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CNN-based damage identification method of tied-arch bridge using spatial-spectral information

机译：基于CNN的空间光谱信息的系杆拱损伤识别方法

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摘要

In the structural health monitoring field, damage detection has been commonly carried out based on the structural model and the engineering features related to the model. However, the extracted features are often subjected to various errors, which makes the pattern recognition for damage detection still challenging In this study, an automated damage identification method is presented for hanger cables in a tied-arch bridge using a convolutional neural network (CNN). Raw measurement data for Fourier amplitude spectra (FAS) of acceleration responses are used without a complex data pre-processing for modal identification. A CNN is a kind of deep neural network that typically consists of convolution, pooling, and fully-connected layers. A numerical simulation study was performed for multiple damage detection in the hangers using ambient wind vibration data on the bridge deck. The results show that the current CNN using FAS data performs better under various damage states than the CNN using time-history data and the traditional neural network using FAS. Robustness of the present CNN has been proven under various observational noise levels and wind speeds.
机译：在结构健康监测领域，通常基于结构模型和与模型有关的工程特征来进行损伤检测。但是，提取的特征经常会遭受各种错误，这使得用于损伤检测的模式识别仍然具有挑战性。在这项研究中，提出了一种使用卷积神经网络（CNN）的自动约束识别方法，用于对系杆拱桥中的吊索进行识别。加速度响应的傅立叶振幅谱（FAS）的原始测量数据无需进行模态识别的复杂数据预处理即可使用。 CNN是一种深度神经网络，通常由卷积，池化和完全连接的层组成。使用桥面板上的环境风振动数据，对衣架中的多个损伤进行了数值模拟研究。结果表明，与使用时间历史数据的CNN和使用FAS的传统神经网络相比，当前使用FAS数据的CNN在各种损伤状态下的性能都更好。在各种观测噪声水平和风速下，已经证明了本CNN的鲁棒性。

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