The goal of this repository is to help researchers get updated about the studies conducted using generative AI models in the civil Structural Health Monitoring (SHM) area.
Thus, the repository herein presents the direct and indirect use of Deep Generative Models (DGMs): Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Diffusion Models (DMs), Flow-Based Models (FBMs), Energy-Based Models (EBMs), and Autoregressive Models (AMs), in the context of civil SHM research area to date.
While the discriminative AI approaches have been prevalent ever since the data-driven applications were used in the civil SHM domain, the DGMs are the generative AI models used for generative AI tasks, and their use has been observed increasingly lately.
Generative AI, as seen in large language models like ChatGPT, LLaMA, BARD, or PaLM, and other image-based generative models used for text-to-image applications, such as Dalle, Midjourney, StableDiffusion, or Imagen, has become a part of our daily lives. The origins of the generative AI research field go back to the 2010s with the development of Deep Learning models, like RNNs and CNNs, which improved the generative tasks significantly, allowing computers to generate data that resembles human-generated content. The AI research field, in fact, dates back as early as the 1950s, when Alan Turing made important contributions. However, the term “generative AI” gained widespread recognition more recently with the release of generative models like ChatGPT. These generative AI models have demonstrated impressive generative skills and have been applied in various domains, from natural language generation to art creation.
The earliest use of generative AI started with using AMs more than a decade ago. The use of AMs has been a very popular research activity in many SHM studies, but largely for feature extraction purposes, more than the generative tasks. I have identified a few studies that use AMs for generative tasks e.g., signal reconstruction. However, there are over a hundred studies that deal with feature extraction (I've included a few of the representative recent studies that directly focused on data generation using AMs in this repo). Also, considering that subsequently developed DGMs, such as VAEs, GANs, or DMs, demonstrate better generative performances than AMs ref1, ref2. As a result, I've temporarily excluded AMs from the repository, with plans to revisit them in the future.
When the AMs are excluded, it is observed that a large portion of generative AI studies are implemented using GANs and then VAEs. I have observed one study using DMs, which is a much newer generative AI model compared to other DGMs. I have not observed any study using EBMs and FBMs in the context of civil SHM research.
As the civil SHM study area is growing rapidly, the scope of the literature review somewhat overlaps with other fields. I aimed to define the scope of this repo as the generative AI models applied to civil engineering structures, excluding any machinery tools/testbeds/rotating machines and pavement structures, but including monitoring of wind turbines.
The four main applications of generative AI in civil SHM are lost data reconstruction, data augmentation, data domain translation, and data denoising, repair, deblurring, and increased resolution. I categorized the others as the studies focused on direct-content generation and the studies that are not directly focused on content generation.
With the release of generative language models (ChatGPT, LLaMA, etc.) or generative art models (Dalle, Midjourney. etc.), I expect the next-generation studies in the SHM will look into (or they may have already started) integrating the APIs of these models for various kinds of research purposes, such as:
- Generating various crack images or point clouds using generative art models to train crack-classifier to improved detection performance or,
- Using generative language models to enhance the decision-making process after damage diagnosis/prognosis.
For instance, this source explains how to use ChatGPT API in your research purposes.
Please let me know in the Issues tab above or personally email me if I missed any studies or if you see any error/typo in this repo. You can find my email address on my website here. I'd appreciate any contribution to this repo.
Thanks!
If you'd like to cite this repo: Furkan Luleci, F. Necati Catbas. (2023). Generative Artificial Intelligence in Civil Structural Health Monitoring. https://github.com/furkan-luleci/GenerativeAI-in-SHM.git.
- Survey
- Lost Data Reconstruction
- Data Augmentation
- Data Domain Translation
- Data Denoising, Repair, Deblurring, and Increased Resolution
- Data Generation for Various Other SHM Applications
- Data Generation (focused on direct-content generation and model's generative-ability, e.g., data feature capturing, distribution construction, etc.)
