Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/164161
Title: A feed-forwarded neural network-based variational Bayesian learning approach for forensic analysis of traffic accident
Authors: Xie, Yuxi
Wu, C. T.
Li, Boyuan
Hu, Xuan
Li, Shaofan
Keywords: Engineering::Mechanical engineering
Issue Date: 2022
Source: Xie, Y., Wu, C. T., Li, B., Hu, X. & Li, S. (2022). A feed-forwarded neural network-based variational Bayesian learning approach for forensic analysis of traffic accident. Computer Methods in Applied Mechanics and Engineering, 397, 115148-. https://dx.doi.org/10.1016/j.cma.2022.115148
Journal: Computer Methods in Applied Mechanics and Engineering
Abstract: In this work, a variational Bayesian learning-based computation algorithm is developed to “inversely” identify the deformation field of a crashed car and hence their residual strain fields based on its final damaged structural configuration (wreckage), which is important in three-dimensional traffic collision reconstruction and its forensic analysis. Different from our previous Generalized Bayesian Regularization Network (GBRN) algorithm (Xie et al. [2002] Computational Mechanics 69, 1191–1212), the present method is based on Variational Bayesian Learning theory coupled with a Feed-forward Neural Network architecture, and it provides a higher computation efficiency. This is because it requires less number of iterations and produces more accurate registration results, since the locality of nodes are greatly preserved during registration process. In this work, we have demonstrated that the developed machine learning algorithm has a unique capability to practically identify the deformation field of a real crashed car and to recover its initial pre-crash state based on residual damaged geometric configuration, and it shows great potential in forensic analysis of car crash and vehicle crashworthiness evaluation.
URI: https://hdl.handle.net/10356/164161
ISSN: 0045-7825
DOI: 10.1016/j.cma.2022.115148
Rights: © 2022 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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