Predicting Costs and Risks in Construction Projects Using BIM and Artificial Intelligence Data Analytics
Keywords:
Building Information Modeling, Artificial Intelligence, Cost Prediction, Project Risk, Construction Management, Machine Learning, BIM AnalyticsAbstract
This study aimed to predict cost overrun and project risk in construction projects using BIM-based indicators and artificial intelligence data analytics. This applied, quantitative, predictive, and analytical study was conducted on 64 construction projects in Tehran, Iran, with the participation of 148 construction professionals, including project managers, BIM specialists, cost and quantity surveying experts, planning and project control engineers, and site engineers. Data were collected using BIM-based project documentation, cost and schedule records, risk assessment forms, and expert validation checklists. The main variables included BIM maturity level, number of detected BIM clashes, design revision frequency, schedule deviation, material cost fluctuation, procurement delay, change orders, rework cost ratio, cost overrun percentage, and composite project risk score. Data were analyzed using correlation analysis and artificial intelligence-based predictive models, including multiple linear regression, decision tree, support vector regression, artificial neural network, random forest, and gradient boosting models. Inferential findings showed that cost overrun had significant positive correlations with schedule deviation, design revisions, BIM clashes, material cost fluctuation, and composite project risk score, while BIM maturity had significant negative correlations with cost overrun, schedule deviation, BIM clashes, and risk score. Gradient boosting regression produced the strongest cost prediction performance, with the lowest mean absolute error and root mean square error and the highest coefficient of determination, explaining 84% of the variance in cost overrun. For project risk classification, gradient boosting also achieved the highest performance, with accuracy of 0.89 and area under the curve of 0.93. Feature importance analysis identified schedule deviation, design revision frequency, BIM clashes, material cost fluctuation, and BIM maturity as the strongest predictors of cost and risk outcomes. The findings indicate that integrating BIM-based project information with artificial intelligence analytics provides a reliable predictive framework for early identification of cost overrun and project risk in construction projects. AI-based models, especially gradient boosting, can support proactive decision-making, improve cost control, and strengthen risk management throughout the project life cycle.
References
Abazid, M., Gökçekuş, H., & Çelik, T. (2021). Implementation of TQM and the Integration of BIM in the Construction Management Sector in Saudi Arabia Validated With Hybridized Emerging Harris Hawks Optimization (HHO). https://doi.org/10.20944/preprints202103.0455.v1
Adeel, M., Zaib, S., Awaz, M., Ali, M. A., Raihan, P. M. S., Akter, M. J., Hasan, M. M., Kalsoom, H., Nissa, L. U., & Amir, R. (2023). Building Information Modeling and Artificial Intelligence Based Smart Construction Management: Materials and Electrical. European Journal of Theoretical and Applied Sciences, 1(6), 684-691. https://doi.org/10.59324/ejtas.2023.1(6).68
Afzal, M., Li, R. Y. M., Shoaib, M., Ayyub, M. F., Tagliabue, L. C., Bilal, M., & Ghafoor, H. (2023). Delving Into the Digital Twin Developments and Applications Beyond BIM in the Construction Industry. https://doi.org/10.20944/preprints202311.0244.v1
Al-Sinan, M. A., Bubshait, A. A., & Aljaroudi, Z. (2024a). Autonomous Resources-Loaded Project Scheduling System Development. https://doi.org/10.20944/preprints202402.0139.v1
Al-Sinan, M. A., Bubshait, A. A., & Aljaroudi, Z. (2024b). Generation of Construction Scheduling Through Machine Learning and BIM: A Blueprint. Buildings, 14(4), 934. https://doi.org/10.3390/buildings14040934
Alzara, M., Attia, Y. A., Mahfouz, S., Yosri, A. M., & Ehab, A. (2023). Building a Genetic Algorithm-Based and BIM-Based 5D Time and Cost Optimization Model. IEEE Access, 11, 122502-122515. https://doi.org/10.1109/access.2023.3317137
Amer, N. (2023). Architectural Design in the Light of AI Concepts and Applications. Msa Engineering Journal, 2(2), 628-646. https://doi.org/10.21608/msaeng.2023.291906
Bassir, D., Lodge, H. D., Chang, H., Majak, J., & Chen, G. (2023). Application of Artificial Intelligence and Machine Learning for BIM: Review. International Journal for Simulation and Multidisciplinary Design Optimization, 14, 5. https://doi.org/10.1051/smdo/2023005
Bernstein, P. A., Greaves, M., McConnell, S., & Pearson, C. (2022). Harnessing Artificial Intelligence to Design Healthy, Sustainable, and Equitable Places. Computer, 55(2), 67-71. https://doi.org/10.1109/mc.2021.3134422
Chashyn, D., Khurudzhi, Y., & Daukšys, M. (2024). Integration of Building Information Modeling and Artificial Intelligence of Things in the Post-War Reconstruction and Renovation of Buildings. Journal of Sustainable Architecture and Civil Engineering, 35(2), 117-132. https://doi.org/10.5755/j01.sace.35.2.35160
Demiss, B. A. A., & Elsaigh, W. A. (2026). A 5ir-Inspired Conceptual Framework for AI-augmented Construction Estimation: Review on Developing Conceptual BIM-integrated Hybrid Models for Cost, Time, and Risk Prediction. Engineering Research Express, 8(11), 112101. https://doi.org/10.1088/2631-8695/ae72f8
