Uncertainty aware and explainable construction cost prediction using a hybrid probabilistic learning model

This paper presents a unified probabilistic framework for construction cost forecasting, NGBoost-ETR (Natural Gradient Boosting with Extra Trees base learners) that delivers predictive accuracy, calibrated uncertainty, and SHAP-based interpretability. Rather than positioning novelty in algorithmic integration alone, the contribution lies in a systems-level design that jointly addresses three critical gaps in cost modeling: reliable interval calibration, model interpretability, and robustness across complex feature interactions in domain such as sustainable building. Trained on a real-world RSMeans dataset of 4477 samples, NGBoost-ETR achieves superior predictive performance (R² = 0.9866, RMSE = 0.4986, MSE = 0.2486, MAE = 0.2300, and MAPE = 1.4314%) compared to 10 baseline regressors and 9 NGBoost-based hybrids. Beyond point prediction, the model also demonstrates robust probabilistic calibration, validated through a comprehensive suite of six quantitative metrics. Specifically, evaluation based on Prediction Interval Coverage Probability (PICP), Prediction Interval Normalized Average Width (PINAW), Mean Prediction Interval Width (MPIW), Coverage Width-based Criterion (CWC), Negative Log-Likelihood (NLL), and Continuous Ranked Probability Score (CRPS). In head-to-head ablations, NGBoost-ETR attains the best interval efficiency (lowest PINAW, MPIW), overall calibration (lowest CWC), and distributional accuracy (lowest CRPS), with competitive NLL and acceptable coverage-outperforming variants that inflate coverage via impractically wide intervals. Crucially, not all hybrids are beneficial (e.g., some NGBoost pairings underperform their base learners), emphasizing that the ETR pairing is a validated choice rather than a generic integration. This work introduces a process innovation by embedding reliable uncertainty estimation into tree-based models without sacrificing performance, supporting greater resource efficiency in cost planning and estimation. The resulting framework not only supports data-driven budgeting and tendering but also promotes transparent institutions and risk-aware decision-making in construction management.