AI-Powered Risk Assessment in Urban Construction Projects Using Predictive Analytics

  • Authors

    • Kenji Sato Engineering Director, Sony Corporation, Japan. Author
    • Aiko Yamamoto Product Manager, Panasonic, Japan. Author

    DOI:

    https://doi.org/10.67228/30715628/IJMIET-2019PI7B9P

    Published 04-05-2019

  • Risk Assessment, Predictive Analytics, Urban Construction, Machine Learning, AI in Civil Engineering, Construction Risk Management, Data-Driven Decision Making

    Issue

    Vol. 2 No. 1 (2019)

    Section

    Articles

    How to Cite

    [1]
    K. Sato and A. Yamamoto, “AI-Powered Risk Assessment in Urban Construction Projects Using Predictive Analytics”, ijmiet, vol. 2, no. 1, pp. 01–07, Apr. 2019, doi: 10.67228/30715628/IJMIET-2019PI7B9P.

  • Abstract

    Urban construction projects are inherently complex and susceptible to various risks including cost overruns, schedule delays, safety hazards, and regulatory issues. Traditional risk assessment methods often rely on static models and expert judgment, which may not adapt well to dynamic urban environments. This paper presents an AI-powered framework leveraging predictive analytics to proactively assess and manage risks in urban construction projects. The proposed system utilizes machine learning algorithms on historical project data, site-specific features, and external urban factors to forecast potential risks and their impact. A case study on multiple urban construction projects demonstrates the model's capability to identify high-risk scenarios, enabling better-informed decision-making. Results indicate significant improvements in risk detection accuracy and proactive mitigation planning compared to conventional methods.

  • References

    [1] Aibinu, A. A., & Jagboro, G. O. (2002). The effects of construction delays on project delivery in Nigerian construction industry. International Journal of Project Management, 20(8), 593–599.

    [2] Cheng, M. Y., Tsai, H. C., & Sudjono, E. (2010). Predicting construction cost and schedule success using artificial neural networks. Journal of Civil Engineering and Management, 16(4), 475–487.

    [3] Kim, G. H., An, S. H., & Kang, K. I. (2004). Comparison of construction cost estimating models based on regression analysis, neural networks, and case-based reasoning. Building and Environment, 39(10), 1235–1242.

    [4] Zhang, X., Wang, Y., & Skitmore, M. (2018). An artificial neural network model for project risk prediction. Automation in Construction, 94, 203–216.

    [5] Fang, D., Zhao, C., & Zhang, M. (2004). A cognitive model of construction workers’ safety behavior. Journal of Construction Engineering and Management, 130(3), 386–395.

    [6] Goh, Y. M., & Chua, D. K. (2010). Neural network forecasting of safety management systems performance. Expert Systems with Applications, 38(2), 1241–1248.

    [7] Son, H., Kim, C., Chong, W. K., & Chou, J. S. (2012). Implementing sustainability in the construction industry: Drivers and change agents. Journal of Cleaner Production, 26, 1–10.

    [8] Love, P. E. D., Edwards, D. J., & Irani, Z. (2012). Moving beyond optimism bias and strategic misrepresentation: An explanation for social infrastructure project cost overruns. IEEE Transactions on Engineering Management, 59(4), 560–571.

    [9] Abioye, S. O., & Oyedele, L. O. (2020). Artificial intelligence for risk management in construction: A review. Journal of Building Engineering, 28, 101070.

    [10] Elghaish, F., Abrishami, S., & Hosseini, M. R. (2020). Integrated project delivery with BIM: An automated EVM-based approach. Automation in Construction, 114, 103182.

    [11] Li, H., Guo, H., Skitmore, M., & Huang, T. (2013). Assessing construction project risks based on the past performance. Engineering, Construction and Architectural Management, 20(3), 286–302.

    [12] Zhang, L., Wu, X., & Shen, G. Q. (2011). Application of Bayesian networks in construction safety management: A case study in Hong Kong. Expert Systems with Applications, 38(6), 7498–7509.

    [13] Liu, H., & Wang, Y. (2017). Predictive analytics for construction project risk management using machine learning. Procedia Engineering, 196, 129–136.

    [14] Lin, Y. C., & Chang, Y. H. (2006). Exploring project risks associated with construction material supply chains. Journal of Civil Engineering and Management, 12(3), 197–207.

    [15] Mahamid, I. (2013). Frequency of change orders in construction projects in Saudi Arabia. International Journal of Project Management, 31(4), 541–547.

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