Cited by 798Structural Health Monitoring TechniquesInfrastructure Maintenance and MonitoringStructural Engineering and Materials Analysis

The DOI matches its Crossref record; 234 of the 234 references it cites were found in a registry or an index.

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

Open access

Azimi, Mohsen, Eslamlou, Armin, Pekcan, Gokhan·2020·Sensors

DOI 10.3390/s20102778PMC PMC7294417

Open this paper in CoChat — free account

Abstract

Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.3390/s20102778

Cites

234 of 234 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Deep learning
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
A Threshold Selection Method from Gray-Level Histograms
Otsu, N. (1979) · IEEE Transactions on Systems, Man and Cybernetics · doi:10.1109/tsmc.1979.4310076 · Cited by 44,708
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834

Flagged first, then by citations

Show all 232 references

Research works printed but not linked: 9

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8–13). Generative adversarial nets. Proceedings of the Advances in neural information processing systems, Montréal, QC, Canada.
Zink, 2015, Unmanned Aer, Veh. Bridge Insp. Demonstr. Proj., 32, 76
Wells, 2017, Unmanned Aircr, Syst. Bridge Insp. Demonstr. Proj. Phase II Final. Rep., 13, 45
Gopalakrishnan, K. (2018). Symposium on Automation and Robotics. Deep Learn. Pavement Image Anal. Autom. Distress Detect. Rev., 9.
Nahata, 2019, Post-Earthquake. Assessment. Buildlings Using Deep Learning, J. Comput. Civ. Eng., 8, 330
Gao, Y., Li, K., Mosalam, K., and Günay, S. (2018, January 16–25). Deep Residual Network with Transfer Learning for Imagebased Structural Damage Recognition. Proceedings of the Eleventh US National Conference on Earthquake Engineering, Integrating Science, Engineering Policy, San Francisco, CA, USA.
Sharma, M., Anotaipaiboon, W., and Chaiyasarn, K. (2018, January 20–25). Crack Detection in Masonry Structures using Convolutional Neural Networks and Support Vector Machines. Proceedings of the 35th International Symposium on Automation and Robotics in Construction (ISARC 2018), Berlin, Germany.
Aono, K., Kondapalli, S.H., Lajnef, N., Pekcan, G., Faridazar, F., and Chakrabartty, S. (2017, January 22–23). Self-powered Sensors to Facilitate Infrastructural Internet-of-Things for Smart Structures. Proceedings of the 13th International Workshop on Advanced Smart Materials and Smart Structures Technology, Tokyo, Japan.
Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., and Zaremba, W. (2020, May 09). Openai Gym. Available online: https://arxiv.org/abs/1606.01540. · arXiv

