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Discovering interpretable blast Loading equations from Black-Box Machine learning models
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DOI：10.1016/j.aei.2025.104244.png)
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摘要
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Boiling Liquid Expanding Vapour Explosion (BLEVE) is a high-energy event characterised by intense blast waves that pose serious safety risks. Accurate prediction of the resulting overpressure wave is essential for knowledgeintensive engineering analysis and decision support. While empirical methods are available for predicting overpressure in simple BLEVE scenarios, they fail to capture nonlinear relationships in multi-feature and complex conditions. Computational Fluid Dynamics (CFD) methods offer high accuracy in overpressure wave prediction but are computationally intensive, expensive to use and difficult to integrate into automated or real-time engineering workflows. Machine learning models offer a promising alternative for rapid predictions, but their limited interpretability, particularly in deep learning architectures, poses a significant barrier to integration into real-world engineering systems. This study proposes a systematic approach combining machine learning, explainable artificial intelligence, and symbolic regression for BLEVE overpressure prediction. A feedforward neural network model is developed and interpreted using SHapley Additive exPlanations (SHAP). Global SHAP analysis identified nine features with the most significant contributions, which were subsequently used to train a global surrogate model via symbolic regression. This approach yielded an explicit mathematical expression that approximates the behaviour of the original neural network. The derived equation achieved a relative error of 15.73% on simulated data and 35.45% on experimental data, outperforming existing empirical formulas. This research demonstrates the potential of combining black-box machine learning models with xAI techniques to develop interpretable and reliable equations for blast load prediction. More importantly, it introduces a novel data-driven methodology of data-model-interpretation-equation that formalises engineering knowledge by transforming black-box models into explicit and interpretable computational representations.
Keyword:
Engineering Decision Support
Machine Learning
Explainable AI
Knowledge Formalisation
BLEVE Overpressure
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4.4K
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