Convolutional Neural Network Approach for Robust Structural Damage Detection and Localization
Convolutional Neural Network Approach for Robust Structural Damage Detection and Localization
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DOI：
10.1061/(asce)cp.1943-5487.0000820
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发表时间：
2019-05-01
影响因子：
6.9
通讯作者：
Pakzad, Shamim N.
中科院分区：
文献类型：
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作者：
Gulgec, Nur Sila;Takac, Martin;Pakzad, Shamim N.
损伤诊断一直是结构健康监测中具有挑战性的反问题。主要的困难是表征测量和损伤模式之间的未知关系（即，损坏指示器选择）。这种损害指示器最好能够查明损害的存在、位置和严重程度。针对这一局限性，本文将图像识别领域的重大突破之一卷积神经网络（CNN）引入到损伤检测与定位问题中。CNN技术能够发现抽象特征和复杂的分类器边界，这些特征和边界能够区分问题的各种属性。本文设计了一种CNN拓扑结构，用于对模拟的受损和健康情况进行分类，并在损伤存在时进行定位。所提出的技术的性能进行了评估，通过有限元模拟未损坏和损坏的结构连接。通过使用应变分布作为具有几种不同裂纹情况的各种载荷的结果来训练样本。在测试过程中，将全新的损伤设置引入模型。基于所提出的研究结果，实现了高精度，鲁棒性和计算效率的损伤诊断和定位。(c)2019年美国土木工程师协会。
Damage diagnosis has been a challenging inverse problem in structural health monitoring. The main difficulty is characterizing the unknown relation between the measurements and damage patterns (i.e., damage indicator selection). Such damage indicators would ideally be able to identify the existence, location, and severity of damage. Therefore, this procedure requires complex data processing algorithms and dense sensor arrays, which brings computational intensity with it. To address this limitation, this paper introduces convolutional neural network (CNN), which is one of the major breakthroughs in image recognition, to the damage detection and localization problem. The CNN technique has the ability to discover abstract features and complex classifier boundaries that are able to distinguish various attributes of the problem. In this paper, a CNN topology was designed to classify simulated damaged and healthy cases and localize the damage when it exists. The performance of the proposed technique was evaluated through the finite-element simulations of undamaged and damaged structural connections. Samples were trained by using strain distributions as a consequence of various loads with several different crack scenarios. Completely new damage setups were introduced to the model during the testing process. Based on the findings of the proposed study, the damage diagnosis and localization were achieved with high accuracy, robustness, and computational efficiency. (c) 2019 American Society of Civil Engineers.