An Unsupervised Tunnel Damage Identification Method Based on Convolutional Variational Auto-Encoder and Wavelet Packet Analysis

Zhang, Yonglai; Xie, Xuansong; Li, Hongqiao; Zhou, Biao

doi:10.3390/s22062412

Cited by 19 publications

(10 citation statements)

References 25 publications

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“…Estimating the flexibility matrix, however, can be challenging for complex structures. Zhang et al [ 73 ] developed an unsupervised tunnel damage detection method using wavelet packet energy of trains’ dynamic response data, which are fed into a CVAE. The RMSE is used as a damage index, while the relative entropy of wavelet packet energy is used to localize damage.…”

Section: Unsupervised Learning Shm Based On Artificial Neural Networkmentioning

confidence: 99%

“…For univariate outlier analysis, a statistical significance test (e.g., z-test or t -test) is commonly used. A threshold can be set using confidence intervals [ 83 , 108 , 130 , 136 ], significance [ 62 , 73 , 88 , 102 , 120 ], percentiles [ 58 , 63 , 64 , 66 , 74 ], or other data statistics. For multidimensional features, MD, or MSD, is often used [ 59 , 67 , 72 , 82 , 89 , 90 , 99 , 116 , 132 ].…”

Section: Novelty Detection Techniquesmentioning

confidence: 99%

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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%

Section: Novelty Detection Techniquesmentioning

confidence: 99%

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

Eltouny

Gomaa

Liang

2023

Sensors

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“…A flexibility disassembly method is then used to compare the input and the prediction to detect damaged members. A CVAE-based tunnel damage detection method was developed by Zhang et al 58 using spectrograms obtained from wavelet packet decomposition of trains' dynamic response data. The rootmean-square error (RMSE) of the network output is used as a DI while the wavelet packet energy relative entropy is used for localization.…”

Section: Deep Variational Encoder Architecturementioning

confidence: 99%

“…A flexibility disassembly method is then used to compare the input and the prediction to detect damaged members. A CVAE‐based tunnel damage detection method was developed by Zhang et al 58 . using spectrograms obtained from wavelet packet decomposition of trains’ dynamic response data.…”

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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“…In [54], the authors proposed a damage detection method based on a VAE ensemble to compute damage statistics on Evidence Variational Lower Bound (ELBO) values in order to classify each input as damaged or not using a user-defined decision rule based on fixed threshold value. In [55], a method based on convolutional VAE is proposed to detect tunnel damages from vibration data processed using Wavelet Packet Decomposition (WPD) [56] in order to produce a damage index that, according to a fixed threshold value, is used to classify the input data as damaged or not.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised detection of structural damage using Variational Autoencoder and a One-Class Support Vector Machine

Pollastro¹,

Testa²,

Bilotta³

et al. 2022

Preprint

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In recent years, Artificial Neural Networks (ANNs) have been introduced in Structural Health Monitoring (SHM) systems. A semi-supervised method with a data-driven approach allows the ANN training on data acquired from an undamaged structural condition to detect structural damages. In standard approaches, after the training stage, a decision rule is manually defined to detect anomalous data. However, this process could be made automatic using machine learning methods, whom performances are maximised using hyperparameter optimization techniques. The paper proposes a semi-supervised method with a datadriven approach to detect structural anomalies. The methodology consists of: (i) a Variational Autoencoder (VAE) to approximate undamaged data distribution and (ii) a One-Class Support Vector Machine (OC-SVM) to discriminate different health conditions using damage sensitive features extracted from VAE's signal reconstruction. The method is applied to a scale steel structure that was tested in nine damage's scenarios by IASC-ASCE Structural Health Monitoring Task Group.

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An Unsupervised Tunnel Damage Identification Method Based on Convolutional Variational Auto-Encoder and Wavelet Packet Analysis

Cited by 19 publications

References 25 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

Unsupervised detection of structural damage using Variational Autoencoder and a One-Class Support Vector Machine

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