Sorted by citations

Items 101–200 of 798

Performance-based post-earthquake decision making for instrumented buildings
Roohi, M., & Hernandez, E. M. (2020) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-020-00416-1 · Cited by 45
Damage detection for prefabricated building modules during transportation
Valinejadshoubi, M., Bagchi, A., & Moselhi, O. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104466 · Cited by 46
Explainable deep learning based ultrasonic guided wave pipe crack identification method
Tang, R., Zhang, S., Wu, W., Han, Z., & Han, Z. (2022) · Measurement · doi:10.1016/j.measurement.2022.112277 · Cited by 44
Physics informed and data-based augmented learning in structural health diagnosis
Lorenzo, D. D., Champaney, V., Marzin, J. Y., Farhat, C., & Chinesta, F. (2023) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2023.116186 · Cited by 44
Smartphone Prospects in Bridge Structural Health Monitoring, a Literature Review
Özer, E., & Kromanis, R. (2024) · Sensors · doi:10.3390/s24113287 · Cited by 45
Internet of things (IoT)-based structural health monitoring of laboratory-scale civil engineering structures
Saravanan, T. J., Mishra, M., Aherwar, A. D., & Lourenço, P. B. (2024) · Innovative Infrastructure Solutions · doi:10.1007/s41062-024-01413-9 · Cited by 42
Interval deep neural networks with damage function for credible structural identification
Cao, G., & Wang, X. (2025) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2025.118214 · Cited by 42
Instance Segmentation with Mask R-CNN Applied to Loose-Housed Dairy Cows in a Multi-Camera Setting
Salau, J., & Krieter, J. (2020) · Animals · doi:10.3390/ani10122402 · Cited by 43
Infrastructure BIM Platform for Lifecycle Management
Jang, K., Kim, J.-W., Ju, K.-B., & An, Y. (2021) · Applied Sciences · doi:10.3390/app112110310 · Cited by 42
A Comparative Study of Damage-Sensitive Features for Rapid Data-Driven Seismic Structural Health Monitoring
Reuland, Y., Martakis, P., & Chatzi, E. (2023) · Applied Sciences · doi:10.3390/app13042708 · Cited by 41
Bridge damage identification under varying environmental and operational conditions combining Deep Learning and numerical simulations
Fernández-Navamuel, A., Pardo, D., Magalhães, F., Zamora-Sánchez, D., Omella, Á. J., & García-Sánchez, D. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110471 · Cited by 41
A lightweight detection method of pavement potholes based on binocular stereo vision and deep learning
Xing, C., Zheng, G., Zhang, Y., Deng, H., Li, M., Zhang, L., & Tan, Y. (2024) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2024.136733 · Cited by 41
A review on early-age cracking of concrete: Causes and control
Qu, Z., Zhang, Y., Liu, Z., Si, R., & Wu, J. (2024) · Case Studies in Construction Materials · doi:10.1016/j.cscm.2024.e03848 · Cited by 42
Application of DIC Method in the Analysis of Stress Concentration and Plastic Zone Development Problems
Romanowicz, P. J., Szybiński, B., & Wygoda, M. (2020) · Materials · doi:10.3390/ma13163460 · Cited by 41
Machine learning for yield prediction in Fergana valley, Central Asia
Boori, M. S., Choudhary, K., Александрович, П. Р., & Kupriyanov, A. V. (2022) · Journal of the Saudi Society of Agricultural Sciences · doi:10.1016/j.jssas.2022.07.006 · Cited by 43
Automated bughole detection and quality performance assessment of concrete using image processing and deep convolutional neural networks
Wei, W., Ding, L., Luo, H., Li, C., & Li, G. (2021) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2021.122576 · Cited by 41
Mobile crowdsensing framework for drive-by-based dense spatial-resolution bridge mode shape identification
Peng, Z., Li, J., Hao, H., & Yang, N. (2023) · Engineering Structures · doi:10.1016/j.engstruct.2023.116515 · Cited by 42
Diagnosis of the health status of mooring systems for floating offshore wind turbines using autoencoders
Gorostidi, N., Pardo, D., & Nava, V. (2023) · Ocean Engineering · doi:10.1016/j.oceaneng.2023.115862 · Cited by 40
Advances in computer vision-based structural health monitoring techniques for wind turbine blades
Sheiati, S., & Chen, X. D. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2025.116078 · Cited by 43
Fiber Optic Sensing Technology and Vision Sensing Technology for Structural Health Monitoring
Wang, H., Guo, J., Han, M., Zhou, X., & Han, Y. (2023) · Sensors · doi:10.3390/s23094334 · Cited by 39
Convolutional neural network based structural health monitoring for rocking bridge system by encoding time‐series into images
Mantawy, I. M., & Mantawy, M. O. (2021) · Structural Control and Health Monitoring · doi:10.1002/stc.2897 · Cited by 39
