Generative Adversarial Network for Damage Identification in Civil Structures

Rastin, Zahra; Amiri, Gholamreza Ghodrati; Darvishan, Ehsan

doi:10.1155/2021/3987835

Cited by 10 publications

(10 citation statements)

References 44 publications

(41 reference statements)

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“…In the civil SHM literature, there are also others [ (Wang et al, 2019;Rastin et al, 2021;Yuan et al, 2021;Yang et al, 2022;Huang et al, 2020;Sathya et al, 2020;Sun et al, 2022;Dunphy et al, 2022)] (a total of 10 studies) who are observed that do not fit a category and address different problems. It is observed that the subject of "damage detection after increased resolution" has one study (Sathya et al, 2020) in the literature where the researchers use SRGAN to increase the resolution of the images to improve the performance of the damage classifier.…”

Section: Frontiers In Built Environmentmentioning

confidence: 99%

“…There is one study on the topic of "track irregularity estimation" on acceleration data (Yuan et al, 2021), one study on the topic of "damage identification" via acceleration data (Rastin et al, 2021), and three studies on the topic of "Annotation reduction via transfer learning" [ (Huang et al, 2020); Dunphy et al, 2022)] which aim to reduce the need of annotating data through transfer-learning are other instances of GAN applications in civil SHM.…”

Section: Frontiers In Built Environmentmentioning

confidence: 99%

“…Then, the authors concluded that the model was able to generate reasonable data for different damaged and undamaged states of the building. Rastin et al (2021) introduced an approach of unsupervised SHM and damage identification. Essentially, the methodology introduced a two-stage damage identification process where the first phase is based on using DCGAN to detect and quantify the damages in the structure, then, in the second phase, the detected damages are localized using CGAN on the acceleration signals.…”

Section: Figure 11mentioning

confidence: 99%

See 2 more Smart Citations

A literature review: Generative adversarial networks for civil structural health monitoring

Luleci

Çatbaş

Avcı

2022

Front. Built Environ.

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Structural Health Monitoring (SHM) of civil structures has been constantly evolving with novel methods, advancements in data science, and more accessible technology to address issues related to structural safety, operations, and resiliency. Research and development in the civil SHM field during the last few decades have been progressive due to the increasing use of Artificial Intelligence (AI) methods such as Machine Learning (ML) and Deep Learning (DL). Particularly, Generative Adversarial Networks (GAN), which is a subfamily of Deep Learning has been highly favored in the SHM community within the last couple of years. After its release in 2014, GANs (original GAN and other GAN variants) have been in use for a wide variety of applications in various disciplines, and it has been one of the most popular research topics in the AI-ML domain. While there has not been a review study on the applications of GAN in the civil SHM field, this paper aims to fill this gap by presenting a literature review of the studies that employed GAN specifically in civil SHM applications from 2014 to date, in a condensed format. This study intends to inform SHM practitioners and researchers about GANs and present the highlights of the published work on GANs in the civil SHM field.

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Section: Frontiers In Built Environmentmentioning

confidence: 99%

Section: Frontiers In Built Environmentmentioning

confidence: 99%

Section: Figure 11mentioning

confidence: 99%

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A literature review: Generative adversarial networks for civil structural health monitoring

Luleci

Çatbaş

Avcı

2022

Front. Built Environ.

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“…Guo et al [3] used a BP neural network combined with a modulated broadband mode decomposition (MBMD) method to monitor crane-bearing parts and concluded that the BP neural network has good performance in feature extraction and fault recognition. Rastin et al [4] presented a novel two-stage technique based on generative adversarial networks (GANs) for unsupervised structural health monitoring and damage identifcation. Existing methods to detect structural damage based on the change in structural natural frequency show certain reliability in predicting the location and degree of multiple damage [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Research on Vibration Fatigue Damage Locations of Offshore Oil and Gas Pipelines Based on the GA-Improved BP Neural Network

Xie

Gao

Wang

et al. 2023

Shock and Vibration

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To study vibration fatigue damage localization of offshore oil and gas pipelines, aiming at the location error caused by uncertainty of the initial parameters in backpropagation (BP) neural network training, an improved BP neural network based on the genetic algorithm (GA) is proposed to locate pipeline damage. This approach was verified by experiments and simulations. First, a BP neural network for structural damage location was constructed, and a method to optimize the BP neural network parameters based on the GA was established. Then, a finite element model was established based on the measured data from modal tests of a physical pipeline model, and a large number of vibration neural network training samples were obtained using the finite element model. Finally, to show that the improved BP neural network based on the GA had a better damage location accuracy, the location results of the original BP and GA BP methods were compared for cases with no noise and with 5% noise. The results show that the average error of the BP neural network based on the GA was less than 3%, which was 11.6% lower than that of the original BP neural network.

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“…Vibration data from IASC-ASCE benchmark structure and TCRF bridge were utilized to evaluate the proposed methodology. Rastin et al [26] used CNNs as generators and discriminator parts of generative adversarial networks to identify damages in civil structures using only healthy state data to train the networks.…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning for CNN-Based Damage Detection in Civil Structures with Insufficient Data

Chamangard

Amiri

Darvishan

et al. 2022

Shock and Vibration

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Among various methods proposed for health monitoring of structures, deep learning-based techniques with their powerful performance have attracted considerable attention in recent years. However, a major problem with these methods is that they usually need large amounts of data in the training phase, while such data may not be available in real applications. In this study, compact one-dimensional (1D) convolutional neural networks (CNNs) are utilized that require less data for training. The study is comprised of two parts: the first stage aims to develop a compact CNN that can recognize damages in a structure with high accuracy, when data are provided to some extent. The problem of inadequate training data in health monitoring of experimental and real-life structures is then investigated in the second part. Transfer learning is used to deal with this problem. A compact CNN is utilized as the source domain network and the target domain network receives all of its knowledge from this source. Acceleration time histories from a numerical model, an experimental structure, and a full-scale bridge are utilized to validate the proposed methodology. According to the results, the compact CNN can reach 100% accuracy when data are available for training. Also, for the case of insufficient data, using a compact network as well as transfer learning causes considerable improvement (about 95%) in the accuracy of damage detection.

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Generative Adversarial Network for Damage Identification in Civil Structures

Cited by 10 publications

References 44 publications

A literature review: Generative adversarial networks for civil structural health monitoring

A literature review: Generative adversarial networks for civil structural health monitoring

Research on Vibration Fatigue Damage Locations of Offshore Oil and Gas Pipelines Based on the GA-Improved BP Neural Network

Transfer Learning for CNN-Based Damage Detection in Civil Structures with Insufficient Data

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