Abstract
Structural health monitoring (SHM) is a hot research topic with the main purpose of damage detection in a structure and assessing its health state. The major focus of SHM studies in recent years has been on developing vibration-based damage detection algorithms and using machine learning, especially deep learning-based approaches. Most of the deep learning-based methods proposed for damage detection in civil structures are based on supervised algorithms that require data from the healthy state and different damaged states of the structure in the training phase. As it is not usually possible to collect data from damaged states of a large civil structure, using such algorithms for these structures may be impractical. This paper proposes a new unsupervised deep learning-based method for structural damage detection based on convolutional autoencoders (CAEs). The main objective of the proposed method is to identify and quantify structural damage using a CAE network that employs raw vibration signals from the structure and is trained by the signals solely acquired from the healthy state of the structure. The CAE is chosen to take advantage of high feature extraction capability of convolution layers and at the same time use the advantages of an autoencoder as an unsupervised algorithm that does not need data from damaged states in the training phase. Applications on the two numerical models of IASC-ASCE benchmark structure and a grid structure located at the University of Central Florida, as well as the full-scale Tianjin Yonghe Bridge, prove the efficiency of the proposed algorithm in assessing the global health state of the structures and quantifying the damage.
Highlights
Civil infrastructures including buildings and bridges are valuable assets necessary for every society to function well.ey are susceptible to damage due to different factors such as material aging, environmental corrosion, poor construction quality, or extreme events such as earthquakes [1].erefore, these structures need to be checked regularly to detect damage in the early stage and prevent their propagation through the structure
Is paper proposes a new unsupervised deep learningbased algorithm for structural damage detection based on convolutional autoencoders (CAEs). e main objective of the proposed method, which makes it different from the current researches, is to identify and quantify structural damage using raw vibration signals from the structure and training the CAE network using only signals acquired from the structure in the healthy state
In order to use the advantages of both autoencoders and convolutional neural networks (CNNs), CAEs are used in this study, which usually use convolution and pooling layers to extract the key features of the input data and compress them and deconvolution and unpooling layers to reconstruct the original data from the compressed form [36]
Summary
Civil infrastructures including buildings and bridges are valuable assets necessary for every society to function well. Is paper proposes a new unsupervised deep learningbased algorithm for structural damage detection based on CAEs. e main objective of the proposed method, which makes it different from the current researches, is to identify and quantify structural damage using raw vibration signals from the structure and training the CAE network using only signals acquired from the structure in the healthy state. E CAE is chosen to take advantage of high feature extraction capability of convolution layers and at the same time use the advantages of an autoencoder as an unsupervised algorithm that does not need data from damaged states in the training phase and can be used for health monitoring of real-life civil structures. E rest of this paper is organized as follows: A brief overview of autoencoders is presented in Section 2, the proposed methodology is introduced in Section 3, applications on the above-mentioned structures and obtained results are described in Section 4, and the last section includes concluding remarks regarding this paper
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