Structural Damage Detection with Automatic Feature-Extraction through Deep Learning

2017 ◽  
Vol 32 (12) ◽  
pp. 1025-1046 ◽  
Author(s):  
Yi-zhou Lin ◽  
Zhen-hua Nie ◽  
Hong-wei Ma
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Damage Detection ◽  
Structural Damage ◽  
Structural Damage Detection ◽  
Automatic Feature Extraction

scholarly journals Unsupervised Structural Damage Detection Technique Based on a Deep Convolutional Autoencoder

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zahra Rastin ◽  
Gholamreza Ghodrati Amiri ◽  
Ehsan Darvishan
Keyword(s):  
Deep Learning ◽  
Damage Detection ◽  
Structural Damage ◽  
Numerical Models ◽  
Health State ◽  
Training Phase ◽  
Structural Damage Detection ◽  
Healthy State ◽  
Convolutional Autoencoder ◽  
Unsupervised Deep Learning

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.

scholarly journals Structural damage detection using hybrid deep learning algorithm

2020 ◽  
Vol 14 (2) ◽  
pp. 53-64
Author(s):  
Dang Viet Hung ◽  
Ha Manh Hung ◽  
Pham Hoang Anh ◽  
Nguyen Truong Thang
Keyword(s):  
Deep Learning ◽  
Damage Detection ◽  
Data Storage ◽  
Structural Damage ◽  
Large Scale ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Vibration Sensor ◽  
Structural Damage Detection ◽  
Deep Learning Algorithm

Timely monitoring the large-scale civil structure is a tedious task demanding expert experience and significant economic resources. Towards a smart monitoring system, this study proposes a hybrid deep learning algorithm aiming for structural damage detection tasks, which not only reduces required resources, including computational complexity, data storage but also has the capability to deal with different damage levels. The technique combines the ability to capture local connectivity of Convolution Neural Network and the well-known performance in accounting for long-term dependencies of Long-Short Term Memory network, into a single end-to-end architecture using directly raw acceleration time-series without requiring any signal preprocessing step. The proposed approach is applied to a series of experimentally measured vibration data from a three-story frame and successful in providing accurate damage identification results. Furthermore, parametric studies are carried out to demonstrate the robustness of this hybrid deep learning method when facing data corrupted by random noises, which is unavoidable in reality. Keywords: structural damage detection; deep learning algorithm; vibration; sensor; signal processing.

scholarly journals Automated Structural Damage Identification Using Data Normalization and 1-Dimensional Convolutional Neural Network

2021 ◽  
Vol 11 (6) ◽  
pp. 2610
Author(s):  
Jongbin Won ◽  
Jong-Woong Park ◽  
Soojin Jang ◽  
Kyohoon Jin ◽  
Youngbin Kim
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Damage Detection ◽  
Structural Damage ◽  
Input Data ◽  
Damage Identification ◽  
Beam Model ◽  
Data Normalization ◽  
Structural Damage Detection ◽  
Structural Damage Identification

In the field of structural-health monitoring, vibration-based structural damage detection techniques have been practically implemented in recent decades for structural condition assessment. With the development of deep-learning networks that make automatic feature extraction and high classification accuracy possible, deep-learning-based structural damage detection has been gaining significant attention. The deep-learning neural networks come with fixed input and output size, and input data must be downsampled or cropped to the predetermined input size of the networks to obtain desired output of the network. However, the length of input data (i.e., sensing data) is associated with the excitation quality of a structure, adjusting the size of the input data while maintaining the excitation quality is critical to ensure high accuracy of the deep-learning-based structural damage detection. To address this issue, natural-excitation-technique-based data normalization and the use of 1-D convolutional neural networks for automated structural damage detection are presented. The presented approach converts input data to predetermined size using cross-correlation and uses convolutional network to extract damage-sensitive feature for automated structural damage identification. Numerical simulations were conducted on a simply supported beam model excited by random and traffic loadings, and the performance was validated under various scenarios. The proposed method successfully detected the location of damage on a beam under random and traffic loadings with accuracies of 99.90% and 99.20%, respectively.

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