- Leveraging Generative AI for Anomaly/Novelty/Outlier Detection, Damage Diagnosis/Prognosis, Physics-based Modeling, and other Condition Assessment Techniques - (the studies here are not directly focused on content generation)
A literature review: Generative adversarial networks for civil structural health monitoring
Furkan Luleci, F. Necati Catbas, Onur Avci
Frontiers in Built Environment [Paper]
07 November 2022
Generative adversarial networks review in earthquake-related engineering fields
Giuseppe Carlo Marano, Marco Martino Rosso, Angelo Aloisio, Giansalvo Cirrincione
Bulletin of Earthquake Engineering [Paper]
28 February 2023
A brief introductory review to deep generative models for civil structural health monitoring
Furkan Luleci, F. Necati Catbas
AI in Civil Engineering [Paper]
23 August 2023
Lost data reconstruction for structural health monitoring using deep convolutional generative adversarial networks
Xiaoming Lei, Limin Sun, Ye Xia
Structural Health Monitoring [Paper]
10 October 2020
Data driven structural dynamic response reconstruction using segment based generative adversarial networks
Gao Fan, Jun Li, Hong Hao, Yu Xin
Engineering Structures [Paper]
12 February 2021
Continuous missing data imputation with incomplete dataset by generative adversarial networks–based unsupervised learning for long-term bridge health monitoring
Huachen Jiang, Chunfeng Wan, Kang Yang, Youliang Ding, Songtao Xue
Structural Health Monitoring [Paper]
4 June 2021
Data Loss Reconstruction Method for a Bridge Weigh-in-Motion System Using Generative Adversarial Networks
Yizhou Zhuang,Jiacheng Qin,Bin Chen, Chuanzhi Dong, Chenbo Xue, Said M. Easa
Sensors [Paper]
23 January 2022
Deep learning and data augmentation based data imputation for structural health monitoring system in multi-sensor damaged state
Jiale Hou, Huachen Jiang, Chunfeng Wan, Letian Yi, Shuai Gao, Youliang Ding, Songtao Xue
Measurement [Paper]
20 April 2022
Missing data imputation framework for bridge structural health monitoring based on slim generative adversarial networks
Shuai Gao, Wenlong Zhao, Chunfeng Wan, Huachen Jiang, Youliang Ding, Songtao Xue
Measurement [Paper]
21 October 2022
Structural dynamic response reconstruction using self-attention enhanced generative adversarial networks
Gao Fan, Zhengyan He, Jun Li
Engineering Structures [Paper]
1 December 2022
Structural health monitoring response reconstruction based on UAGAN under structural condition variations with few-shot learning
Gao Fan, Zhengyan He, Jun Li
ResearchGate [Paper]
1 December 2022
Reconstruction of long-term strain data for structural health monitoring with a hybrid deep-learning and autoregressive model considering thermal effects
Chengbin Chen, Liqun Tang, Yonghui Lu, Yong Wang, Zejia Liu, Yiping Liu, Licheng Zhou, Zhenyu Jiang, Bao Yang
Engineering Structures, [Paper]
7 April 2023
Multi-channel response reconstruction using transformer based generative adversarial network
Wenhao Zheng, Jun Li, Qilin Li, Hong Hao
Earthquake Engineering and Structural Dynamics [Paper]
5 Jul 2023
Deep Learning-Based Imputation Framework in Bridge Health Monitoring using Generative Adversarial Networks
Saha, Sumit
Masters thesis, Indian Institute of Technology Hyderabad [Paper]
19 Jul 2023
Efficient Processing of Distributed Acoustic Sensing Data Using a Deep Learning Approach
Lihi Shiloh, Avishay Eyal, and Raja Giryes
Journal of Lightwave Technology [Paper]
May 29, 2019
Deep leaf-bootstrapping generative adversarial network for structural image data augmentation
Yuqing Gao, Boyuan Kong, Khalid M. Mosalam
Computer-Aided Civil and Infrastructure Engineering [Paper]
18 July 2019
Unsupervised domain adaptation with self-attention for post-disaster building damage detection
Yundong Li, Chen Lin, Hongguang Li, Wei Hu, Han Dong, Yi Liu
Neurocomputing [Paper]
14 July 2020
Railway Fastener Fault Diagnosis Based on Generative Adversarial Network and Residual Network Model
Dechen Yao, Qiang Sun, Jianwei Yang, Hengchang Liu, Jiao Zhang
Shochk and Vibration [Paper]
07 Nov 2020
Balanced semisupervised generative adversarial network for damage assessment from low-data imbalanced-class regime
Yuqing Gao, Pengyuan Zhai, Khalid M. Mosalam
Computer-Aided Civil and Infrastructure Engineering [Paper]
28 June 2021
A general framework for supervised structural health monitoring and sensor output validation mitigating data imbalance with generative adversarial networks-generated high-dimensional features
Mohammad Hesam Soleimani-Babakamali, Roksana Soleimani-Babakamali, Rodrigo Sarlo
Structural Health Monitoring [Paper]
13 July 2021
Fatigue Damage Diagnostics of Composites Using Data Fusion and Data Augmentation With Deep Neural Networks
Shweta Dabetwar, Stephen Ekwaro-Osire, João Paulo Dias
ASME J Nondestructive Evaluation [Paper]
23 August 2021
Data augmentation and its application in distributed acoustic sensing data denoising
Y X Zhao, Y Li, N Wu
Geophysical Journal International [Paper]
24 August 2021
Enhancement of Multi-Class Structural Defect Recognition Using Generative Adversarial Network
Hyunkyu Shin,Yonghan Ahn, Sungho Tae, Heungbae Gil, Mihwa Song, Sanghyo Lee
Sustainability [Paper]
16 November 2021
Crack Detection Based on Generative Adversarial Networks and Deep Learning
Gongfa Chen, Shuai Teng, Mansheng Lin, Xiaomei Yang, Xiaoli Sun
KSCE Journal of Civil Engineering [Paper]
24 January 2022
Pavement crack detection algorithm based on generative adversarial network and convolutional neural network under small samples
Boqiang Xu, Chao Liu
Measurement [Paper]
19 April 2022
The Multiclass Fault Diagnosis of Wind Turbine Bearing Based on Multisource Signal Fusion and Deep Learning Generative Model
Liang Zhang; Hao Zhang; Guowei Cai
IEEE [Paper]
27 May 2022
Attention-based generative adversarial network with internal damage segmentation using thermography
Rahmat Ali, Young-Jin Cha
KSCE Journal of Civil Engineering [Paper]
13 June 2022
Data Augmentation for Deep-Learning-Based Multiclass Structural Damage Detection Using Limited Information
Kyle Dunphy, Mohammad Navid Fekri,Katarina Grolinger, Ayan Sadhu
Sensors [Paper]
18 August 2022
Improvement of Fiber Bragg Grating Wavelength Demodulation System by Cascading Generative Adversarial Network and Dense Neural Network
Shuna Li, Sufen Ren, Shengchao Chen, Benguo Yu
Applied Sciences [Paper]
8 September 2022
Generative adversarial networks for labeled acceleration data augmentation for structural damage detection
Furkan Luleci, F. Necati Catbas, Onur Avci
Journal of Civil Structural Health Monitoring [Paper]
24 September 2022
A Novel Data Augmentation Method for Improved Visual Crack Detection Using Generative Adversarial Networks
Efstathios Branikas; Paul Murray; Graeme West
IEEE [Paper]
03 March 2023
Dual generative adversarial networks combining conditional assistance and feature enhancement for imbalanced fault diagnosis
Ranran Li, Shunming Li, Kun Xu, Mengjie Zeng, Xianglian Li
Structural Health Monitoring [Paper]
24 April 2023
A machine learning-based data augmentation strategy for structural damage classification in civil infrastructure system
Lechen Li & Raimondo Betti
Journal of Civil Structural Health Monitoring [Paper]
11 May 2023
Subdomain-Alignment Data Augmentation for Pipeline Fault Diagnosis: An Adversarial Self-Attention Network
Chuang Wang, Zidong Wang, Lifeng Ma, Hongli Dong, Weiguo Sheng
IEEE [Paper]
12 May 2023
Data Anomaly Detection through Semisupervised Learning Aided by Customised Data Augmentation Techniques
Xiaoyou Wang, Yao Du, Xiaoqing Zhou, Yong Xia
Structural Control and Health Monitoring [Paper]
22 July 2023
Effective Generative Data Augmentation in Condition Monitoring
Daniel Ortiz-Arroyo, Petar Durdevic
IEEE Sensors Journal [Paper]
28 August 2023
Self-Supervised Structure Learning for Crack Detection Based on Cycle-Consistent Generative Adversarial Networks
Kaige Zhang, Yingtao Zhang, H. D. Cheng
Journal of Computing in Civil Engineering [Paper]
21 Jan 2020
Generative Damage Learning for Concrete Aging Detection using Auto-flight Images
Takato Yasuno, Akira Ishii, Junichiro Fujii, Masazumi Amakata, Yuta Takahashi
37th International Symposium on Automation and Robotics in Construction [Paper]
27 Jun 2020
Forecasting infrastructure deterioration with inverse GANs
Eric Bianchi, Matthew Hebdon
Proceedings Volume 11843, Applications of Machine Learning 2021 [Paper]
1 August 2021
On the application of generative adversarial networks for nonlinear modal analysis
G. Tsialiamanis, M.D. Champneys, N. Dervilis, D.J. Wagg, K. Worden
Mechanical Systems and Signal Processing [Paper]
8 October 2021
Damage analysis and quantification of RC beams assisted by Damage-T Generative Adversarial Network
Yanzhi Qi, Cheng Yuan, Peizhen Li, Qingzhao Kong
Engineering Applications of Artificial Intelligence [Paper]
28 October 2022
Structural State Translation: Condition Transfer between Civil Structures Using Domain-Generalization for Structural Health Monitoring
Furkan Luleci, F. Necati Catbas
arXiv [Paper]
28 December 2022
Improved undamaged-to-damaged acceleration response translation for Structural Health Monitoring
Furkan Luleci, Onur Avci, F. Necati Catbas
Engineering Applications of Artificial Intelligence [Paper]
22 March 2023
Establishment and evaluation of conditional GAN-based image dataset for semantic segmentation of structural cracks
Furkan Luleci, F. Necati Catbas
Engineering Structures [Paper]
31 March 2023
CycleGAN for undamaged-to-damaged domain translation for structural health monitoring and damage detection
Furkan Luleci, Onur Avci, F. Necati Catbas
Mechanical Systems and Signal Processing [Paper]
23 April 2023
Unsupervised deep learning-based ground penetrating radar image translation for internal defect recognition of underground engineering structures
Zhengfang Wang, Ming Lei, Jing Wang, Bo Li, Jing Xu, Yuchen Jiang, Qingmei Sui, Yao Li
Structural Health Monitoring [Paper]
16 May 2023
Condition transfer between prestressed bridges using structural state translation for structural health monitoring
Furkan Luleci, F. Necati Catbas
AI in Civil Engineering [Paper]
02 August 2023
Application of improved least-square generative adversarial networks for rail crack detection by AE technique
Wang Kangwei, Zhang Xin, Hao Qiushi, Wang Yan, Shen Yi
Neurocomputing [Paper]
30 December 2018
Deep Learning–Based Enhancement of Motion Blurred UAV Concrete Crack Images
Yiqing Liu, Justin K. W. Yeoh, David K. H. Chua
Journal of Computing in Civil Engineering [Paper]
10 June 2020
Recovering compressed images for automatic crack segmentation using generative models
Yong Huang, Haoyu Zhang, Hui Li, Stephen Wu
Mechanical Systems and Signal Processing [Paper]
29 June 2020
Improved Image Based Super Resolution and Concrete Crack Prediction Using Pre-Trained Deep Learning Models
Karunanithi Sathya, D. Sangavi, P. Sridharshini, M. Manobharathi, G. Jayapriya
Journal of Soft Computing in Civil Engineering [Paper]
24 July 2020
A two-stage data cleansing method for bridge global positioning system monitoring data based on bi-direction long and short term memory anomaly identification and conditional generative adversarial networks data repair
Kang Yang, Youliang Ding, Huachen Jiang, Hanwei Zhao, Gan Luo
Structural Control and Health Monitoring [Paper]
11 May 2022
Vision-based displacement measurement enhanced by super-resolution using generative adversarial networks
Chujin Sun, Donglian Gu, Yi Zhang, Xinzheng Lu
Structural Control and Health Monitoring [Paper]
13 July 2022
A fast sparsity-free compressive sensing approach for vibration data reconstruction using deep convolutional GAN
Guan-Sen Dong, Hua-Ping Wan, Yaozhi Luo, Michael D. Todd
Mechanical Systems and Signal Processing [Paper]
26 November 2022
Anomaly detection for wind turbine pitch bearings via autoencoder enhanced nonlinear autoregressive model
Chao Zhang, Long Zhang
IEEE [Paper]
13 March 2023
CrackDiffusion: crack inpainting with denoising diffusion models and crack segmentation perceptual score
Lizhou Chen, Luoyu Zhou, Lei Li, Mingzhang Luo
Smart Materials and Structures [Paper]
30 March 2023
Learning Structure for Concrete Crack Detection Using Robust Super-Resolution with Generative Adversarial Network
Jin Kim, Seungbo Shim, Seok-Jun Kang, Gye-Chun Cho
Structural Control and Health Monitoring [Paper]
04 Apr 2023
Multi-component condition monitoring method for wind turbine gearbox based on adaptive noise reduction
Yang Chen, Yongqian Liu, Shuang Han, Yanhui Qiao
Structural Health Monitoring [Paper]
12 June 2023
Structural floor acceleration denoising method using generative adversarial network
Junkai Shen, Lingxin Zhang, Koichi Kusunoki, Trevor Zhiqing Yeow
Soil Dynamics and Earthquake Engineering [Paper]
4 July 2023
Robust multitask compressive sampling via deep generative models for crack detection in structural health monitoring
Haoyu Zhang, Stephen Wu, Yong Huang, Hui Li
Structural Health Monitoring [Paper]
17 July 2023
Data Generation (focused on direct-content generation and model's generative-ability, e.g., data feature capturing, distribution reconstruction, etc.)
Generative Model of Acceleration Data for Deep Learning-based Damage Detection for Bridges Using Generative Adversarial Network
Lee Kanghyeok, Shin Do Hyoung
Journal of KIBIM [Paper]
31 March 2019
A Generative Adversarial Network Model for Simulating Various Types of Human-Induced Loads
Jiecheng Xiong, Jun Chen
International Journal of Structural Stability and Dynamics [Paper]
19 April 2019
Deep digital twins for detection, diagnostics and prognostics
Wihan Booyse, Daniel N. Wilke, Stephan Heyns
Mechanical Systems and Signal Processing [Paper]
1 February 2020
An application of generative adversarial networks in structural health monitoring
G.Tsialiamanis, E.Chatzi, N.Dervilis, D.J.Wagg, K.Worden
Proceedings of the XI International Conference on Structural Dynamics [Paper]
30 September 2020
Deep-Learning-Based Bridge Condition Assessment by Probability Density Distribution Reconstruction of Girder Vertical Deflection and Cable Tension Using Unsupervised Image Transformation Model
Yang Xu, Yadi Tian, Yufeng Zhang, Hui Li
European Workshop on Structural Health Monitoring [Paper]
09 January 2021
Probabilistic vehicle weight estimation using physics-constrained generative adversarial network
Yang Yu, C. S. Cai, Yongming Liu
Computer-Aided Civil and Infrastructure Engineering [Paper]
30 March 2021
On Generating Parametrised Structural Data Using Conditional Generative Adversarial Networks
G. Tsialiamanis, D. J. Wagg, N. Dervilis, K. Worden
Data Science in Engineering [Paper]
17 May 2021
A Generative Adversarial Network Based Autoencoder for Structural Health Monitoring
Giorgia Colombera,Luca Rosafalco, Matteo Torzoni, Filippo Gatti, Stefano Mariani, Andrea Manzoni, Alberto Corigliano
Computuer Sciences Mathematic Forum [Paper]
22 September 2021
Time-Domain Signal Synthesis with Style-Based Generative Adversarial Networks Applied to Guided Waves
Mateusz Heesch, Krzysztof Mendrok, Ziemowit Dworakowski
Artificial Intelligence and Soft Computing [Paper]
05 October 2021
A Wasserstein generative adversarial network-based approach for real-time track irregularity estimation using vehicle dynamic responses
Zhandong Yuan,Jun Luo,Shengyang Zhu, Wanming Zhai
Vehicle System Dynamics [Paper]
24 November 2021
Deep generative Bayesian optimization for sensor placement in structural health monitoring
Seyedomid Sajedi, Xiao Liang
IEEE [Paper]
22 December 2021
Generative Adversarial Networks for Data Generation in Structural Health Monitoring
Furkan Luleci, F. Necati Catbas, Onur Avci
Frontiers in Built Environment [Paper]
11 February 2022
Diagnostic-Quality Guided Wave Signals Synthesized Using Generative Adversarial Neural Networks
Mateusz Heesch, Michał Dziendzikowski, Krzysztof Mendrok, Ziemowit Dworakowski
Sensors [Paper]
19 May 2022
Self-Supervised Metalearning Generative Adversarial Network for Few-Shot Fault Diagnosis of Hoisting System With Limited Data
Yang Li, Feiyun Xu, Chi-Guhn Lee
IEEE [Paper]
27 May 2022
Autoregressive Deep Learning Models for Bridge Strain Prediction
Anastasios Panagiotis Psathas, Lazaros Iliadis, Dimitra V. Achillopoulou, Antonios Papaleonidas, Nikoleta K. Stamataki, Dimitris Bountas, Ioannis M. Dokas
Engineering Applications of Neural Networks [Paper]
10 June 2022
Dual generative adversarial networks for automated component layout design of steel frame-brace structures
Bochao Fu, Yuqing Gao, Wei Wang
arXiv [Paper]
25 November 2022
In situ health monitoring of multiscale structures and its instantaneous verification using mechanoluminescence and dual machine learning
Seong Yeon Ahn, Suman Timilsina, Ho Geun Shin, Jeong Heon Lee, Seong-Hoon Kim, Kee-Sun Sohn, Yong Nam Kwon 5, Kwang Ho Lee, Ji Sik Kim
iScience [Paper]
January 20, 2023
Conditional deep generative models as surrogates for spatial field solution reconstruction with quantified uncertainty in Structural Health Monitoring applications
Nicholas E. Silionis, Theodora Liangou, Konstantinos N. Anyfantis
arXiv [Paper]
14 February 2023
Crack image generation algorithm based on deep convolutional generative adversarial network
DingJun Zhang, MingChao Liao, XiXiang Wang, LaLao Gao
Second International Conference on Green Communication, Network, and Internet of Things [Paper]
8 March 2023
Reconstruction of long-term strain data for structural health monitoring with a hybrid deep-learning and autoregressive model considering thermal effects
Chengbin Chen, Liqun Tang, Yonghui Lu, Yong Wang, Zejia Liu, Yiping Liu, Licheng Zhou, Zhenyu Jiang, Bao Yang
Engineering Structures [Paper]
7 April 2023
A fast-response-generation method for single-layer reticulated shells based on implicit parameter model of generative adversarial networks
Xiaonong Guo, Jindong Zhang, Shaohan Zong, Shaojun Zhu
Journal of Building Engineering [Paper]
15 April 2023
Acoustic Sensor Placement Optimization for Compressor Based on Adversarial Transfer Learning and Vibro-Acoustic Simulation
Di Song, Junxian Shen,Tianchi Ma, Feiyun Xu
IEEE [Paper]
10 May 2023
Generative adversarial network for predicting visible deterioration and NDE condition maps in highway bridge decks
Amirali Najafi, John Braley, Nenad Gucunski, Ali Maher
Journal of Infrastructure Intelligence and Resilience [Paper]
2 June 2023
Implicit parametric modal expansion method for single-layer reticulated shells based on generative adversarial network
Jindong Zhang, Xiaonong Guo, Shaohan Zong
Structures [Paper]
11 June 2023
Generative adversarial networks for real-time realistic physics simulations
Mark Harlow, Matthew Martinez, Xiaoyan Han, Stoian Borissov, Jason Hill
Proceedings Volume 12536, Thermosense: Thermal Infrared Applications XLV [Paper]
12 June 2023
Neural Extended Kalman Filters for Learning and Predicting Dynamics of Structural Systems
Wei Liu, Zhilu Lai, Kiran Bacsa, Eleni Chatzi
Structural Health Monitoring [Paper]
29 June 2023
A Data-Driven Based Response Reconstruction Method of Plate Structure with Conditional Generative Adversarial Network
He Zhang, Chengkan Xu, Jiqing Jiang, Jiangpeng Shu, Liangfeng Sun, Zhicheng Zhang
Sensors [Paper]
28 July 2023
Multi-modal generative adversarial networks for synthesizing time-series structural impact responses
Zhymir Thompson, Austin R.J. Downey, Jason D. Bakos, Jie Wei, Jacob Dodson
Mechanical Systems and Signal Processing [Paper]
9 September 2023
Leveraging Generative AI for Anomaly/Novelty/Outlier Detection, Damage Diagnosis/Prognosis, Physics-based Modeling, and other Condition Assessment Techniques - (the studies here are not directly focused on content generation)
Variational autoencoder-based approach for rail defect identification
Yuan Hao Wei, Yi Qing Ni
The Hong Kong Polytechnic University [Paper]
1 Jan 2019
Deep Unsupervised Learning for Condition Monitoring and Prediction of High Dimensional Data with Application on Windfarm SCADA Data
C. Mylonas, I. Abdallah & E. N. Chatzi
Model Validation and Uncertainty Quantification [Paper]
31 May 2019
Structural damage identification based on unsupervised feature-extraction via Variational Auto-encoder
Xirui Ma, Yizhou Lin, Zhenhua Nie, Hongwei Ma
Measurement [Paper]
13 April 2020
Toward data anomaly detection for automated structural health monitoring: Exploiting generative adversarial nets and autoencoders
Jianxiao Mao, Hao Wang, Billie F Spencer, Jr
Structural Health Monitoring [Paper]
7 June 2020
Infrastructure degradation and post-disaster damage detection using anomaly detecting generative adversarial networks
S. M. Tilon, F. Nex, D. Duarte, N. Kerle, and G. Vosselman
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. [Paper]
03 August 2020
Wind Turbine Gearbox Failure Detection Based on SCADA Data: A Deep Learning-Based Approach
Luoxiao Yang; Zijun Zhang
IEEE [Paper]
18 December 2020
Post-Disaster Building Damage Detection from Earth Observation Imagery Using Unsupervised and Transferable Anomaly Detecting Generative Adversarial Networks
Sofia Tilon,Francesco Nex,Norman Kerle, George Vosselman
Remote Sensing [Paper]
21 December 2020
Prognostics With Variational Autoencoder by Generative Adversarial Learning
Yu Huang, Yufei Tang, James VanZwieten
IEEE [Paper]
28 January 2021
Conditional variational autoencoders for probabilistic wind turbine blade fatigue estimation using Supervisory, Control, and Data Acquisition data
Charilaos Mylonas, Imad Abdallah, Eleni Chatzi
Wind Energy [Paper]
11 February 2021
VAE-TALSTM: a temporal attention and variational autoencoder-based long short-term memory framework for dam displacement prediction
Xiaosong Shu, Tengfei Bao, Yangtao Li, Jian Gong & Kang Zhang
Engineering with Computers [Paper]
22 April 2021
Bat Ant Lion Optimization-Based Generative Adversarial Network For Structural Heath Monitoring In IoT
Yoganand S, Chithra S
The Computer Journal [Paper]
07 June 2021
Dam anomaly assessment based on sequential variational autoencoder and evidence theory
Xiaosong Shu, Tengfei Bao, Ruichen Xu, Yangtao Li, Kang Zhang
Applied Mathematical Modelling [Paper]
27 June 2021
Generative Adversarial Network for Damage Identification in Civil Structures
Zahra Rastin, Gholamreza Ghodrati Amiri, Ehsan Darvishan
Shock and Vibration [Paper]
06 Sept 2021
An unsupervised method based on convolutional variational auto-encoder and anomaly detection algorithms for light rail squat localization
Zhandong Yuan, Shengyang Zhu, Chao Chang, Xuancheng Yuan, Qinglai Zhang, Wanming Zhai
Construction and Building Materials [Paper]
13 November 2021
Toward a general unsupervised novelty detection framework in structural health monitoring
Mohammad Hesam Soleimani-Babakamali, Reza Sepasdar, Kourosh Nasrollahzadeh, Ismini Lourentzou, Rodrigo Sarlo
Computer-Aided Civil and Infrastructure Engineering [Paper]
21 January 2022
Data anomaly detection for structural health monitoring using a combination network of GANomaly and CNN
Gaoyang Liu, Yanbo Niu, Weijian Zhao, Yuanfeng Duan and Jiangpeng Shu
Smart Structures and Systems [Paper]
29 January 2022
A system reliability approach to real-time unsupervised structural health monitoring without prior information
Mohammad Hesam Soleimani-Babakamali, Reza Sepasdar, Kourosh Nasrollahzadeh, Rodrigo Sarlo
Frontiers in Built Environment [Paper]
22 February 2022
Unsupervised dam anomaly detection with spatial–temporal variational autoencoder
Xiaosong Shu, Tengfei Bao, Yuhang Zhou, Ruichen Xu, Yangtao Li, Kang Zhang
Structural Health Monitoring [Paper]
4 April 2022
A novel vision-based method for loosening detection of marked T-junction pipe fittings integrating GAN-based segmentation and SVM-based classification algorithms
Xinjian Deng, Jianhua Liu, Hao Gong, Jiayu Huang
Journal of Intelligent Manufacturing [Paper]
22 April 2022
Variational AutoEncoder (VAE) Boosted Parametric Reduced Order Modelling (pROM)
Konstantinoscc Vlachas, Thomas Simpson, Anthony Garland, Carianne Martinez, Dane Quinn, Eleni Chatzi,
6th Workshop on Nonlinear System Identification Benchmarks [Paper]
26 April 2022
Unsupervised deep learning method for bridge condition assessment based on intra-and inter-class probabilistic correlations of quasi-static responses
Yang Xu, Yadi Tian, and Hui Li
Structural Health Monitoring [Paper]
21 May 2022
RCNN-GAN: An Enhanced Deep Learning Approach Towards Detection of Road Cracks
Shadrack Fred Mahenge, Stephen Wambura, Licheng Jiao
ICCDA 2022: 2022 The 6th International Conference on Compute and Data Analysis [Paper]
23 May 2022
Generative adversarial networks with fuzzy clustering on damage detection of rc beams using piezoceramic sensing signals
YUAN Cheng, XIONG Qing-song, QIN Xiao-ming, XIONG Hai-bei, KONG Qing-zhao
Engineering Mechanics [Paper]
30 May 2022
Anomaly Detection with Autoencoders as a Tool for Detecting Sensor Malfunctions
Artur Liebert, Wolfgang Weber, Sebastian Reif, Bernd Zimmering, Oliver Niggemann
IEEE [Paper]
18 July 2022
On an Application of Generative Adversarial Networks on Remaining Lifetime Estimation
G. Tsialiamanis, D. Wagg, N. Dervilis, K. Worden
arXiv [Paper]
18 August 2022
Anomaly Detection by Unsupervised Adversarial Generative Self-labelling Autoencoder
Deyi Zeng
IEEE [Paper]
05 August 2022
Dam safety assessment through data-level anomaly detection and information fusion
Yuhang Zhou, Xiaosong Shu, Tengfei Bao, Yangtao Li, Kang Zhang
Structural Health Monitoring [Paper]
16 August 2022
Multiclass damage detection in concrete structures using a transfer learning-based generative adversarial networks
Kyle Dunphy, Ayan Sadhu, Jinfei Wang
Structural Control and Health Monitoring [Paper]
19 August 2022
Adversarially-Trained Tiny Autoencoders for Near-Sensor Continuous Structural Health Monitoring
Alessio Burrello; Giacomo Sintoni; Davide Brunelli; Luca Benini
IEEE [Paper]
5 September 2022
Multi-objective variational autoencoder: an application for smart infrastructure maintenance
Ali Anaissi, Seid Miad Zandavi, Basem Suleiman, Mohamad Naji & Ali Braytee
Applied Intelligence [Paper]
20 September 2022
A comparative study of attention mechanism and generative adversarial network in facade damage segmentation
Fangzheng Lin, Jiesheng Yang, Jiangpeng Shu, Raimar J. Scherer
arXiv [Paper]
27 September 2022
Modern Crack Detection for Bridge Infrastructure Maintenance Using Machine Learning
Hafiz Suliman Munawar, Ahmed W. A. Hammad, S. Travis Waller & Md Rafiqul Islam
Human-Centric Intelligent Systems [Paper]
28 September 2022
DEGAN: Time Series Anomaly Detection using Generative Adversarial Network Discriminators and Density Estimation
Yueyan Gu, Farrokh Jazizadeh
arXiv [Paper]
5 October 2022
A deep generative model based on CNN-CVAE for wind turbine condition monitoring
Jiarui Liu, Guotian Yang, Xinli Li, Shumin Hao, Yingming Guan, Yaqi Li
Measurement Science and Technology [Paper]
6 December 2022
An unsupervised structural health monitoring framework based on Variational Autoencoders and Hidden Markov Models
Eduardo M. Coraça, Janito V. Ferreira, Eurípedes G.O. Nóbrega
Reliability Engineering & System Safety [Paper]
9 December 2022
A GAN-based fault detection for dynamic process with deconvolutional networks
Dapeng Zhang, Zhiwei Gao
IEEE [Paper]
15 December 2022
Unsupervised structural damage detection based on an improved generative adversarial network and cloud model
Yongpeng Luo, Xu Guo, Lin-Kun Wang, Jin-Ling Zheng, Jing-Liang Liu, Fei-Yu Liao
Journal of Low Frequency Noise, Vibration and Active Control [Paper]
12 January 2023
Structural Nonlinear Model Updating Based on an Improved Generative Adversarial Network
Zi-Qing Yuan, Yu Xin, Zuo-Cai Wang, Ya-Jie Ding, Jun Wang, Dong-Hui Wang
Structural Control and Health Monitoring [Paper]
07 February 2023
Unsupervised learning-based framework for indirect structural health monitoring using adversarial autoencoder
A. Calderon Hurtado, K. Kaur, M. Makki Alamdari, E. Atroshchenko, K.C. Chang, C.W. Kim
arXiv [Paper]
11 February 2023
VpROM: A novel Variational AutoEncoder-boosted Reduced Order Model for the treatment of parametric dependencies in nonlinear systems
Thomas Simpson, Konstantinos Vlachas, Anthony Garland, Nikolaos Dervilis, Eleni Chatzi
arXiv [Paper]
11 April 2023
A Semi-Supervised Learning Approach for Pixel-Level Pavement Anomaly Detection
Ruiqi Ren, Peixin Shi, Pengjiao Jia, Xiangyang Xu
IEEE [Paper]
21 April 2023
Uncertainty-aware structural damage warning system using deep variational composite neural networks
Kareem A. Eltouny, Xiao Liang
Earthquake Engineering & Structural Dynamics [Paper]
24 April 2023
Zero-shot transfer learning for structural health monitoring using generative adversarial networks and spectral mapping
Mohammad Hesam Soleimani-Babakamali, Roksana Soleimani-Babakamali, Kourosh Nasrollahzadeh, Onur Avci, Serkan Kiranyaz, Ertugrul Taciroglu
Mechanical Systems and Signal Processing [Paper]
9 May 2023
Early Detection and Diagnosis of Wind Turbine Abnormal Conditions Using an Interpretable Supervised Variational Autoencoder Model
Adaiton Oliveira-Filho, Adaiton Oliveira-Filho, Ryad Zemouri, Philippe Cambron, Antoine Tahan
Energies [Paper]
6 June 2023
Microstructural image based convolutional neural networks for efficient prediction of full-field stress maps in short fiber polymer composites
S. Gupta, T. Mukhopadhyay, V. Kushvaha
Defence Technology [Paper]
23 June 2023
Semi-Supervised Detection of Structural Damage Using Variational Autoencoder and a One-Class Support Vector Machine
Andrea Pollastro; Giusiana Testa; Antonio Bilotta; Roberto Prevete
IEEE [Paper]
03 July 2023
Concrete Crack Detection Using Deep Convolutional Generative Adversarial Network
Biswajit Padhi, Motahar Reza, Md. Salman Shams & Arcot Navya Sai
Advanced Computing [Paper]
14 July 2023
Deep Representation Clustering of Multi-Type Damage Features Based on Unsupervised Generative Adversarial Network
Feng-Liang Zhang, Xiao Li, Jun Lei, Wei Xiang
SSRN [Paper]
22 July 2023
Deep generative models for unsupervised delamination detection using guided waves
Mahindra Rautela, Amin Maghareh, Shirley Dyke, S. Gopalakrishnan
arXiv [Paper]
10 August 2023