Bayesian network for foundation pit collapse risk: expert-guided and data-driven

Purpose This study aims to address a critical research gap in foundation pit collapse risk assessment by developing an innovative framework that integrates expert-guided knowledge with data-driven Bayesian Networks (BNs). Current methods often rely heavily on subjective expert judgment or limited post-accident data, leading to potential biases and incomplete risk assessments. This research seeks to enhance the accuracy and reliability of risk assessments by systematically combining expert insights with data-driven analysis, thereby improving safety management and reducing worker mortality rates in foundation pit construction. Design/methodology/approach The study processed 552 collapse cases via sentiment analysis to extract risk factors. A BN was developed using the Expectation–Maximization algorithm, and expert judgments were integrated via the Analytic Hierarchy Process, forming a data-expert hybrid framework to minimize subjectivity in risk evaluation. Findings The proposed method effectively identifies and quantifies risk factors associated with foundation pit collapses, as demonstrated through case validation. The integration of expert-guided and data-driven approaches not only reduces the cost of expert decision-making but also improves the accuracy of risk predictions. The framework successfully addresses the limitations of existing methods by providing a more comprehensive and objective risk assessment tool. Originality/value This study introduces a novel semi-empirical, semi-data-driven process for constructing BNs, combining the strengths of expert knowledge and data analysis. By addressing the challenges of data scarcity and expert bias, the proposed method offers a significant advancement in foundation pit risk assessment. Crucially, the framework's adaptive architecture – combining a universal risk model derived from global data with a flexible, expert-guided interface for local calibration – ensures its broad applicability across diverse international regulatory and geological contexts. This inherent versatility makes it a valuable and scalable safety management solution for various construction projects worldwide and offers a methodological template for risk analysis in other high-risk industries.