Transparent and reliable construction cost prediction using advanced machine learning and explainable AI
Accurate construction cost prediction is vital for project management, influencing budgeting, resource allocation, and overall success. This study proposes a comprehensive framework that combines machine learning models, uncertainty quantification through Confidence Intervals, and explainable AI tec...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-10-01
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| Series: | Engineering Science and Technology, an International Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215098625002149 |
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| author | Lifei Chen Changyong Xu Wei Hong Lim Abhishek Sharma Sew Sun Tiang Kim Soon Chong El-Sayed M. El-kenawy Amel Ali Alhussan Marwa M. Eid Doaa Sami Khafaga |
| author_facet | Lifei Chen Changyong Xu Wei Hong Lim Abhishek Sharma Sew Sun Tiang Kim Soon Chong El-Sayed M. El-kenawy Amel Ali Alhussan Marwa M. Eid Doaa Sami Khafaga |
| author_sort | Lifei Chen |
| collection | DOAJ |
| description | Accurate construction cost prediction is vital for project management, influencing budgeting, resource allocation, and overall success. This study proposes a comprehensive framework that combines machine learning models, uncertainty quantification through Confidence Intervals, and explainable AI techniques using SHAP (SHapley Additive exPlanations) to enhance transparency and decision-making. Ten machine learning models, including Ridge Regression, Lasso Regression, Elastic Net, K-Nearest Neighbor Regression, and advanced ensemble methods such as XGBoost, CatBoost, and HistGradient Boosting, were evaluated on the RSMeans dataset. Among these, HistGradient Boosting achieved the best performance on the testing dataset. Beyond traditional metrics, Confidence Intervals quantified prediction reliability, and SHAP identified critical cost drivers like “Formwork” and “Tributary Area,” enabling interpretable and robust prediction. This study highlights the potential of machine learning models to revolutionize construction cost estimation by integrating predictive accuracy, uncertainty analysis, and explainability. The proposed framework supports resource efficiency and enables process innovation in cost management. It also contributes to the advancement of sustainable building practices, offering a strong foundation for future research and promoting the adoption of machine learning-based solutions with enhanced transparency and confidence. |
| format | Article |
| id | doaj-art-2c80fc09e096426da21ffb62f268566e |
| institution | Kabale University |
| issn | 2215-0986 |
| language | English |
| publishDate | 2025-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Engineering Science and Technology, an International Journal |
| spelling | doaj-art-2c80fc09e096426da21ffb62f268566e2025-08-20T03:58:11ZengElsevierEngineering Science and Technology, an International Journal2215-09862025-10-017010215910.1016/j.jestch.2025.102159Transparent and reliable construction cost prediction using advanced machine learning and explainable AILifei Chen0Changyong Xu1Wei Hong Lim2Abhishek Sharma3Sew Sun Tiang4Kim Soon Chong5El-Sayed M. El-kenawy6Amel Ali Alhussan7Marwa M. Eid8Doaa Sami Khafaga9Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, MalaysiaGraduate Business School, UCSI University, Kuala Lumpur 56000, MalaysiaFaculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, Malaysia; Corresponding author.Department of Computer Science and Engineering, Graphic Era Deemed to Be University, Dehradun 248002, IndiaFaculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, MalaysiaFaculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, MalaysiaSchool of ICT, Faculty of Engineering, Design and Information & Communications Technology (EDICT), Bahrain Polytechnic, PO Box 33349 Isa Town, Bahrain; Applied Science Research Center. Applied Science Private University, Amman, JordanDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaFaculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura, Egypt; Jadara Research Center, Jadara University, Irbid 21110, JordanDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaAccurate construction cost prediction is vital for project management, influencing budgeting, resource allocation, and overall success. This study proposes a comprehensive framework that combines machine learning models, uncertainty quantification through Confidence Intervals, and explainable AI techniques using SHAP (SHapley Additive exPlanations) to enhance transparency and decision-making. Ten machine learning models, including Ridge Regression, Lasso Regression, Elastic Net, K-Nearest Neighbor Regression, and advanced ensemble methods such as XGBoost, CatBoost, and HistGradient Boosting, were evaluated on the RSMeans dataset. Among these, HistGradient Boosting achieved the best performance on the testing dataset. Beyond traditional metrics, Confidence Intervals quantified prediction reliability, and SHAP identified critical cost drivers like “Formwork” and “Tributary Area,” enabling interpretable and robust prediction. This study highlights the potential of machine learning models to revolutionize construction cost estimation by integrating predictive accuracy, uncertainty analysis, and explainability. The proposed framework supports resource efficiency and enables process innovation in cost management. It also contributes to the advancement of sustainable building practices, offering a strong foundation for future research and promoting the adoption of machine learning-based solutions with enhanced transparency and confidence.http://www.sciencedirect.com/science/article/pii/S2215098625002149Construction cost predictionMachine learningConfidence intervalsExplainable AIEnsemble methodsSHAP |
| spellingShingle | Lifei Chen Changyong Xu Wei Hong Lim Abhishek Sharma Sew Sun Tiang Kim Soon Chong El-Sayed M. El-kenawy Amel Ali Alhussan Marwa M. Eid Doaa Sami Khafaga Transparent and reliable construction cost prediction using advanced machine learning and explainable AI Engineering Science and Technology, an International Journal Construction cost prediction Machine learning Confidence intervals Explainable AI Ensemble methods SHAP |
| title | Transparent and reliable construction cost prediction using advanced machine learning and explainable AI |
| title_full | Transparent and reliable construction cost prediction using advanced machine learning and explainable AI |
| title_fullStr | Transparent and reliable construction cost prediction using advanced machine learning and explainable AI |
| title_full_unstemmed | Transparent and reliable construction cost prediction using advanced machine learning and explainable AI |
| title_short | Transparent and reliable construction cost prediction using advanced machine learning and explainable AI |
| title_sort | transparent and reliable construction cost prediction using advanced machine learning and explainable ai |
| topic | Construction cost prediction Machine learning Confidence intervals Explainable AI Ensemble methods SHAP |
| url | http://www.sciencedirect.com/science/article/pii/S2215098625002149 |
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