Data-driven progress prediction in construction: a multi-project portfolio management approach

Abstract

Introduction: 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 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.

Methods: To address this gap, the study develops a stage-based forecasting model using Multinomial Logistic Regression, which was 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 employed to assess combinations of duration, value, type, and contractor involvement, measuring accuracy, log-loss, and Cohen’s kappa using 10 years of New Zealand construction data. Model reliability was further examined using confusion matrices to derive sensitivity, specificity, predictive values, and balanced accuracy. Validation was conducted through cross-validation, ROC/AUC, and temporal hold-out testing.

Results: The results show that while all models performed reasonably well, the model using only project duration and value achieved the highest accuracy. The validation procedures confirmed the framework’s robustness and generalisability. Visualisations further illustrated milestone-specific progress predictions (5%–100%), making stage-wise forecasts easy to interpret.

Discussion: The model provides project managers with practical insights for planning, monitoring, risk management, and resource allocation. By offering a transparent and interpretable approach, it 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.