An unsupervised method based on convolutional variational auto-encoder and anomaly detection algorithms for light rail squat localization

Yuan, Zhandong; Zhu, Shengyang; Chang, Chao; Yuan, Xuancheng; Zhang, Qinglai; Zhai, Wanming

doi:10.1016/j.conbuildmat.2021.125563

Cited by 24 publications

(8 citation statements)

References 22 publications

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“…However, since this approach only localizes damage, no thresholding strategy was proposed. Another one-dimensional CVAE architecture was proposed by Yuan et al [ 86 ] to identify light rail squat damage, and they combined it with either an MSD-based elliptic envelope or OC-SVM as an anomaly detector. When tested on a laboratory full-scale track platform, they concluded that the elliptic envelope was the better choice, as it makes full use of the Gaussianity of the latent variables.…”

Section: Unsupervised Learning Shm Based On Artificial Neural Networkmentioning

confidence: 99%

Unsupervised Learning Methods for Data-Driven Vibration-Based Structural Health Monitoring: A Review

Eltouny

Gomaa

Liang

2023

Sensors

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Structural damage detection using unsupervised learning methods has been a trending topic in the structural health monitoring (SHM) research community during the past decades. In the context of SHM, unsupervised learning methods rely only on data acquired from intact structures for training the statistical models. Consequently, they are often seen as more practical than their supervised counterpart in implementing an early-warning damage detection system in civil structures. In this article, we review publications on data-driven structural health monitoring from the last decade that relies on unsupervised learning methods with a focus on real-world application and practicality. Novelty detection using vibration data is by far the most common approach for unsupervised learning SHM and is, therefore, given more attention in this article. Following a brief introduction, we present the state-of-the-art studies in unsupervised-learning SHM, categorized by the types of used machine-learning methods. We then examine the benchmarks that are commonly used to validate unsupervised-learning SHM methods. We also discuss the main challenges and limitations in the existing literature that make it difficult to translate SHM methods from research to practical applications. Accordingly, we outline the current knowledge gaps and provide recommendations for future directions to assist researchers in developing more reliable SHM methods.

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Section: Unsupervised Learning Shm Based On Artificial Neural Networkmentioning

confidence: 99%

Unsupervised Learning Methods for Data-Driven Vibration-Based Structural Health Monitoring: A Review

Eltouny

Gomaa

Liang

2023

Sensors

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“…In this method, the acceleration response data due to moving loads are used as input and the Euclidean distance between the latent features at different stages is used to localize damage. Similarly, Yuan et al 56 proposed a one-dimensional CVAE feature extractor for localizing light rail squats. Benefiting from the Gaussianity of the latent variables, they propose using either elliptic envelope or one-class support vector machine (OC-SVM) techniques for anomaly detection.…”

Section: Deep Variational Encoder Architecturementioning

confidence: 99%

Section: Deep Variational Encoder Architecturementioning

confidence: 99%

Uncertainty‐aware structural damage warning system using deep variational composite neural networks

Eltouny

Liang

2023

Earthq Engng Struct Dyn

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Structural health monitoring (SHM) is, without a doubt, one of the most important assets for building resilient communities. The vast and rapidly advancing research in data science and machine learning has provided researchers in the civil engineering community with various tools that can facilitate the processing of significant amounts of gathered data. However, deep learning models are prone to mistakes, and with the catastrophic consequences that can happen due to damage misidentification, damage diagnosis models’ predictions should not be taken for granted. In this study, we present an uncertainty‐aware early‐warning system that can provide near real‐time SHM. The system utilizes a deep composite encoder‐decoder network that combines elements from convolutional neural networks, recurrent neural networks, and variational inference (VI) to provide damage index distributions. The framework can detect anomalies in the structural system during seismic events and provide a measure of uncertainty that can be used to question the model's predictions. To assess the system's validity and practicality, we apply our proposal to three real structures, two of which suffered damage during the 1994 Northridge earthquake. We found that the early warning system delivers an accurate, yet cautious, continuous monitoring that is capable of sending warning signals when damage occurs in the course of seismic events. Source code is available at: https://github.com/keltouny/VSCAN.

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“…The use of VAEs in SHM can go back to the early 2020s (Liu et al, 2019b ; Ma et al, 2020 ), presenting anomaly detection on railways and feature extraction via VAE. Since then, several studies have been presented employing the generative skill of VAEs in civil SHM for various purposes, such as damage and anomaly identification, and condition assessment (Anaissi et al, 2023 ; Pollastro et al, 2022 ; Xu et al, 2021b ; Yuan et al, 2021 ; Zhou et al, 2022 ), and optimal sensor placement (Sajedi & Liang, 2022 ) (Fig. 2 ), addressing data scarcity challenge in the SHM domain in one way or another.…”

Section: Deep Generative Modelsmentioning

confidence: 99%

A brief introductory review to deep generative models for civil structural health monitoring

Luleci,

Catbas

2023

AI Civ. Eng.

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The use of deep generative models (DGMs) such as variational autoencoders, autoregressive models, flow-based models, energy-based models, generative adversarial networks, and diffusion models has been advantageous in various disciplines due to their high data generative skills. Using DGMs has become one of the most trending research topics in Artificial Intelligence in recent years. On the other hand, the research and development endeavors in the civil structural health monitoring (SHM) area have also been very progressive owing to the increasing use of Machine Learning techniques. As such, some of the DGMs have also been used in the civil SHM field lately. This short review communication paper aims to assist researchers in the civil SHM field in understanding the fundamentals of DGMs and, consequently, to help initiate their use for current and possible future engineering applications. On this basis, this study briefly introduces the concept and mechanism of different DGMs in a comparative fashion. While preparing this short review communication, it was observed that some DGMs had not been utilized or exploited fully in the SHM area. Accordingly, some representative studies presented in the civil SHM field that use DGMs are briefly overviewed. The study also presents a short comparative discussion on DGMs, their link to the SHM, and research directions.

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An unsupervised method based on convolutional variational auto-encoder and anomaly detection algorithms for light rail squat localization

Cited by 24 publications

References 22 publications

Unsupervised Learning Methods for Data-Driven Vibration-Based Structural Health Monitoring: A Review

Unsupervised Learning Methods for Data-Driven Vibration-Based Structural Health Monitoring: A Review

Uncertainty‐aware structural damage warning system using deep variational composite neural networks

A brief introductory review to deep generative models for civil structural health monitoring

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