A comparative study on the most effective machine learning model for blast loading prediction: From GBDT to Transformer

机器学习 计算机科学 人工神经网络 人工智能 变压器 极限学习机 感知器 预测建模 随机森林 工程类 电压 电气工程
作者
Qilin Li,Yang Wang,Yanda Shao,Ling Li,Hong Hao
出处
期刊:Engineering Structures [Elsevier]
卷期号:276: 115310-115310 被引量:5
标识
DOI:10.1016/j.engstruct.2022.115310
摘要

In this paper, we present a rigorous comparative study to assess and identify the most effective machine learning model for blast loading prediction. Blast loads are known to produce catastrophic effects including structural collapse and personnel fatality. Accurate and efficient prediction of these extreme loads using empirical methods and numerical solvers remains a challenging problem. Machine learning provides a promising alternative solution, which has been increasingly used in various engineering applications. However, there is seldom any analysis or justification of the selection of machine learning method that would lead to the best performance for such applications. For example, most existing machine learning-based approaches for blast loading prediction utilise the classic multi-layer perceptron (MLP) network with no justifications of their suitability and efficiency nor attempts of leveraging other state-of-the-art neural network architectures. In this study, four well-known machine learning models, including one ensemble tree method and three neural networks of different types, are investigated to demonstrate the effectiveness of different machine learning methods for blast loading prediction. It is showcased using BLEVE (boiling liquid expanding vapour explosion) pressure prediction that the Transformer model achieves the best performance, reaching a relative error of 3.5% and R2 0.997 that outperforms the existing MLP approach (relative error 6.0%, R2 0.985) with a clear margin. This study shows that the Transformer network is an effective tool for prediction of blast loading from BLEVE as well as other explosion sources.
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