Semi-autonomous inspection for concrete structures using digital models and a hybrid approach based on deep learning and photogrammetry
Mirzazade, A., Popescu, C., Gonzalez-Libreros, J. H., Blanksvärd, T., Täljsten, B., & Sas, G. (2023) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-023-00680-x · Cited by 38
Unsupervised deep learning method for bridge condition assessment based on intra-and inter-class probabilistic correlations of quasi-static responses
Xu, Y., Tian, Y., & Li, H. (2022) · Structural Health Monitoring · doi:10.1177/14759217221103016 · Cited by 37
A Novel Structural Damage Detection Method via Multisensor Spatial–Temporal Graph-Based Features and Deep Graph Convolutional Network
Zhan, P., Qin, X., Zhang, Q., & Sun, Y. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3238048 · Cited by 37
Surface Damage Detection and Localization for Bridge Visual Inspection Based on Deep Learning and 3D Reconstruction
Ni, Y., Mao, J., Wang, H., Xi, Z., & Chen, Z. (2024) · Structural Control and Health Monitoring · doi:10.1155/2024/9988793 · Cited by 37
Deep learning based segmentation using full wavefield processing for delamination identification: A comparative study
Ijjeh, A. A., & Kudela, P. (2021) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2021.108671 · Cited by 37
An Unsupervised Tunnel Damage Identification Method Based on Convolutional Variational Auto-Encoder and Wavelet Packet Analysis
Zhang, Y., Xie, X., Li, H., & Zhou, B. (2022) · Sensors · doi:10.3390/s22062412 · Cited by 37
Damage identification using wave damage interaction coefficients predicted by deep neural networks
Humer, C., Höll, S., Kralovec, C., & Schagerl, M. (2022) · Ultrasonics · doi:10.1016/j.ultras.2022.106743 · Cited by 37
Feature extraction and classification of multiple cracks from raw vibrational responses of composite beams using 1D-CNN network
Shirazi, M. I., Khatir, S., Boutchicha, D., & Wahab, M. M. A. (2023) · Composite Structures · doi:10.1016/j.compstruct.2023.117701 · Cited by 36
Uncertainty Handling in Structural Damage Detection via Non-Probabilistic Meta-Models and Interval Mathematics, a Data-Analytics Approach
Ghiasi, R., Noori, M. N., Altabey, W. A., Silik, A., Wang, T., & Wu, Z. (2021) · Applied Sciences · doi:10.3390/app11020770 · Cited by 36
A deep learning-based fatigue crack growth rate measurement method using mobile phones
Long, X., Yu, M., Liao, W., & Jiang, C. (2022) · International Journal of Fatigue · doi:10.1016/j.ijfatigue.2022.107327 · Cited by 36
A machine learning framework for seismic risk assessment of industrial equipment
Quinci, G., Paolacci, F., Fragiadakis, M., & Bursi, O. S. (2024) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2024.110606 · Cited by 35
Enhancing asset management: Integrating digital twins for continuous permitting and compliance - A systematic literature review
Alhadi, A., Beach, T., & Yacine, R. (2024) · Journal of Building Engineering · doi:10.1016/j.jobe.2024.111515 · Cited by 35
Performance Comparison of Multiple Convolutional Neural Networks for Concrete Defects Classification
Arafin, P., Issa, A., & Billah, A. H. M. M. (2022) · Sensors · doi:10.3390/s22228714 · Cited by 34
Composite Panel Damage Classification Based on Guided Waves and Machine Learning: An Experimental Approach
Perfetto, D., Rezazadeh, N., Aversano, A., Luca, A. D., & Lamanna, G. (2023) · Applied Sciences · doi:10.3390/app131810017 · Cited by 34
Civil Structural Health Monitoring and Machine Learning: A Comprehensive Review
Anjum, A., Hrairi, M., Aabid, A., Yatim, N. H. M., & Ali, M. (2024) · Frattura ed Integrità Strutturale · doi:10.3221/igf-esis.69.04 · Cited by 34
Optical Fiber-Based Structural Health Monitoring: Advancements, Applications, and Integration with Artificial Intelligence for Civil and Urban Infrastructure
Владимирович, Г. Н., Kazanskiy, N. L., & Khonina, S. N. (2025) · Photonics · doi:10.3390/photonics12060615 · Cited by 33
Development and Utilization of Bridge Data of the United States for Predicting Deck Condition Rating Using Random Forest, XGBoost, and Artificial Neural Network
Fard, F., & Fard, F. S. N. (2024) · Remote Sensing · doi:10.3390/rs16020367 · Cited by 32
Hyperspectral imaging for the elimination of visual ambiguity in corrosion detection and identification of corrosion sources
Lavadiya, D. N., Sajid, H. U., Yellavajjala, R. K., & Sun, X. (2021) · Structural Health Monitoring · doi:10.1177/14759217211041690 · Cited by 31
Transformer‐based framework for accurate segmentation of high‐resolution images in structural health monitoring
Azimi, M., & Yang, T. Y. (2024) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.13211 · Cited by 32
