Use of explainable machine learning models in blast load prediction
Publication Name
Engineering Structures
Abstract
The effects of blast waves and their consequent damage to structures have been an increasingly popular research topic in the past decade. Various methods are used in blast load prediction on structures, including experimental, semi-empirical and numerical approaches. However, there is a demand for developing time-efficient predictive methods that help various professionals, such as engineering practitioners and first responders, in their regular activities. Machine learning (ML), as a subset of artificial intelligence (AI), has gained significant attention in various engineering applications over the past decades for its ability to aid decision-making. However, a critical gap exists in the comprehensive analysis of these ML models' transparency and interpretability levels. This paper presents a novel approach to employing explainable machine learning (XML) to predict the blast loads generated by high explosives. The primary objective is to assess the validity of utilising a machine learning (ML) framework for blast load predictions, examining the scientific consistency of the model's prediction process. This research utilises XML to interpret the ML model-based blast load prediction framework, marking the first documented use of XML in published literature for blast load prediction. This research study utilised three ML models, namely a) Decision Tree, b) Random Forest, and c) Extreme Gradient Boost (XGB) models, to predict blast loads on rigid structures. Validated numerical model results were used to generate the dataset to train the ML models, and the respective dataset consists of approximately 600 independent blast events. The models were evaluated for their predictability using error metrics: adjusted coefficient of determination (adjusted R2), root mean square error (RMSE), and mean absolute error (MAE). The overall ML model performance evaluation showed that the best-performing model, XGB, could make predictions with 98 % accuracy compared to validated numerical predictions. The XML explanations for the XGB models showcased that the blast load prediction procedure of the models aligns with the blast physics principles, ensuring the credibility of the developed ML models. In summary, these XML predictive models can be considered accurate and reliable for blast load prediction on structures.
Open Access Status
This publication is not available as open access
Volume
312
Article Number
118271
Funding Number
DP230101133
Funding Sponsor
Australian Research Council