD manufacturing equipment with artificial intelligence models," International Journal of Production Economics, Elsevier, vol. 128(1), pages 339-350, November.
    4. Zangeneh, Pouya & McCabe, Brenda, 2022. "Modelling socio-technical risks of industrial megaprojects using Bayesian Networks and reference classes," Resources Policy, Elsevier, vol. 79(C).
    5. Agnieszka Leśniak & Krzysztof Zima, 2018. "Cost Calculation of Construction Projects Including Sustainability Factors Using the Case Based Reasoning (CBR) Method," Sustainability, MDPI, vol. 10(5), pages 1-14, May.
    6. Wei Tong Chen & Ying-Hua Huang, 2006. "Approximately predicting the cost and duration of school reconstruction projects in Taiwan," Construction Management and Economics, Taylor & Francis Journals, vol. 24(12), pages 1231-1239.
    7. Ivan Damnjanovic & Xue Zhou, 2009. "Impact of crude oil market behaviour on unit bid prices: the evidence from the highway construction sector," Construction Management and Economics, Taylor & Francis Journals, vol. 27(9), pages 881-890.
    8. Swei, Omar & Gregory, Jeremy & Kirchain, Randolph, 2017. "Construction cost estimation: A parametric approach for better estimates of expected cost and variation," Transportation Research Part B: Methodological, Elsevier, vol. 101(C), pages 295-305.
    9. Qiao, Yu & Fricker, Jon D. & Labi, Samuel, 2019. "Effects of bundling policy on project cost under market uncertainty: A comparison across different highway project types," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 606-625.

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