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Journals / Turkish Journal of Engineering / 2026 / Volume: 10 Issue: 4

Artificial Intelligence in Construction Management Enhancing Decision Making A cross a Project Life Cycle: throught A Machine Learning Method

Elafri Nedjwa(UNIVERSITY OF SALAH BOUBNIDER CONSTANTINE3)0000-0001-9623-0107
Abdallah Lalmi(UNIVERSITY OF CONSTANTINE 3)0000-0002-9806-1391
Badreddine Boumali(UNIVERSITY OF CONSTANTINE 3)0000-0002-7565-3730
Koudoua Ferhati(CRAT)0000-0003-3733-7718
Pages
—
Publication Date
2026-10-16
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

These findings underscore the importance of ensemble ML in proactive, data-driven risk management, bringing projects into a predictive framework rather than reactive.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The construction industry, which creates 13% of the world’s GDP, suffers from cost overruns of 28% and delays of over 42%, yet is still undigitized.
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Key Findings & Data:This research provides a systematic review of AI applications and performs an original machine learning analysis on seven algorithms, i.e., Support Vector Machines, Logistic Regression, K-Nearest Neighbors, Random Forest, Stacking Ensemble, Artificial Neural Networks, and XGBoost on 139 Indian road projects with 44 risk factors.
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Conclusion & Impact:These findings underscore the importance of ensemble ML in proactive, data-driven risk management, bringing projects into a predictive framework rather than reactive.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:artificial intelligence · construction management · machine learning

Abstract

The construction industry, which creates 13% of the world’s GDP, suffers from cost overruns of 28% and delays of over 42%, yet is still undigitized. AI has the promise of transforming management in the areas of cost, time and safety. This research provides a systematic review of AI applications and performs an original machine learning analysis on seven algorithms, i.e., Support Vector Machines, Logistic Regression, K-Nearest Neighbors, Random Forest, Stacking Ensemble, Artificial Neural Networks, and XGBoost on 139 Indian road projects with 44 risk factors. XGBoost performed the best with 82.50% accuracy and 84.21% F1-score. The most important concerns were the delay in decision-making (0.152), cash flow problems (0.082) and cost underestimate (0.071). Case studies demonstrate AI’s capabilities: 98.7% accurate cost estimation, 94.36% delay prediction via deep learning, and 97% safety monitoring with computer vision. These findings underscore the importance of ensemble ML in proactive, data-driven risk management, bringing projects into a predictive framework rather than reactive.

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Cite This Article

Nedjwa, E., Lalmi, A., Boumali, B., Ferhati, K. (2026). Artificial Intelligence in Construction Management Enhancing Decision Making A cross a Project Life Cycle: throught A Machine Learning Method. Turkish Journal of Engineering, Volume: 10 Issue: 4. https://doi.org/10.31127/tuje.1968203

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References (22)

