A hybrid light GBM and Harris Hawks optimization approach for forecasting construction project performance: enhancing schedule and budget predictions

Abstract

The study investigates machine learning applications in civil engineering, which are biased towards construction management. The hybrid model was developed for better schedule deviation and budget overrun performance, based on Harris Hawks Optimization combined with Light GBM. Using HHO for feature selection, the model identified the most influencing factors like Project Size, Risk Score, and Change Orders. This optimized the prediction process. This hybrid approach outperformed the traditional machine learning models, including Random Forest and XGBoost, by an optimum RMSE of 15.32 days schedule deviations and $25,840 budget overruns, proving more accurate and efficient. Therefore, this underpins the potential AI-driven solutions for improving project planning, risk mitigation, and decision-making within construction management. Future work will need to refine models as artificial intelligence becomes integrated into practice within civil engineering. Additional predictive variables will be further investigated while extending the approach to other areas of construction management and civil engineering applications.