IRAF-BRB: An explainable AI framework for enhanced interpretability in project risk assessment

Abstract

In high-stakes project risk assessment, balancing predictive accuracy with interpretability is critical to fostering stakeholder trust and supporting well-informed decision-making. This study presents the Interpretable Risk Assessment Framework with Belief Rule-Based Systems (IRAF-BRB), an Explainable AI (XAI) framework specifically designed to improve transparency, accountability, and accuracy in risk assessment. IRAF-BRB combines Interpretive Structural Modeling (ISM) to map and analyze interdependencies among risk factors with an optimized Belief Rule-Based (BRB) model. A modified Differential Evolution Covariance Matrix Self-Adaptation (DECMSA) algorithm is employed to enhance the predictive power of the BRB model while preserving interpretability, ensuring that stakeholders can both trust and understand the model’s outputs. By transforming complex risk data into intuitive visualizations, the IRAF-BRB framework enables project managers to identify key risk drivers and anticipate cascading effects, leading to proactive risk mitigation. Experimental results demonstrate that IRAF-BRB reduces Mean Squared Error (MSE) to 4.09 e − 4 in predicting risk levels for high-rise construction projects, outperforming traditional BRB models such as Differential Evolution-based BRB (DE-BRB) ( 8.29 e − 4 ) and Particle Swarm Optimization-based BRB (PSO-BRB) ( 2.53 e − 3 ) . The statistical significance of these results was confirmed via a two-sample t-test ( p < 0.05 ) , establishing IRAF-BRB as a reliable and effective tool for accurate and interpretable risk assessment.

Topics & Concepts