典型文献
Bridging finite element and deep learning:High-resolution stress distribution prediction in structural components
文献摘要：
Finite-element analysis(FEA)for structures has been broadly used to conduct stress analysis of various civil and mechanical engineering structures.Conventional methods,such as FEA,provide high fidelity results but require the solution of large linear systems that can be computationally intensive.Instead,Deep Learning(DL)techniques can generate results significantly faster than conventional run-time analysis.This can prove extremely valuable in real-time structural assessment applications.Our proposed method uses deep neural networks in the form of convolutional neural networks(CNN)to bypass the FEA and predict high-resolution stress distributions on loaded steel plates with variable loading and boundary conditions.The CNN was designed and trained to use the geometry,boundary conditions,and load as input to predict the stress contours.The proposed technique's performance was compared to finite-element simulations using a partial differential equation(PDE)solver.The trained DL model can predict the stress distributions with a mean absolute error of 0.9％and an absolute peak error of 0.46％for the von Mises stress distribution.This study shows the feasibility and potential of using DL techniques to bypass FEA for stress analysis applications.
文献关键词：
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作者姓名：
Hamed BOLANDI;Xuyang LI;Talal SALEM;Vishnu Naresh BODDETI;Nizar LAJNEF
作者机构：
Department of Civil and Environmental Engineering,Michigan State University,East Lansing,MI 48824,USA;Department of Computer Science and Engineering,Michigan State University,East Lansing MI 48824,USA
文献出处：
引用格式：
[1]Hamed BOLANDI;Xuyang LI;Talal SALEM;Vishnu Naresh BODDETI;Nizar LAJNEF-.Bridging finite element and deep learning:High-resolution stress distribution prediction in structural components)[J].结构与土木工程前沿,2022(11):1365-1377
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AB值：
0.568091
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