Generative Adversarial Networks for Data Generation in Structural Health Monitoring

• URL: http://arxiv.org/abs/2112.08196v1
• Date: Tue, 7 Dec 2021 03:39:31 GMT
• Title: Generative Adversarial Networks for Data Generation in Structural Health Monitoring
• Authors: Furkan Luleci, F. Necati Catbas, Onur Avci
• Abstract summary: In AI, Machine Learning (ML) and Deep Learning (DL) algorithms require plenty of datasets to train. In SHM applications, collecting data from civil structures through sensors is expensive and obtaining useful data (damage associated data) is challenging. This paper shows that for the cases of insufficient data in DL or ML-based damage diagnostics, 1-D WDCGAN-GP can successfully generate data for the model to be trained on.
• Score: 0.8250374560598496
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Structural Health Monitoring (SHM) has been continuously benefiting from the advancements in the field of data science. Various types of Artificial Intelligence (AI) methods have been utilized for the assessment and evaluation of civil structures. In AI, Machine Learning (ML) and Deep Learning (DL) algorithms require plenty of datasets to train; particularly, the more data DL models are trained with, the better output it yields. Yet, in SHM applications, collecting data from civil structures through sensors is expensive and obtaining useful data (damage associated data) is challenging. In this paper, 1-D Wasserstein loss Deep Convolutional Generative Adversarial Networks using Gradient Penalty (1-D WDCGAN-GP) is utilized to generate damage associated vibration datasets that are similar to the input. For the purpose of vibration-based damage diagnostics, a 1-D Deep Convolutional Neural Network (1-D DCNN) is built, trained, and tested on both real and generated datasets. The classification results from the 1-D DCNN on both datasets resulted to be very similar to each other. The presented work in this paper shows that for the cases of insufficient data in DL or ML-based damage diagnostics, 1-D WDCGAN-GP can successfully generate data for the model to be trained on. Keywords: 1-D Generative Adversarial Networks (GAN), Deep Convolutional Generative Adversarial Networks (DCGAN), Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP), 1-D Convolutional Neural Networks (CNN), Structural Health Monitoring (SHM), Structural Damage Diagnostics, Structural Damage Detection

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