Self-citation
Enhancing concrete defect segmentation using multimodal data and Siamese Neural Networks
Pozzer, S., Ramos, G., Azar, E. R., Osman, A., Refai, A. E., López, F., Ibarra‐Castanedo, C., & Maldague, X. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105594 · Cited by 31
Deep learning for automated detection and classification of crack severity level in concrete structures
Shi, T., & Luo, H. (2025) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2025.140793 · Cited by 31
Structural Damage Classification in a Jacket-Type Wind-Turbine Foundation Using Principal Component Analysis and Extreme Gradient Boosting
Leon‐Medina, J. X., Anaya, M., Mariné, N. P., Burgos, D. A. T., & Pozo, F. (2021) · Sensors · doi:10.3390/s21082748 · Cited by 31
Towards Interpretable Machine Learning for Automated Damage Detection Based on Ultrasonic Guided Waves
Schnur, C., Goodarzi, P., Lugovtsova, Y., Bulling, J., Prager, J., Tschöke, K., Moll, J., Schütze, A., & Schneider, T. (2022) · Sensors · doi:10.3390/s22010406 · Cited by 31
Computational intelligence methods in simulation and modeling of structures: A state-of-the-art review using bibliometric maps
Solorzano, G., & Plevris, V. (2022) · Frontiers in Built Environment · doi:10.3389/fbuil.2022.1049616 · Cited by 31
Damage Detection in Largely Unobserved Structures under Varying Environmental Conditions: An AutoRegressive Spectrum and Multi-Level Machine Learning Methodology
Entezami, A., Mariani, S., & Shariatmadar, H. (2022) · Sensors · doi:10.3390/s22041400 · Cited by 30
Physics-informed deep learning for structural vibration identification and its application on a benchmark structure
Zhang, M., Guo, T., Zhang, G., Liu, Z., & Xu, W. (2023) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2022.0400 · Cited by 30
Computer vision in drone imagery for infrastructure management
Ejaz, N., & Choudhury, S. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105418 · Cited by 30
Health Monitoring of Large-Scale Civil Structures: An Approach Based on Data Partitioning and Classical Multidimensional Scaling
Entezami, A., Sarmadi, H., Behkamal, B., & Mariani, S. (2021) · Sensors · doi:10.3390/s21051646 · Cited by 30
A transfer learning SHM strategy for bridges enriched by the use of speaker recognition x-vectors
Tronci, E. M., Beigi, H., Feng, M. Q., & Betti, R. (2022) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-022-00591-3 · Cited by 30
Improving Soil Stability with Alum Sludge: An AI-Enabled Approach for Accurate Prediction of California Bearing Ratio
Baghbani, A., Nguyen, M. D., Alnedawi, A., Milne, N., Baumgartl, T., & Abuel-Naga, H. (2023) · Applied Sciences · doi:10.3390/app13084934 · Cited by 30
Damage Localization and Severity Assessment of a Cable-Stayed Bridge Using a Message Passing Neural Network
Son, H., Pham, V.-T., Jang, Y., & Kim, S.-E. (2021) · Sensors · doi:10.3390/s21093118 · Cited by 29
Fatigue Crack Evaluation with the Guided Wave–Convolutional Neural Network Ensemble and Differential Wavelet Spectrogram
Chen, J., Wu, W., Ren, Y., & Yuan, S. (2021) · Sensors · doi:10.3390/s22010307 · Cited by 29
Automatic bolt tightness detection using acoustic emission and deep learning
Fu, W., Zhou, R., & Guo, Z. (2023) · Structures · doi:10.1016/j.istruc.2023.06.100 · Cited by 29
Computer Vision and Image Processing in Structural Health Monitoring: Overview of Recent Applications
Ferraris, C., Amprimo, G., & Pettiti, G. (2023) · Signals · doi:10.3390/signals4030029 · Cited by 29
Diagnostic-prognostic framework for assessing the health status of composite structures
Zhang, Y., Radzieński, M., & Ostachowicz, W. M. (2024) · International Journal of Mechanical Sciences · doi:10.1016/j.ijmecsci.2024.109461 · Cited by 29
Machine learning-driven structural health monitoring: STFT-based feature extraction for damage detection
Katam, R., Pasupuleti, V. D. K., & Kalapatapu, P. (2025) · Structures · doi:10.1016/j.istruc.2025.109244 · Cited by 29
Dispersion compensation of Lamb waves based on a convolutional auto-encoder
Zhang, H., Hua, J., Tong, T., Zhang, T., & Lin, J. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110432 · Cited by 28
Explainable Artificial Intelligence to Advance Structural Health Monitoring
Luckey, D., Fritz, H., Legatiuk, D., Abadía, J. J. P., Walther, C., & Smarsly, K. (2021) · Structural integrity · doi:10.1007/978-3-030-81716-9_16 · Cited by 28
Efficient semi-supervised surface crack segmentation with small datasets based on consistency regularisation and pseudo-labelling
Shamsabadi, E. A., Erfani, S. M. H., Xu, C., & Dias‐da‐Costa, D. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.105181 · Cited by 30