Doukari, O., Seck, B., & Greenwood, D. (2022). The Creation of Construction Schedules in 4D BIM: A Comparison of Conventional and Automated Approaches. Buildings, 12(8), 1145. https://doi.org/10.3390/buildings12081145
Guo, H. (2024). Application of Intelligent Construction Techniques in China. Highlights in Science Engineering and Technology, 106, 606-615. https://doi.org/10.54097/77923955
Jazzar, M. E., Schranz, C., Urban, H., & Nassereddine, H. (2021). Integrating Construction 4.0 Technologies: A Four-Layer Implementation Plan. Frontiers in Built Environment, 7. https://doi.org/10.3389/fbuil.2021.671408
Khudhair, A., Li, H., Ren, G., & Liu, S. (2021). Towards Future BIM Technology Innovations: A Bibliometric Analysis of the Literature. Applied Sciences, 11(3), 1232. https://doi.org/10.3390/app11031232
Kim, I. (2022). Applying Ai Technology to Recognize Bim Objects and Visible Properties for Achieving Automated Code Compliance Checking. Journal of Civil Engineering and Management, 28(6), 497-508. https://doi.org/10.3846/jcem.2022.16994
Kor, M., Yitmen, İ., & Alizadehsalehi, S. (2022). An Investigation for Integration of Deep Learning and Digital Twins Towards Construction 4.0. Smart and Sustainable Built Environment, 12(3), 461-487. https://doi.org/10.1108/sasbe-08-2021-0148
Krausková, V., & Pifko, H. (2021). Use of Artificial Intelligence in the Field of Sustainable Architecture: Current Knowledge. Architecture Papers of the Faculty of Architecture and Design STU, 26(1), 20-29. https://doi.org/10.2478/alfa-2021-0004
Li, R., Niu, Z., Liu, C., & Wu, B. (2022). The Co-Movement Effect of Managers' Psychological Factors on the BIM Adoption Decision in SMEs: A Study Based on fsQCA. Engineering Construction & Architectural Management, 31(4), 1454-1472. https://doi.org/10.1108/ecam-08-2022-0721
Marinho, A. J. C., Couto, J. P., & Teixeira, J. M. C. (2021). Relational Contracting and Its Combination With the Bim Methodology in Mitigating Asymmetric Information Problems in Construction Projects. Journal of Civil Engineering and Management, 27(4), 217-229. https://doi.org/10.3846/jcem.2021.14742
Özturk, G. B., & Tunca, M. (2020). Artificial Intelligence in Building Information Modeling Research: Country and Document-Based Citation and Bibliographic Coupling Analysis. Celal Bayar Üniversitesi Fen Bilimleri Dergisi, 16(3), 269-279. https://doi.org/10.18466/cbayarfbe.770565
Rane, N. L., Choudhary, S. P., & Rane, J. (2023). Leading-Edge Technologies for Architectural Design: A Comprehensive Review. International Journal of Architecture and Planning, 3(2), 12-48. https://doi.org/10.51483/ijarp.3.2.2023.12-48
Salzgeber, H., Ernst, M., Schneiderbauer, L., & Flora, M. (2024). From Digital Model to Digital Twin in Tunnel Construction. Civil Engineering Design, 6(3), 74-83. https://doi.org/10.1002/cend.202400020
Sun, H., & Liu, Z. (2022). Research on Intelligent Dispatching System Management Platform for Construction Projects Based on Digital Twin and BIM Technology. Advances in Civil Engineering, 2022(1). https://doi.org/10.1155/2022/8273451
Taheri, A., & Sobanjo, J. (2024). Civil Integrated Management (CIM) for Advanced Level Applications to Transportation Infrastructure: A State-of-the-Art Review. Infrastructures, 9(6), 90. https://doi.org/10.3390/infrastructures9060090
Tallet, E., Gledson, B., Rogage, K., Thompson, A., & Wiggett, D. (2021). Digitally-Enabled Design Management. 63-89. https://doi.org/10.4018/978-1-7998-6600-8.ch003
Wang, J., Antwi‐Afari, M. F., Tezel, A., Antwi-Afari, P., & Kasim, T. (2024). Artificial Intelligence in Cloud Computing Technology in the Construction Industry: A Bibliometric and Systematic Review. Journal of Information Technology in Construction, 29, 480-502. https://doi.org/10.36680/j.itcon.2024.022
Wang, Z., He, B., Yu, Y., Shen, C., & Peña-Mora, F. (2020). Building a Next Generation AI Platform for AEC: A Review and Research Challenges. 27-45. https://doi.org/10.46421/2706-6568.37.2020.paper003
Xing, J. (2024). Enhancing Large-Span Structure Design and Maintenance Through the Synergy of CAD, BIM, and IoT-AI Technologies. Applied and Computational Engineering, 57(1), 241-246. https://doi.org/10.54254/2755-2721/57/20241340
Yang, S., Cui, G., & Lu, S. (2020). Ambidextrous Learning of Engineering Project Team: Relying on Control or BIM AI VR AR MR? International Journal of Engineering Business Management, 12, 184797902094874. https://doi.org/10.1177/1847979020948749
Zakaria, M., Mridha, N., Khan, M. S. R., & Chunga, L. (2024). The Role of Building Information Modeling (BIM) in Enhancing Efficiency, Sustainability, and Integration With Emerging Technologies. European Journal of Theoretical and Applied Sciences, 2(6), 676-688. https://doi.org/10.59324/ejtas.2024.2(6).60
Zawada, K., Rybak-Niedziółka, K., Donderewicz, M., & Starzyk, A. (2024). Digitization of AEC Industries Based on BIM and 4.0 Technologies. Buildings, 14(5), 1350. https://doi.org/10.3390/buildings14051350
Zhang, F., Chan, A. P., Darko, A., Chen, Z., & Li, D. (2022). Integrated Applications of Building Information Modeling and Artificial Intelligence Techniques in the AEC/FM Industry. Automation in Construction, 139, 104289. https://doi.org/10.1016/j.autcon.2022.104289
Downloads
Published
Submitted
Revised
Accepted
Issue
Section
License
Copyright (c) 2026 Mohammadmoein Zohreh
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
