DATA-DRIVEN PROGRESS PREDICTION IN CONSTRUCTION: A MULTI-PROJECT PORTFOLIO MANAGEMENT APPROACH

Maryam Tagharobi^1*Mostafa Babaeian Jelodar¹Teo Susnjak²

• ¹Massey University, School of Built Environment, Auckland, New Zealand
• ²Massey University School of Mathematical and Computational Sciences, Palmerston North, New Zealand

Construction projects often experience delays and cost overruns, particularly in regions like New Zealand, where natural hazards and climate change exacerbate these risks. Despite extensive research on forecasting overall construction project timelines, limited attention has been given to stage-wise progress across the project lifecycle, constraining project managers' ability to monitor performance and respond to risks. To address this gap, the study develops a stage-based forecasting model using Multinomial Logistic Regression, which is identified as the most suitable method after comparison with selected machine learning approaches within the study's scope and assumptions. A stepwise comparative framework was then employed to assess combinations of duration, value, type, and contractor involvement, measuring accuracy, log-loss, and Cohen's kappa using ten years of New Zealand construction data. The results show that, while all models performed reasonably well, the model using only project duration and value achieved the highest accuracy. Model reliability was further examined using confusion matrices to derive sensitivity, specificity, predictive values, and balanced accuracy. Validation through cross-validation, ROC/AUC, and temporal hold-out testing confirmed the framework's robustness and generalizability. Finally, the results were presented through visualisations that make milestone-specific progress predictions (5% to 100%) easy to interpret, providing project managers with practical insights for planning, monitoring, risk management, and resource allocation. By offering a transparent, interpretable approach, the model bridges statistical forecasting with real-world practice, supporting timely delivery and data-driven infrastructure development. Future research could incorporate additional factors, extend the model locally and internationally, and explore integration with digital twins or real-time adaptive systems.