A data-driven framework for conceptual cost estimation of infrastructure projects using XGBoost and Bayesian optimization

Abstract

Cost estimation is a key component of project plans, yet it is challenging to provide reliable and efficient estimations using conventional methods in the conceptual phase of infrastructure projects. This study proposes a framework that integrates feature selection, extreme gradient boosting (XGBoost), Bayesian optimization (BO), and SHapley Additive exPlanations (SHAP) to provide conceptual cost estimations and explain the results for early decision-making. Correlation analysis and forward search are combined to select the key features. XGBoost is developed as the estimator and enhanced by BO in accuracy and efficiency. Model explanations were presented using SHAP. The framework is demonstrated through a case study of electric substations containing 605 samples. The results show that the proposed framework can provide satisfactory performance on conceptual cost estimations, where BO-XGBoost outperforms the benchmark models (with ${R^2}$ ~0.9567, adjusted ${R^2}$ ~0.9549, RMSE ~ 0.8690, and MAE ~ 0.4875). SHAP reveals how the features contribute to the cost based on both global and local explanations. The framework provides a guideline for more accurate, efficient, and explainable cost estimations in the conceptual phase of infrastructure projects. It can support the government and project planners in early decision-making, including reliable project budget and plan alternatives selection.

Keywords

• conceptual cost estimation
• infrastructure
• xgboost
• bayesian optimization
• shap