Damage detection in structural systems utilizing artificial neural networks and proper orthogonal decomposition
Damage detection in structural systems utilizing artificial neural networks and proper orthogonal decomposition
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DOI：
10.1002/stc.2288
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发表时间：
2019-02-01
影响因子：
5.4
通讯作者：
Linzell, Daniel
中科院分区：
文献类型：
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作者：
Azam, Saeed Eftekhar;Rageh, Ahmed;Linzell, Daniel
提出了一种监督学习方案，用于使用人工神经网络 (ANN) 和适当正交分解 (POD) 来检测、定位和量化结构中的损坏强度。对于建筑物和桥梁等结构系统，与其响应相关的固有正交模态 (POM) 是 (1) 施加的外部载荷和 (2) 机械特性的函数。在本研究中，采用监督学习策略来帮助区分由于施加的负载变化造成的损坏造成的 POM 变化。训练神经分类器对不同负载模式的响应进行分类，随后使用施加负载的集合来训练回归 ANN，以检测分类 POM 可能造成的损坏。为了证明所提出方法的有效性，进行了模拟实验，旨在确定铁路桁架桥的损坏指数。使用现有桥梁经过验证的三维 (3D) 有限元 (FE) 模型来生成从桥梁附近的动态称重 (WIM) 站测量的列车载荷下的应变时程。通过这些模拟实验证明了所提出方法的有效性。
A supervised learning scheme is proposed for detecting, locating, and quantifying the intensity of damage in structures using Artificial Neural Networks (ANNs) and Proper Orthogonal Decomposition (POD). For structural systems, such as buildings and bridges, Proper Orthogonal Modes (POMs) associated with their response are functions of (1) applied external loads and (2) mechanistic properties. In the present research, a supervised learning strategy was adopted to help discriminate POM variations because of damage from damage caused by applied load variations. A neural classifier was trained to categorize response to different load patterns, and a regression ANN was subsequently trained using an ensemble of applied loads to detect possible damage from the categorized POMs. To demonstrate the effectiveness of the proposed approach, simulated experiments were performed with the intent of identifying damage indices for a railway truss bridge. A validated, three-dimensional (3D) finite element (FE) model of an existing bridge was used to generate strain time histories under train loads measured from weigh-in-motion (WIM) stations near the bridge. The efficacy of the proposed method was demonstrated through these simulated experiments.