What question did this study set out to answer?

The aim is to develop a data-driven framework to predict and mitigate construction cost overruns using machine learning techniques.

March 5, 2026

A Machine Learning-Driven Framework for Sector-Specific Construction Cost Overrun Prediction and Mitigation

Key Points

The aim is to develop a data-driven framework to predict and mitigate construction cost overruns using machine learning techniques.
Used a structured Likert-scale questionnaire to collect data from 70 stakeholders in construction.
Categorized potential factors causing cost overruns into five major categories with three vital factors each.
Employed various machine learning models, primarily testing Random Forest Regression for effectiveness.
Analyzed model performance using R2 scores across residential, commercial, and industrial sectors.
Random Forest Regression achieved the highest R2 score of 0.8001 for overall predictions and above 0.87 for specific sectors.
XGBoost, CatBoost, and MLP regressors showed lower scores compared to Random Forest, tracking in the range of 0.79 to 0.86.
Classical models like Ridge and Linear Regression had significantly lower performance than machine learning methods.

Abstract

The construction industry is essential for infrastructure development in residential, commercial, and industrial sectors. Despite careful planning, cost overruns persist due to factors like imprecise estimations, inadequate risk mitigation, inflation, and site conditions. This study proposes a data-driven cost-management framework by intertwining statistical analysis with machine learning to anticipate and mitigate financial deviations. A structured Likert-scale questionnaire designed through an extensive literature review and the factors thus found influential were categorized into five major categories, each encompassing three vital factors causing cost overruns, questionnaires were used to collect 70 responses from diverse stakeholders (engineers, contractors, and owners) across all sectors. Among the various models tested, Random Forest Regression outperformed all others, achieving R2 scores of 0.8001 (Overall), 0.8715 (Residential), 0.8715 (Commercial), and 0.7990 (Industrial & Heavy). Comparatively, XGBoost yielded 0.7901, 0.8615, 0.8615, 0.7890, CatBoost 0.7851, 0.8565, 0.8565, 0.7840, and MLP regressors 0.7801, 0.8515, 0.8515, 0.7790 for overall, residential, commercial, industrial, and heavy, respectively. whereas classical models, such as Ridge and Linear Regression, trailed behind. The strength of Random Forest lies in capturing nonlinear interactions within perceptual data, while enabling interpretability through SHAP and feature importance analysis. Although prior studies have employed machine learning, the novelty of this research lies in its sector-specific, stakeholder-informed real-time data approach, offering actionable insights for targeted risk mitigation, cost control, and effective execution, bridging a critical gap in the construction cost overrun literature and also posing a major contribution of the study. The novelty of this work lies in its sector-specific (residential, commercial, industrial/heavy), stakeholder-informed real-time perceptual data approach combined with explainable AI (SHAP and PCA), filling critical gaps in generic, single-sector, or archival-data-focused models prevalent in prior research.

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A Machine Learning-Driven Framework for Sector-Specific Construction Cost Overrun Prediction and Mitigation

Key Points

Abstract

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