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Automated damage location for building structures using the hysteretic model and frequency domain neural networks
Structural Control and Health Monitoring ( IF 5.4 ) Pub Date : 2020-06-25 , DOI: 10.1002/stc.2584
Jesús Morales‐Valdez 1, 2 , Mario Lopez‐Pacheco 2 , Wen Yu 2
Affiliation  

This paper presents a novel and accurate model‐reference health monitoring system for the location of damage to building structures using the dissipated energy approach, frequency domain convolutional neural networks (CNNs), and principal component analysis (PCA). Due to the fact that the earthquake introduces several stress cycles in different directions in the structure, load–strain curves can be used as an indicator of damage. The CNN in the frequency domain (CNNFI) is used to estimate the hysteretic displacement of the reference of the Bouc–Wen model. Automated damage locations are resolved with the CNN classification models (CNNFC). The comparison study for damage location is presented by using classical neural networks. The results of the damage location of a two‐story building prototype confirmed that the proposed method is promising for real applications.

中文翻译:

使用滞后模型和频域神经网络对建筑结构进行自动损伤定位

本文介绍了一种新颖,准确的模型参考健康监控系统,该系统使用耗散能量方法,频域卷积神经网络(CNN)和主成分分析(PCA)对建筑物的损坏位置进行定位。由于地震在结构中沿不同方向引入了多个应力循环,因此可以将荷载-应变曲线用作破坏的指标。频域中的CNN(CNNFI)用于估计Bouc–Wen模型参考的滞后位移。可以使用CNN分类模型(CNNFC)解决自动损坏的位置。利用经典神经网络对损伤位置进行了比较研究。
更新日期:2020-06-25
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