Application of deep learning neural network to identify collision load conditions based on permanent plastic deformation of shell structures
Application of deep learning neural network to identify collision load conditions based on permanent plastic deformation of shell structures
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DOI：
10.1007/s00466-019-01706-2
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发表时间：
2019-08-01
影响因子：
4.1
通讯作者：
Li, Shaofan
中科院分区：
文献类型：
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作者：
Chen, Guorong;Li, Tiange;Li, Shaofan
在这项工作中，我们开发了一种新的深度学习逆解或识别方法，根据壳体结构的最终损伤或非弹性变形状态来确定和识别壳体结构的冲击载荷条件。这种人工智能方法为解决基于材料和结构最终损伤状态和永久塑性变形的工程失效分析反问题提供了一种实用的解决方案。更确切地说，机器学习逆问题求解器可以提供实际解决方案，以基于正在检查的壳体结构的最终永久塑性变形分布来表征失效载荷参数和条件。在这项工作中，我们已经证明，所提出的深度学习方法可以准确地识别一个实际上独特的静态加载条件以及基于永久塑性变形的半球壳结构的冲击动态加载条件作为法医签名。在这项工作中开发的基于数据驱动的方法可以提供一个强大的工具，法医诊断，确定和识别工程结构在各种意外故障事件，如车祸，压力容器故障，或薄壁基础设施结构倒塌的损伤载荷条件。在这项工作中开发的机器学习逆问题求解器可能对基于最终永久塑性变形的一般法医材料和结构失效分析产生潜在影响。
In this work, we have developed a novel deep learning inverse solution or identification method to determine and identify the impact load conditions of shell structures based on their final state of damage or inelastic deformation. This artificial intelligence approach offers a practical solution to solve the inverse problem of engineering failure analysis based on final material and structure damage state and permanent plastic deformation. More precisely, the machine learning inverse problem solver may provide a practical solution to characterize failure load parameters and conditions based on the final permanent plastic deformation distribution of the shell structure that is under examination. In this work, we have demonstrated that the proposed deep learning method can accurately identify a practically unique static loading condition as well as the impact dynamic loading condition for a hemispherical shell structure based the permanent plastic deformation after the impact event as the forensic signatures. The data-driven based method developed in this work may provide a powerful tool for forensically diagnosing, determining, and identifying damage loading conditions for engineering structures in various accidental failure events, such as car crashes, pressure vessel failure, or thin-walled infrastructure structure collapses. The machine learning inverse problem solver developed here in this work may have potential impacts on general forensic material and structure failure analysis based on final permanent plastic deformations.