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@article{204900,
        author = {Abhishek Patwardhan and Dr. P.M Alandkar and A. B. Khemalapure},
        title = {Role of Machine learning in predicting project delays},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {4258-4260},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204900},
        abstract = {Construction project delays are a pervasive challenge globally, leading to significant cost overruns, contractual disputes, and decreased client satisfaction. Traditional project management and statistical methods often rely on lagging indicators and fail to capture the high-dimensional, non-linear relationships that exist among the delay factors (labor, supply chain, design changes, external factors).
This project explores the role and effectiveness of Machine Learning (ML) techniques in shifting construction risk management from a reactive approach to a proactive one. The study utilizes a Supervised Learning methodology, employing historical project data as training input, with the goal of predicting a binary outcome: whether a project will experience a schedule delay exceeding 10\%.
The model also successfully identified Design Changes/Revisions, Material Lead-Time Deviation, and Labor Productivity Fluctuation as the most critical predictors of delay risk. This research validates ML as a robust, data-driven tool for delivering timely, high-confidence early warnings, thereby enabling project managers to implement targeted mitigation strategies and enhance overall project predictability and control.},
        keywords = {},
        month = {June},
        }