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Forecasting Construction Cost Index through Artificial Intelligence

Author

Listed:
  • Bilal Aslam

    (School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ 86011, USA)

  • Ahsen Maqsoom

    (Department of Civil Engineering, COMSATS University Islamabad, Wah Cantt 47040, Pakistan)

  • Hina Inam

    (College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Rawalpindi 44000, Pakistan)

  • Mubeen ul Basharat

    (Department of Computer Science and Engineering, HITEC University, Taxila 47080, Pakistan)

  • Fahim Ullah

    (School of Surveying and Built Environment, University of Southern Queensland, Springfield, QLD 4300, Australia)

Abstract

This study presents a novel approach for forecasting the construction cost index (CCI) of building materials in developing countries. Such estimations are challenging due to the need for a longer time, the influence of inflation, and fluctuating project prices in developing countries. This study used three techniques—a modified Artificial Neural Network (ANN), time series, and linear regression—to predict and forecast the local building material CCI in Pakistan. The predicted CCI is based on materials, including bricks, steel, cement, sand, and gravel. In addition, the swish activation function was introduced to increase the accuracy of the associated algorithms. The results suggest that the ANN model has superior prediction results, with the lowest Mean Error (ME), Mean Absolute Error (MAE), and Theil’s U statistic (U-Stat) values of 0.04, 28.3, and 0.62, respectively. The time series and regression models have ME values of 0.22 and 0.3, MAE values of 30.07 and 28.3, and U-Stat values of 0.65 and 0.64, respectively. The proposed models can assist contractors, project managers, and owners through an accurately estimated cost index. Such accurate CCIs help correctly estimate project budgets based on building material prices to mitigate project risks, delays, and failures.

Suggested Citation

  • Bilal Aslam & Ahsen Maqsoom & Hina Inam & Mubeen ul Basharat & Fahim Ullah, 2023. "Forecasting Construction Cost Index through Artificial Intelligence," Societies, MDPI, vol. 13(10), pages 1-15, October.
    • Handle: RePEc:gam:jsoctx:v:13:y:2023:i:10:p:219-:d:1257307
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    References listed on IDEAS

    as
    1. James Wong & Albert Chan & Y. H. Chiang, 2005. "Time series forecasts of the construction labour market in Hong Kong: the Box-Jenkins approach," Construction Management and Economics, Taylor & Francis Journals, vol. 23(9), pages 979-991.
    2. Salman Atif & Muhammad Umar & Fahim Ullah, 2021. "Investigating the flood damages in Lower Indus Basin since 2000: Spatiotemporal analyses of the major flood events," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(2), pages 2357-2383, September.
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