Explainable Artificial Intelligence (XAI)-Enabled Probabilistic Fire Risk Prediction for High-Rise Residential Buildings: SHAP Attribution of Human and Organisational Risks

Fire safety in high-rise residential buildings depends on active fire protection systems subject to technical, human, and organisational risks. Prior probabilistic models incorporating human and organisational errors (HOEs) raise expected risk-to-life by 20 to 37%, yet remain inaccessible to the owners, managers, qualified persons, and regulators who must act on them. This paper applies SHAP (SHapley Additive exPlanations) to a reconstructed Bayesian network fire risk model with Markov Chain Monte Carlo uncertainty propagation, extending the T-H-O-Risk methodology across sixteen system configurations and seven buildings in five jurisdictions (Singapore, Australia, Hong Kong, New Zealand, the UK) plus a generic reference case. Global SHAP analysis attributes 89.8% of total HOE attribution to maintenance-related factors (H7, insufficient technical handover; H8, insufficient safety check; H9, inadequate periodic inspection), reframing the primary intervention from behavioural to structural. The reconstruction reproduces 112 published ERL values with a mean absolute percentage error of 1.8% on total ERL (13.7% on the HOE increment, the layer carrying the paper’s claims) and 96% interval coverage. Attribution outputs are translated into graded, risk-informed maintenance conditions for SCDF waiver assessment. To the authors’ knowledge, this is the first Shapley values attribution of HOE factors in a building system-level Bayesian network fire risk model.