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.

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