AI-Driven Non-Destructive Testing Insights
Safhi, A. E. M., Keserle, G. C., & Blanchard, S. C. (2024) · Encyclopedia · doi:10.3390/encyclopedia4040116 · Cited by 28
Detecting surface defects of heritage buildings based on deep learning
Fu, X., & Angkawisittpan, N. (2024) · Journal of Intelligent Systems · doi:10.1515/jisys-2023-0048 · Cited by 27
A novel information entropy approach for crack monitoring leveraging nondestructive evaluation sensing
Malik, S., & Kontsos, A. (2024) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2024.111207 · Cited by 27
An efficient edge/cloud medical system for rapid detection of level of consciousness in emergency medicine based on explainable machine learning models
El-Rashidy, N. M., Sedik, A., Siam, A. I., & Ali, Z. H. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08258-w · Cited by 27
Damage identification for jacket offshore platforms using Transformer neural networks and random decrement technique
Bao, X., Liu, M., Fu, D., Shi, C., Cui, H., Sun, Z., Liu, Z., & Iglesias, G. (2023) · Ocean Engineering · doi:10.1016/j.oceaneng.2023.115973 · Cited by 26
Bridging Convolutional Neural Networks and Transformers for Efficient Crack Detection in Concrete Building Structures
Yadav, D. P., Sharma, B., Chauhan, S., & Dhaou, I. B. (2024) · Sensors · doi:10.3390/s24134257 · Cited by 26
Deep learning for seismic structural monitoring by accounting for mechanics-based model uncertainty
Cheraghzade, M., & Roohi, M. (2022) · Journal of Building Engineering · doi:10.1016/j.jobe.2022.104837 · Cited by 26
Image Classification-Based Defect Detection of Railway Tracks Using Fiber Bragg Grating Ultrasonic Sensors
Dang, D.-Z., Lai, C.-C., Ni, Y., Zhao, Q. L., Su, B., & Zhou, Q. (2022) · Applied Sciences · doi:10.3390/app13010384 · Cited by 26
A novel multistage damage detection method for trusses using time-history data based on model order reduction and deep neural network
Lieu, Q. X. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110635 · Cited by 26
Deep predictions and transfer learning for simulation-driven structural health monitoring based on guided waves
Höll, S., & Humer, C. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122133 · Cited by 26
Advancing bridge resilience: a review of monitoring technologies for flood-prone infrastructure
Buka-Vaivade, K., Nicoletti, V., & Gara, F. (2025) · Open Research Europe · doi:10.12688/openreseurope.19232.1 · Cited by 25
A fast and efficient feature extraction methodology for structural damage localization based on raw acceleration measurements
Alves, V., & Cury, A. (2021) · Structural Control and Health Monitoring · doi:10.1002/stc.2748 · Cited by 25
Fatigue crack detection in welded structural components of steel bridges using artificial neural network
Mashayekhi, M., Santini‐Bell, E., & Azam, S. E. (2021) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-021-00488-7 · Cited by 25
A machine learning-based data augmentation strategy for structural damage classification in civil infrastructure system
Li, L., & Betti, R. (2023) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-023-00705-5 · Cited by 25
A Critical Review of IoT-Based Structural Health Monitoring for Dams
Khan, A., Mishra, S., Pandey, S., Sardar, T., Mishra, A., Mudgal, M., & Singhai, S. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3488290 · Cited by 24
Distribution Sub-Domain Adaptation Deep Transfer Learning Method for Bridge Structure Damage Diagnosis Using Unlabeled Data
Xiao, H., Dong, L., Wang, W., & Ogai, H. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3186885 · Cited by 24
Damage localization in composite structures based on Lamb wave and modular artificial neural network
Gao, Y., Sun, L., Song, R., Peng, C. Q., Wu, X., Wei, J., Jiang, M., Sui, Q., & Zhang, L. (2024) · Sensors and Actuators A Physical · doi:10.1016/j.sna.2024.115644 · Cited by 23
Comprehensive review of AI and ML tools for earthquake damage assessment and retrofitting strategies
Bhadauria, P. K. S. (2024) · Earth Science Informatics · doi:10.1007/s12145-024-01431-2 · Cited by 23
A deep feed-forward neural network for damage detection in functionally graded carbon nanotube-reinforced composite plates using modal kinetic energy
Le, H. Q., Truong, T. T., Dinh-Cong, D., & Nguyen-Thoi, T. (2021) · Frontiers of Structural and Civil Engineering · doi:10.1007/s11709-021-0767-z · Cited by 23
Genetic Algorithm Supported by Influence Lines and a Neural Network for Bridge Health Monitoring
Marasco, G., Piana, G., Chiaia, B. M., & Ventura, G. (2022) · Journal of Structural Engineering · doi:10.1061/(asce)st.1943-541x.0003345 · Cited by 23
