Explainable machine learning-based meta-modeling for predicting fire damage to motor control cabinets in a switchgear room

Accurate prediction of damage to cables and cabinets in switchgear rooms during fire emergencies is crucial for personal safety and maintenance planning of critical infrastructure. Traditional software tools, such as consolidated fire and smoke transport (CFAST), are used to simulate fire emergencies based on initial fire conditions. However, applying such physics-based simulators to model sequential fire propagation damage to multiple objects is challenging because of significant computational costs. We propose to use machine learning and deep learning-based surrogate models for real-time fire damage prediction of multiple objects in switchgear rooms. To train the surrogate models, we generated a motor cabinet damage dataset using CFAST simulations of fire propagation in the switchgear room. We also use an explainable artificial intelligence (XAI) technique, Shapley additive explanations (SHAP), to interpret relationships between cabinet damage and initial fire conditions. Experimental results demonstrate that the multi-layer perceptron (MLP) is the most effective model for predicting fire damage of multiple objects. Furthermore, SHAP-based interpretations align closely with the sensitivity analysis results, providing reliable and transparent insights into the model's reasoning process. These results highlight our framework provides more robust and efficient results than the existing physics-based simulators, contributing to advancements in fire safety and resilience technology.