  1. Abdelrahman, O., Elhag, H. A., & Bakhshipour, A. A. (2023). Predicting construction cost overrun using ensemble machine learning: A comparative study. Automation in Construction, 155, 105049.
  2. Ahmeti, M., & Sylejmani, M. (2025). Analyzing construction delays in post-conflict economies: An empirical study of causes and mitigation strategies in the Western Balkans. Turkish Journal of Engineering, 10(1), 252-266. Erişim adresi: https://doi.org/10.31127/tuje.1780047
  3. Alzara, M. (2022). Exploring the impacts of change orders on performance of construction projects in Saudi Arabia. Advances in Civil Engineering, 2022(1), 5775926.
  4. Ansar, A., Flyvbjerg, B., Budzier, A., & Lunn, D. (2016). Does infrastructure investment lead to economic growth or economic fragility? Evidence from China. Oxford Review of Economic Policy, 32(3), 360-390.
  5. Başarslan, M. S., & Kayaalp, F. (2024). Sentiment analysis of coronavirus data with ensemble and machine learning methods. Turkish Journal of Engineering, 8(2), 175-185. Erişim adresi: https://doi.org/10.31127/tuje.1352481
  6. Börner, T., & Zinn, M. (2024). Key challenges in the advancement and industrialization of biobased and biodegradable plastics: A value chain overarching perspective. Frontiers in Bioengineering and Biotechnology, 12, 1406278.
  7. Çalışkan, E. B., & Pekeriçli, M. K. (2025). The involvement of project stakeholders in the briefing process: Evaluations through a construction project. Turkish Journal of Engineering, 9(1), 12-20. Erişim adresi: https://doi.org/10.31127/tuje.1496562
  8. Ding, F., Pors, A., & Bozhevolnyi, S. I. (2018). Gradient metasurfaces: A review of fundamentals and applications. Reports on Progress in Physics, 81(2), 026401.
  9. Elalwani, E., & Çalışkan, E. B. (2024). Integrating BIM technology in construction for effective knowledge management: Case studies and methodological insights. Turkish Journal of Engineering, 8(4), 647-655. Erişim adresi: https://doi.org/10.31127/tuje.1458923
  10. Gao, Y., Antwi-Afari, M. F., Huang, Y., Chen, Z.-S., & Manzoor, B. (2026). Artificial intelligence in construction project management: A systematic literature review of cost, time, and safety management. Buildings, 16(5), 1061.
  11. Hamid, S. A. B. A., Zin, H. H. M., & Bakar, A. H. A. (2021). Artificial neural network-based modelling for predicting construction cost at the early design stage. Buildings, 11(12), 622.
  12. Hayat, A., & Morgado-Dias, F. (2022). Deep learning-based automatic safety helmet detection system for construction safety. Applied Sciences, 12(16), 8268.
  13. Jain, R., Singh, S. K., Palaniappan, D., Parmar, K., & T, P. (2025). Data-driven civil engineering: Applications of artificial intelligence, machine learning, and deep learning. Turkish Journal of Engineering, 9(2), 354-377. Erişim adresi: https://doi.org/10.31127/tuje.1581564
  14. Khalil, R. A., Safelnasr, Z., Yemane, N., Kedir, M., Shafiqurrahman, A., & Saeed, N. (2024). Advanced learning technologies for intelligent transportation systems: Prospects and challenges. IEEE Open Journal of Vehicular Technology, 5, 397-427.
  15. Kim, J., Lee, S., & Kim, M. (2023). Machine learning for construction cost prediction: Systematic review and meta-analysis. Journal of Construction Engineering and Management, 149(6), 04023048.
  16. Lee, J. H. (2022). Enhancing organizational transformation for design-build infrastructure projects: Design liability, construction quality assurance, and new engineering leadership requirements (Doctoral Thesis). Georgia Institute of Technology, Atlanta, GA, USA.
  17. Nastase, C., Adomnitei, A., & Apetri, A. (2025). Strategic human resource management in the digital era: Technology, transformation, and sustainable advantage. Merits, 5(4), 23.
  18. Saka, S. A. A. A., & Akinade, O. O. A. (2021). Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges. Journal of Building Engineering, 44, 103299.
  19. Shirazi, D. H., & Toosi, H. (2023). Deep multilayer perceptron neural network for the prediction of Iranian dam project delay risks. Journal of Construction Engineering and Management, 149(4), 04023011.
  20. Uddin, M. G., Nash, S., Diganta, M. T. M., Rahman, A., & Olbert, A. I. (2022). Robust machine learning algorithms for predicting coastal water quality index. Journal of Environmental Management, 321, 115923.
  21. Van Tam, N. (2025). How generative AI reshapes construction and built environment: The good, the bad, and the ugly. Building and Environment, 113526.
  22. Zheng, L., Lu, J., & Zhang, Y. (2022). Application of artificial intelligence in construction safety management: A systematic review. Safety Science, 152, 105778.