Sensory methods and machine learning based damage identification of fibre-reinforced composite structures: An introductory review
Bandara, S., Herath, M., & Epaarachchi, J. A. (2022) · Journal of Reinforced Plastics and Composites · doi:10.1177/07316844221145972 · Cited by 23
Transfer learning for cross-scene 3D pavement crack detection based on enhanced deep edge features
Gui, R., Sun, Q., Wu, W., Zhang, D., & Li, Q. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106452 · Cited by 23
Structural damage identification based on variational mode decomposition–Hilbert transform and CNN
Diao, Y., Lv, J., Wang, Q., Li, X., & Xu, J. (2023) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-023-00715-3 · Cited by 23
Deep learning-based prediction of structural responses of RC slabs subjected to blast loading
Zhou, X., Huang, B.-G., Wang, X., & Xia, Y. (2024) · Engineering Structures · doi:10.1016/j.engstruct.2024.118184 · Cited by 23
Wave based damage detection in solid structures using spatially asymmetric encoder–decoder network
Wuttke, F., Lyu, H., Sattari, A. S., & Rizvi, Z. H. (2021) · Scientific Reports · doi:10.1038/s41598-021-00326-2 · Cited by 24
Vibration-based structural health monitoring exploiting a combination of convolutional neural networks and autoencoders for temperature effects neutralization
Parziale, M., Lomazzi, L., Giglio, M., & Cadini, F. (2022) · Structural Control and Health Monitoring · doi:10.1002/stc.3076 · Cited by 21
A New Method to Predict Damage to Composite Structures Using Convolutional Neural Networks
Mezeix, L., Rivas, A. S., Relandeau, A., & Bouvet, C. (2023) · Materials · doi:10.3390/ma16227213 · Cited by 22
Assessing uncertainty in image-based monitoring: addressing false positives, false negatives, and base rate bias in structural health evaluation
Plevris, V. (2025) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-024-02898-7 · Cited by 22
Machine learning and cointegration for structural health monitoring of a model under environmental effects
Rodrigues, M., Miguéis, V. L., Félix, C., & Rodrigues, C. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121739 · Cited by 25
Quantitative detection of typical bridge surface damages based on global attention mechanism and YOLOv7 network
Ni, Y., Wang, H., Mao, J., Xi, Z., & Chen, Z.-Y. (2024) · Structural Health Monitoring · doi:10.1177/14759217241246953 · Cited by 22
Modal Identification Techniques for Concrete Dams: A Comprehensive Review and Application
Mostafaei, H. (2024) · Sci · doi:10.3390/sci6030040 · Cited by 22
Comparative Analysis of Improved YOLO v5 Models for Corrosion Detection in Coastal Environments
Yu, Q., Han, Y., Gao, X., Lin, W., & Han, Y. (2024) · Journal of Marine Science and Engineering · doi:10.3390/jmse12101754 · Cited by 23
One-Class Convolutional Neural Network (OC-CNN) Model for Rapid Bridge Damage Detection Using Bridge Response Data
Yessoufou, F., & Zhu, J. (2023) · KSCE Journal of Civil Engineering · doi:10.1007/s12205-023-0063-7 · Cited by 22
Data augmentation of dynamic responses for structural health monitoring using denoising diffusion probabilistic models
Zheng, W., Li, J., & Hao, H. (2025) · Engineering Structures · doi:10.1016/j.engstruct.2025.119685 · Cited by 22
Deep transfer learning for connection defect identification in prefabricated structures
Tang, H., & Xie, Y. (2022) · Structural Health Monitoring · doi:10.1177/14759217221119537 · Cited by 21
Quantum‐Based Combinatorial Optimization for Optimal Sensor Placement in Civil Structures
Martín, G. S., & Droguett, E. L. (2024) · Structural Control and Health Monitoring · doi:10.1155/2024/6681342 · Cited by 21
Concrete acoustic emission signal augmentation method based on generative adversarial networks
Fu, W., Zhou, R., & Guo, Z. (2024) · Measurement · doi:10.1016/j.measurement.2024.114574 · Cited by 22
Variational Neural Network Embedded with Digital Twins for Probabilistic Structural Damage Quantification
Xu, J., Zhou, X., Giglio, M., Sbarufatti, C., & Dong, L. (2024) · AIAA Journal · doi:10.2514/1.j064808 · Cited by 22
An Unsupervised Learning Approach for Early Damage Detection by Time Series Analysis and Deep Neural Network to Deal with Output-Only (Big) Data
Entezami, A., Sarmadi, H., & Mariani, S. (2020) · International Electronic Conference on Sensors and Applications (ECSA) · doi:10.3390/ecsa-7-08281 · Cited by 21
Standardization construction and development trend of bridge health monitoring systems in China
Zhou, G.-D., Yi, T., Li, W., Zhong, J., & Zhang, G. (2020) · Advances in Bridge Engineering · doi:10.1186/s43251-020-00016-5 · Cited by 21

