基于机器学习的弱夹紧棱镜悬臂梁横向裂纹检测

Lupu David, Tufisi Cristian, Gillich Rainer-Gilbert, Ardeljan Mario
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引用次数: 3

摘要

由于我们的基础设施正在老化,接近其预期的功能时间,检测损伤或关节松动是结构健康监测中一个非常重要的话题。评估工程结构在运行过程中的健康状况最理想的方法是使用基于非破坏性振动的方法,这种方法可以对结构的完整性进行全局评估。本文比较了使用不同模态数据训练前馈反向传播神经网络来检测类梁结构的横向损伤,这种结构也可能受到不完全边界条件的影响。不同的RFS、RFSmin和DLC训练数据集是通过应用我们的研究团队先前开发的分析方法生成的,该方法使用已知关系,基于模态曲率、横向裂纹的严重程度估计和弱夹紧的估计严重程度。获得的数据集值用于训练三个前馈反向传播神经网络,这些神经网络将用于定位悬臂梁中的横向裂缝并检测结构是否受到弱夹紧的影响。通过绘制每种情况下的计算误差来比较三种人工神经网络模型的输出。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detection of transversal cracks in prismatic cantilever beams with weak clamping using machine learning
Because our infrastructure is aging and approaching the end of its intended functioning time, the detection of damage or loosening of joints is a topic of high importance in structural health monitoring. The most desired way to assess the health of engineering structures during operation is to use non-destructive vibration-based methods that can offer a global evaluation of the structure’s integrity. A comparison of using different modal data for training feedforward backpropagation neural networks for detecting transverse damages in beam-like structures that can also be affected by imperfect boundary conditions is presented in the current paper. The different RFS, RFSmin, and DLC training datasets are generated by applying an analytical method, previously developed by our research team, that uses a known relation, based on the modal curvature, severity estimation of the transverse crack, and the estimated severity for the weak clamping. The obtained dataset values are employed for training three feedforward backpropagation neural networks that will be used to locate transverse cracks in cantilever beams and detect if the structure is affected by weak clamping. The output from the three ANN models is compared by plotting the calculated error for each case.
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