Forecasting Construction Cost Index through Artificial Intelligence

Bilal Aslam, Ahsen Maqsoom, Hina Inam, Mubeen ul Basharat and Fahim Ullah ()
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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

Societies, 2023, vol. 13, issue 10, 1-15

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.

Keywords: building materials; construction cost index (CCI); developing countries; cost estimation; artificial neural network (ANN) (search for similar items in EconPapers)
JEL-codes: A13 A14 P P0 P1 P2 P3 P4 P5 Z1 (search for similar items in EconPapers)
Date: 2023
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