Forecasting Construction Cost Index through Artificial Intelligence
Article
Article Title | Forecasting Construction Cost Index through Artificial Intelligence |
---|---|
ERA Journal ID | 211363 |
Article Category | Article |
Authors | Aslam, Bilal, Maqsoom, Ahsen, Inam, Hina, Basharat, Mubeen ul and Ullah, Fahim |
Editors | Bergman, M.M. |
Journal Title | Societies |
Journal Citation | 13 |
Article Number | 219 |
Number of Pages | 15 |
Year | 2023 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 2075-4698 |
Digital Object Identifier (DOI) | https://doi.org/10.3390/soc13100219 |
Web Address (URL) | https://www.mdpi.com/2075-4698/13/10/219 |
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) |
Article Publishing Charge (APC) Amount Paid | 0.0 |
Article Publishing Charge (APC) Funding | Researcher |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 330201. Automation and technology in building and construction |
330202. Building construction management and project planning | |
Byline Affiliations | Northern Arizona University, United States |
COMSATS University Islamabad, Pakistan | |
National University of Sciences and Technology, Pakistan | |
HITEC University, Pakistan | |
School of Surveying and Built Environment |
https://research.usq.edu.au/item/z1x6z/forecasting-construction-cost-index-through-artificial-intelligence
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