Authors: Azimi, M., Eslamlou, A., & Pekcan, G.
Affiliations: University of Nevada, Reno (US)
Iran University of Science and Technology (IR)
Department of Civil and Environmental Engineering, University of Nevada, Reno, NV 89557, USA
Corresponding author: Azimi, Mohsen
ORCID: Azimi, Mohsen
Eslamlou, Armin
Pekcan, Gokhan
Venue: Sensors
Publisher: MDPI AG
Published: 2020-05-13
Volume: 20
Issue: 10
Pages: 2778
ISSN: 1424-8220 (ISSN-L)
DOI: 10.3390/s20102778
PMC: PMC7294417
OpenAlex: W3024912007
Cited by: 769
References: 232
Citations by year: 2026: 151 · 2025: 181 · 2024: 133 · 2023: 138 · 2022: 96 · 2021: 56 · 2020: 14
FWCI: 44.44
Type: journal article
Language: en
Open access: gold
dx.doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Sensors (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: National Science Foundation (1646420)
Topics: Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
Physical Sciences › Engineering › Civil and Structural Engineering › Structural Engineering and Materials Analysis
Keywords: structural health monitoring, damage detection, deep learning, vibration-based condition monitoring, vision-based monitoring, transfer learning, deep neural network, unmanned aerial vehicles, structural sensors, civil infrastructure

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

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

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks