AI-Based Schedule Overrun Prediction in Technical Projects Using Gradient Boosting Machine for Decision Support
Abdelkarim Ramzy Sweilem, Hala Jamil Abdoh, Ahmad Jamil Abdoh · Frontiers in Computer Science and Artificial Intelligence · 24 May 2026
AI-Based Schedule Overrun Prediction in Technical Projects Using Gradient Boosting Machine for Decision Support
The question. Traditional project control relies on expert judgment and static plans, which makes schedule performance hard to predict as technical projects grow more complex.
Method. We trained a gradient boosting machine on a public dataset of 4,517 projects. Inputs were duration, total cost, team size, risk factor and client satisfaction; the target was schedule overrun as a percentage.
Findings. The model explained about 83.5% of the variation in overrun. Risk factor and project duration were the most influential predictors, followed by client satisfaction.
Sweilem, A. R., Abdoh, H. J., & Abdoh, A. J. (2026). AI-Based Schedule Overrun Prediction in Technical Projects Using Gradient Boosting Machine for Decision Support. Frontiers in Computer Science and Artificial Intelligence, 5(8), 01–16. https://doi.org/10.32996/jcsts.2026.5.8.1
Plain-language summaries for practitioners: What 4,517 projects tell us about schedule overrun and Six models, one question.
Related paper: Benchmarking Machine Learning Models for Software Project Schedule Overrun Prediction
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