期刊
INTERNATIONAL JOURNAL OF IMPACT ENGINEERING
卷 166, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ijimpeng.2022.104240
关键词
Artificial neural network; Machine learning; Crashworthiness; Buckling transition; Imperfection sensitivity
This paper proposes a machine learning based methodology for predicting the buckling response of tubular structures. By generating a large dataset and evaluating the model, it is demonstrated that this method can accurately predict the crushing response of the structures.
This paper proposes a machine learning based methodology for predicting the buckling response of tubular structures. An extensive dataset of force-time curves is generated using a calibrated finite element model within a parametric space where buckling response is highly non-linear. Based on a fully connected neural network template, the machine learning hyper-parameters are determined and the resulting model is evaluated on a separate test set, with regard to maximum and average load and energy absorption errors. This evaluation shows a non-random error distribution which can be correlated with the physical properties of the structural collapse. To validate this assumption, a similar error analysis is conducted between finite element simulations with varying geometric imperfections. Evaluation of imperfection sensitivity reveals a similar error distribution and comparison of individual curves shows that errors made by the neural network model have a physical interpretation. These results indicate that the proposed machine learning based approach is capable of predicting the crushing response with a level of accuracy comparable to the errors that would be caused by a minor change in geometric imperfection.
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