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

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 · see citing works

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 · see citing works

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works

Very Deep Convolutional Networks for Large-Scale Image Recognition

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works

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 · see citing works

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 56,035 · see citing works

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 · see citing works

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 43,748 · see citing works

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 · see citing works

Rethinking the Inception Architecture for Computer Vision

Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138 · see citing works

Human-level control through deep reinforcement learning

Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357 · see citing works

Fast R-CNN

Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105 · see citing works

A survey on transfer learning

Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387 · see citing works

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2644615 · Cited by 19,631 · see citing works

GAN（Generative Adversarial Nets）

淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,268 · see citing works

Deep learning in neural networks: an overview

Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570 · see citing works

TensorFlow: A system for large-scale machine learningPreprint

Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · arXiv (Cornell University) · doi:10.5555/3026877.3026899 · Cited by 16,369 · see citing works

Visualizing and understanding convolutional networks

Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839 · see citing works

Caffe: Convolutional Architecture for Fast Feature Embedding

Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077 · see citing works

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11231 · Cited by 4,636 · see citing works

Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review

Rawat, W., & Wang, Z. (2017) · Neural Computation · doi:10.1162/neco_a_00990 · Cited by 3,503 · see citing works

Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks

Cha, Y., Choi, W., & Büyüköztürk, O. (2017) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12263 · Cited by 3,295 · see citing works

Deep learning and its applications to machine health monitoring

Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R. X. (2019) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2018.05.050 · Cited by 2,894 · see citing works

A Summary Review of Vibration-Based Damage Identification Methods

Doebling, S. W., Farrar, C. R., & Prime, M. B. (1998) · The Shock and Vibration Digest · doi:10.1177/058310249803000201 · Cited by 2,829 · see citing works

Detection of structural damage through changes in frequency: a review

Salawu, O. S. (1997) · Engineering Structures · doi:10.1016/s0141-0296(96)00149-6 · Cited by 2,080 · see citing works

Robust Object Recognition with Cortex-Like Mechanisms

Serre, T., Wolf, L., Bileschi, S., Riesenhuber, M., & Poggio, T. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.56 · Cited by 1,667 · see citing works

Road crack detection using deep convolutional neural network

Zhang, L., Yang, F., Zhang, Y. D., & Zhu, Y. J. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7533052 · Cited by 1,602 · see citing works

Autonomous Structural Visual Inspection Using Region‐Based Deep Learning for Detecting Multiple Damage Types

Cha, Y., Choi, W., Suh, G., Mahmoudkhani, S., & Büyüköztürk, O. (2018) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12334 · Cited by 1,558 · see citing works

Vibration Based Condition Monitoring: A Review

Carden, E. P., & Fanning, P. (2004) · Structural Health Monitoring · doi:10.1177/1475921704047500 · Cited by 1,549 · see citing works

A review of structural health monitoring literature 1996-2001

Farrar, C. R., Czarnecki, J. J., Hoon Sohn, A., & Hemez, F. (2002) · Cited by 1,496 · see citing works

A Survey on Deep Learning

Pouyanfar, S., Sadiq, S. B., Yan, Y., Tian, H., Tao, Y., Reyes, M. P., Shyu, M., Chen, S., & Iyengar, S. S. (2018) · ACM Computing Surveys · doi:10.1145/3234150 · Cited by 1,435 · see citing works

Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks

Abdeljaber, O., Avcı, O., Serkan Kiranyaz, B., Gabbouj, M., & Inman, D. J. (2016) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2016.10.043 · Cited by 1,284 · see citing works

Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring

Spencer, B. F., Hoskere, V., & Narazaki, Y. (2019) · Engineering · doi:10.1016/j.eng.2018.11.030 · Cited by 1,235 · see citing works

Autonomous concrete crack detection using deep fully convolutional neural network

Dung, C. V., & Anh, L. D. (2019) · