Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

Cited by (798)

Items 201–300 of 798

1. Review on crack detection in civil infrastructure using structural health monitoring and machine learning techniques
   Muhammad Zeeshan Khan, Maryam Shahzadi, Asim Ali Khan, Umar Azam Ali, Muhammad Arshad Shehzad Hassan, Majid Hussain · 2025 · Innovative Infrastructure Solutions
2. Digital Twin and AI Models for Infrastructure Resilience: A Systematic Knowledge Mapping
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3. A hybrid modeling strategy for training data generation in machine learning-based structural health monitoring
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4. DNN surrogate model based cable force optimization for cantilever erection construction of large span arch bridge with concrete filled steel tube
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5. Machine Learning Methods for Geotechnical Site Characterization and Scour Assessment
   Negin Yousefpour, Zhongqiang Liu, Chao Zhao · 2024 · Transportation Research Record Journal of the Transportation Research Board
6. A review on full-, zero-, and partial-knowledge based predictive models for industrial applications
   Stefano Zampini, Guido Parodi, Luca Oneto, Andrea Coraddu, Davide Anguita · 2025 · Information Fusion
7. A review of image-based deep learning methods for crack detection
   Vindhyesh Pandey, Shambhu Sharan Mishra · 2025 · Multimedia Tools and Applications
8. Artificial Intelligence and Data Analytics for Structural Health Monitoring: A Review of Recent Developments
   Shrikant M. Harle, Amol P. Bhagat, Ruchita Ingole, Nilesh Suresh Zanjad · 2025 · Archives of Computational Methods in Engineering
9. A hybrid data-physics framework with conformal GNN for enhanced damage identification
   Armin Dadras Eslamlou, Arshia Ghasemlou, Brais Barros, Belén Riveiro · 2025 · Advanced Engineering Informatics
10. Fiber-Optic Sensor-Based Structural Health Monitoring with Machine Learning: A Task-Oriented and Cross-Domain Review
    Yasir Mahmood, Nof Yasir, Kathryn Quenette, Gul Badin, Ying Huang, Luyang Xu · 2026 · Sensors
11. Fusion of experimental and synthetic data for reliable prediction of steel connection behaviour using machine learning
    Manuela Cabrera, Jelena Ninić, Walid Tizani · 2023 · Engineering With Computers
12. Modification and Evaluation of Attention-Based Deep Neural Network for Structural Crack Detection
    Hangming Yuan, Tao Jin, Xiao‐Wei Ye · 2023 · Sensors
13. Review of Remote Sensing Approaches and Soft Computing for Infrastructure Monitoring
    Aggeliki Kyriou, Vassiliki Mpelogianni, Konstantinos G. Nikolakopoulos, Peter P. Groumpos · 2023 · Geomatics
14. Data anomaly detection with automatic feature selection and deep learning
    Huachen Jiang, Ensheng Ge, Chunfeng Wan, Shu Li, Ser Tong Quek, Kang Yang, Youliang Ding, Songtao Xue · 2023 · Structures
15. Computer vision-based quantification of updated stiffness for damaged RC columns after earthquake
    Mohammadjavad Hamidia, Majid Sheikhi, Amir Hossein Asjodi, Kiarash M. Dolatshahi · 2024 · Advances in Engineering Software
16. SHM-Traffic: DRL and Transfer Learning Based UAV Control for Structural Health Monitoring of Bridges With Traffic
    Divija Swetha Gadiraju, Saeed Eftekhar Azam, Deepak Khazanchi · 2025 · IEEE Access
17. A Lightweight SHM Framework Based on Adaptive Multisensor Fusion Network and Multigeneration Knowledge Distillation
    Li, Sijue, Liu, Fengyi, Peng, Gaoliang, Cheng, Feng, Zhao, Benqi, Ji, Mengyu · 2022 · IEEE Transactions on Instrumentation and Measurement
18. Segment anything model-based crack segmentation using low-rank adaption fine-tuning
    Yapeng Guo, Yang Xu, Hongtao Cui, Minghao Dang, Shunlong Li · 2024 · Structural Health Monitoring
19. Bayesian optimization bidirectional LSTM approach for the condition assessment of underground-operating trains
    Yu-Ling Wang, Yuhang Lu, Yanke Tan, Wai Kei Ao, Yi‐Qing Ni, Qing-Chen Tang · 2025 · Journal of Civil Structural Health Monitoring
20. Review of State-of-the-art of structural health monitoring in hydrogen composite pressure vessels
    Lyazid Bouhala, Jérôme Polesel‐Maris, Argyrios Karatrantos, Séverine Perbal, Björn Senf, Alexander Hiekel, Heiner Reinhardt, Alexander Rauscher, Thomas F. Mader · 2025 · Composites Part C Open Access
21. Unsupervised deep learning framework for damage identification under ambient excitations: trait of damage localization and demonstrative applications
    Jinwen Huang, Zhimeng Huang, Chunhui Xie, Yufeng Zhang, Wiesław Mieczysław Ostachowicz, Maosen Cao · 2026 · Mechanical Systems and Signal Processing
22. Data-driven modeling for the dynamic behavior of nonlinear vibratory systems
    Huizhen Liu, Chengying Zhao, Xianzhen Huang, Guo Yao · 2023 · Nonlinear Dynamics
23. Performance evaluation of deep learning algorithms for heat loss damage classification in buildings from UAV-borne infrared images
    Shweta Dabetwar, Richa Padhye, Nitin Nagesh Kulkarni, Christopher Niezrecki, Alessandro Sabato · 2023 · Journal of Building Engineering
24. Deep learning metasensor for crack-width assessment and self-healing evaluation in concrete
    Jacek Jakubowski, Kamil Tomczak · 2024 · Construction and Building Materials
25. Deep neural network for damage detection in Infante Dom Henrique bridge using multi-sensor data
    Ana Fernández-Navamuel, David Pardo, Filipe Magalhães, Diego Zamora-Sánchez, Ángel J. Omella, David García-Sánchez · 2024 · Structural Health Monitoring
26. The Duo of Visual Servoing and Deep Learning-Based Methods for Situation-Aware Disaster Management: A Comprehensive Review
    Senthil Kumar Jagatheesaperumal, Mohammad Mehedi Hassan, Md. Rafiul Hassan, Giancarlo Fortino · 2024 · Cognitive Computation
27. Modeling relationships for field strain data under thermal effects using functional data analysis
    Huachen Jiang, Chunfeng Wan, Kang Yang, Youliang Ding, Songtao Xue · 2021 · Measurement
28. Detection of Barely Visible Impact Damage in Polymeric Laminated Composites Using a Biomimetic Tactile Whisker
    Sakineh Fotouhi, Saber Khayatzadeh, Wei Xia Pui, Mahdi Damghani, Mahdi Bodaghi, Mohamad Fotouhi · 2021 · Polymers
29. A data-driven approach for linear and nonlinear damage detection using variational mode decomposition and GARCH model
    Vahidreza Gharehbaghi, Hashem Kalbkhani, Ehsan Noroozinejad Farsangi, T.Y. Yang, Seyedali Mirjalili · 2022 · Engineering With Computers
30. Road Damage Detection Using the Hunger Games Search with Elman Neural Network on High-Resolution Remote Sensing Images
    Mesfer Al Duhayyim, Areej Malibari, Abdullah Alharbi, Kallekh Afef, Ayman Yafoz, Raed A. I. Alsini, Omar Alghushairy, Heba Mohsen · 2022 · Remote Sensing
31. BdSLW-11: Dataset of Bangladeshi sign language words for recognizing 11 daily useful BdSL words
    Md. Monirul Islam, Md. Rasel Uddin, Jannatul Ferdous, Sharmin Akter, Md. Nasim Akhtar · 2022 · Data in Brief
32. Structural damage detection based on transmissibility functions with unsupervised domain adaptation
    Xutong Zhang, Xinqun Zhu, Yingqi Wang, Jianchun Li · 2024 · Engineering Structures
33. Inspection-Nerf: Rendering Multi-Type Local Images for Dam Surface Inspection Task Using Climbing Robot and Neural Radiance Field
    Kunlong Hong, Hongguang Wang, Bingbing Yuan · 2023 · Buildings
34. Multiple Defects Inspection of Dam Spillway Surface Using Deep Learning and 3D Reconstruction Techniques
    Kunlong Hong, Hongguang Wang, Bingbing Yuan, Tianfu Wang · 2023 · Buildings
35. A review on fatigue monitoring of structures
    N. García-Fernández, Manuel López Aenlle, Adrián Álvarez‐Vázquez, Miguel Muñiz‐Calvente, Fernandez Pelayo · 2023 · International Journal of Structural Integrity
36. Data–Model Fusion Methods and Applications Toward Smart Manufacturing and Digital Engineering
    Fei Tao, Yilin Li, Yupeng Wei, Chenyuan Zhang, Ying Nan Zuo · 2025 · Engineering
37. Uncertainty-quantified unsupervised transfer learning for ultrasonic-based corrosion detection in underwater steel pipes
    Hou-Yu Lu, Muhammad Haris, S. Cantero-Chinchilla, Chen Fang, Dimitri Soccol, Konstantinos Gryllias, D. Chronopoulos · 2025 · Automation in Construction
38. Signal Anomaly Detection of Bridge SHM System Based on Two-Stage Deep Convolutional Neural Networks
    Sheng Li, Liang Jin, Yang Qiu, Mimi Zhang, Jie Wang · 2021 · Structural Engineering International
39. Diagnosis of internal cracks in concrete using vibro-acoustic modulation and machine learning
    Sarah Miele, Pranav Karve, Sankaran Mahadevan, Vivek Agarwal · 2022 · Structural Health Monitoring
40. Mode shape prediction based on convolutional neural network and autoencoder
    Kejian Hu, Xiaoguang Wu · 2022 · Structures
41. Testing machine learning models for heuristic building damage assessment applied to the Italian Database of Observed Damage (DaDO)
    Subash Ghimire, Philippe Guéguen, Adrien Pothon, Danijel Schorlemmer · 2023 · Natural hazards and earth system sciences
42. An intelligent crack damage assessment method by integrating information and physics
    Xiangyun Long, Xinfeng Ding, Chao Jiang, Xiaoge Zhang, Wangwang Liao, Kai Liu · 2023 · Engineering Fracture Mechanics
43. Multilayer Cooperative Particle Swarm Optimizer for Feature Selection in Structural Health Monitoring
    Gang Chen, Tianyi Shang, Wenrui Song, Weihan Shao, Hu Sun, Xinlin Qing · 2025 · IEEE Sensors Journal
44. Detection and Identification of Expansion Joint Gap of Road Bridges by Machine Learning Using Line-Scan Camera Images
    In Hwan Kim, Jun Cho, Goang Zi, Beom Cho, Seon Lee, Hyoung Kim · 2021 · Applied System Innovation
45. Challenges in applying vibration-based damage detection to highway bridge structures
    Berhanu Tefera, Adil Zekaria, Abrham Gebre · 2023 · Asian Journal of Civil Engineering
46. An IoT Toolchain Architecture for Planning, Running and Managing a Complete Condition Monitoring Scenario
    Federico Montori, Ivan Zyrianoff, Lorenzo Gigli, Alessandro Calvio, Riccardo Venanzi, Simone Sindaco, Luca Sciullo, Federica Zonzini, Matteo Zauli, Nicola Testoni et al. · 2023 · IEEE Access
47. Robust and efficient dual-graph neural networks for structural damage detection and localization
    Rashinda Wijethunga, Jagath K. Samarabandu, Ayan Sadhu · 2025 · Engineering Structures
48. Time-Inferred Autoencoder: A noise adaptive condition monitoring tool
    Nitin Nagesh Kulkarni, Nicholas A. Valente, Alessandro Fabrizio Sabato · 2023 · Mechanical Systems and Signal Processing
49. Automated Prediction of Crack Propagation Using H2O AutoML
    Intisar Omar, Muhammad Khan, Andrew G. Starr, Khaled Abou Rok Ba · 2023 · Sensors
50. Machine Learning-Driven Predictive Modeling for FRP Strengthened Structural Elements: A Review of AI-Based Damage Detection, Fatigue Prediction, and Structural Health Monitoring
    James Avevor, Michael Adeniyi, Lawrewnce Anebi Enyejo, Selasi Agbale Aikins · 2024 · International Journal of Scientific Research and Modern Technology.
51. Effective Jet-Grouting Application for Improving the State of Deformation of Landmarks
    Alessandro Gaetano Severino, Alexandre de Macêdo Wahrhaftig, Oleksii Tiutkin, Valentyna Gubashova, Larysa Neduzha · 2022 · Buildings
52. Intelligent Multitasking Framework for Boundary-Preserving Semantic Segmentation, Width Estimation, and Propagation Modeling of Concrete Cracks
    Qasim Zaheer, Shi Qiu, Syed Muhammad Ahmed Hassan Shah, Chengbo Ai, Jin Wang · 2025 · Journal of Infrastructure Systems
53. Vibration-based damage identification in concrete beams via a decomposed deep learning approach
    Umberto De Maio, Alfredo Garro, Fabrizio Greco, Andrea Pranno, Alessandra Silvestri · 2026 · Progress in Engineering Science
54. Review of anomaly detection in large span bridges: available methods, recent advancements and future trends
    Ziyuan Fan, Xiaoli Tang, Yang Chen, Yuan Ren, Chao Deng, Zihang Wang, Ying Peng, Chenghong Shi, Qiao Huang · 2024 · Advances in Bridge Engineering
55. Deep learning-based siltation image recognition of water conveyance tunnels using underwater robot
    Xinbin Wu, Junjie Li · 2024 · Journal of Civil Structural Health Monitoring
56. Structural Health Monitoring of Laminated Composites Using Lightweight Transfer Learning
    Muhammad Muzammil Azad, Izaz Raouf, Muhammad Sohail, Heung Soo Kim · 2024 · Machines
57. Detection and assessment of post-earthquake functional building ceiling damage based on improved YOLOv8
    Duozhi Wang, Rong Zhang, Rong Zhang, Guibo Nie, W. P. Wang · 2024 · Journal of Building Engineering
58. Statistical and Machine Learning-Based FHB Detection in Durum Wheat
    Nasrin Azimi, Omid Sofalian, Mahdi Davari, Ali Asghari, Nasser Zare · 2020 · Plant Breeding and Biotechnology
59. A Damage Detection Method Using Neural Network Optimized by Multiple Particle Collision Algorithm
    Sergio V. Farias, Osamu Saotome, Haroldo Fraga de Campos Velho, Elcio Hideiti Shiguemori · 2021 · Journal of Sensors
60. Fatigue damage detection of aerospace-grade aluminum alloys using feature-based and feature-less deep neural networks
    Susheel Dharmadhikari, Amrita Basak · 2021 · Machine Learning with Applications
61. Damage localization for bridges monitored within one cluster based on a spatiotemporal correlation model of strain monitoring data
    Jianxin Cao, Yang Liu · 2022 · Structural Health Monitoring
62. Grey Fuzzy Comprehensive Evaluation of Bridge Risk during Periods of Operation Based on a Combination Weighting Method
    Xiantao Qin, Xianlai Zha, Zhongheng Wu, Lei Zhang · 2023 · Applied Sciences
63. Application of Graph Convolutional Neural Networks Combined with Single-Model Decision-Making Fusion Neural Networks in Structural Damage Recognition
    Xiaofei Li, Langxing Xu, Hainan Guo, Lu Yang · 2023 · Sensors
64. ADVANCEMENTS IN STRUCTURAL HEALTH MONITORING: A REVIEW OF MACHINE LEARNING APPROACHES FOR DAMAGE DETECTION AND ASSESSMENT
    Muhammad Numan · 2024 · International Journal for Computational Civil and Structural Engineering
65. Modelling and Validation of a Guided Acoustic Wave Temperature Monitoring System
    Lawrence Yule, Bahareh Zaghari, Nicholas R. Harris, Martyn Hill · 2021 · Sensors
66. An unsupervised data-driven approach for wind turbine blade damage detection under passive acoustics-based excitation
    Jaclyn Solimine, Murat İnalpolat · 2022 · Wind Engineering
67. Detection of Nut–Bolt Loss in Steel Bridges Using Deep Learning Techniques
    Zhijun Li, Kabiru Adamu, Kai Yan, Xiu-Li Xu, Peng Shao, Xue-Hong Li, Hafsat Muhammad Bashir · 2022 · Sustainability
68. A Pre-Trained Deep Learning Model for Fast Online Prediction of Structural Seismic Responses
    Wei-Jian Tang, Dongsheng Wang, Haibin Huang, Jian-Cheng Dai, Fan Shi · 2023 · International Journal of Structural Stability and Dynamics
69. Validity Evaluation Method Based on Data Driving for On-Line Monitoring Data of Transformer under DC-Bias
    Yuanda He, Qi Zhou, Sheng Lin, Liping Zhao · 2020 · Sensors
70. WSN-Based Multi-Sensor System for Structural Health Monitoring
    Fatih Dagsever, Zahra Sharif Khodaei, M.H. Aliabadi · 2025 · Sensors
71. Digital disaster prevention for ocean engineering: Current progress and future directions
    Zhen-Zhong Hu, Yilin Li, Fu-Ping Gao, Jian-Min Zhang · 2026 · Ocean Engineering
72. A novel spatiotemporal 3D CNN framework with multi-task learning for efficient structural damage detection
    Sadeq Kord, Touraj Taghikhany, Mohammad Akbari · 2023 · Structural Health Monitoring
73. Automatic non-destructive UAV-based structural health monitoring of steel container cranes
    Vanessa De Arriba López, Mehdi Maboudi, Pedro Achanccaray, M. Gerke · 2023 · Applied Geomatics
74. Structural Damage Detection Based on Static and Dynamic Flexibility: A Review and Comparative Study
    Xi Peng, Qiuwei Yang, Fengjiang Qin, Sun Bin-xiang · 2023 · Coatings
75. Enhancing Vibration-based Damage Assessment with 1D-CNN: Parametric Studies and Field Applications
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76. Post-event evaluation of residual capacity of building structures based on seismic monitoring
    Luji Wang, Jiazeng Shan · 2024 · Journal of Civil Structural Health Monitoring
77. Reinforcement learning for multi-objective AutoML in vision-based structural health monitoring
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78. A Review of Health Monitoring and Model Updating of Vibration Dissipation Systems in Structures
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79. Military Training Aircraft Structural Health Monitoring Leveraging an Innovative Biologically Inspired Feedback Mechanism for Neural Networks
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80. Real-Time Damage Detection and Localization on Aerospace Structures Using Graph Neural Networks
    Emiliano Del Priore, Luca Lampani · 2025 · Journal of Sensor and Actuator Networks
81. Efficient Identification of water conveyance tunnels siltation based on ensemble deep learning
    Xinbin Wu, Junjie Li, Linlin Wang · 2022 · Frontiers of Structural and Civil Engineering
82. Robust and efficient feature-based method for structural health monitoring of large structures
    Sneha Prasad, Chih-Hung Chiang, David Kumar, Sumit Kalra, Arpit Khandelwal · 2023 · Journal of Civil Structural Health Monitoring
83. Pruning In Time (PIT): A Lightweight Network Architecture Optimizer for Temporal Convolutional Networks
    Matteo Risso, Alessio Burrello, Daniele Jahier Pagliari, Francesco Conti, Lorenzo Lamberti, Enrico Macii, L. Benini, Massimo Poncino · 2021 · ACM/IEEE Design Automation Conference (DAC)
84. Footbridge structural health monitoring – A review of current research and future directions
    Huiyue Qiao, Hong Guan, Yong Zhu · 2025 · Structure and Infrastructure Engineering
85. A Review of Vibration-Based Scour Diagnosis Methods for Bridge Foundation
    Zhenhao Zhang, Guowei Lin, Xiaopeng Yang, Shilin Cui, Yan Li, Xueqing Shi, Zhongyu Han · 2023 · Sustainability
86. Bolt-loosening detection in offshore wind turbines’ jacket-type supports
    Rhandall Valdez-Yepez, Edwin Palacios, Christian Tutivén, Yolanda Vidal · 2024 · Structural Health Monitoring
87. A model-based deep learning framework for damage classification and detection in polycarbonate infused with AEROSIL under dynamic loading conditions
    Y. Qarssis, Ayoub Karine, Sirine Sayed, Mohamed Daly, Mourad Nachtane, Mostapha Tarfaoui · 2024 · Composites Part B Engineering
88. Cross-domain damage detection through partial conditional adversarial domain adaptation
    Zuoqiang Li, Shun Weng, Hongping Zhu, Aoqi Lei · 2024 · Mechanical Systems and Signal Processing
89. Spectral Jump Anomaly Detection: Temperature-compensated algorithm for structural damage detection using vibration data
    Giulio Mariniello, Tommaso Pastore, Domenico Asprone · 2025 · Automation in Construction
90. Image-based strain response estimation of in-situ bridge using Recurrent Neural Networks (RNNs)
    Mubarak Faisal Abu Zouriq, Daniel G. Linzell, Saeed Eftekhar Azam · 2025 · Structures
91. Structural health monitoring in a population of similar structures with self-supervised learning: a two-stage approach for enhanced damage detection and model tuning
    Yacine Bel-Hadj, Wout Weijtjens, Christof Devriendt · 2025 · Structural Health Monitoring
92. A generative adversarial network optimization method for damage detection and digital twinning by deep AI fault learning: Z24 Bridge structural health monitoring benchmark validation
    Marios Impraimakis, Evangelia Nektaria Palkanoglou · 2025 · Structural and Multidisciplinary Optimization
93. Damage identification for bridges using machine learning: Development and application to KW51 bridge
    Yuqing Qiu, Bilal Ahmed, Diab Abueidda, Waleed El-Sekelly, Borja García de Soto, Tarek H. Abdoun, Hongli Ji, Jinhao Qiu, Mostafa E. Mobasher · 2026 · Digital engineering.
94. Adversarial Fuzzy-Weighted Deep Transfer Learning for Intelligent Damage Diagnosis of Bridge With Multiple New Damages
    Haitao Xiao, Wenjie Wang, Harutoshi Ogai, Mingjun Wang, Rui Shen · 2022 · IEEE Sensors Journal
95. Deep autoencoder model for direct monitoring of bridges subjected to a moving vehicle load under varying temperature conditions
    Fadel Yessoufou, Jinsong Zhu · 2023 · Structures
96. Vibration-based hidden damage imaging using stereo cameras with digital image correlation
    Shao-Han Wang, Trenton B. Abbott, Rey-Yie Abel Fong, Chengying Xu, Fuh‐Gwo Yuan · 2023 · Structural Health Monitoring
97. Deep learning based structural damage identification for the strain field of a subway bolster
    Chengxing Yang, Liting Yang, Weinian Guo, Ping Xu · 2023 · Alexandria Engineering Journal
98. MLPER: A Machine Learning-Based Prediction Model for Building Earthquake Response Using Ambient Vibration Measurements
    Spyros Damikoukas, Nikos D. Lagaros · 2023 · Applied Sciences
99. An iterative regression-based thermal response prediction methodology for instrumented civil infrastructure
    Theo Glashier, Rolands Kromanis, Craig Buchanan · 2024 · Advanced Engineering Informatics
100. Improving the Ability of a Laser Ultrasonic Wave-Based Detection of Damage on the Curved Surface of a Pipe Using a Deep Learning Technique
     Byoungjoon Yu, Kassahun Demissie Tola, Changgil Lee, Seunghee Park · 2021 · Sensors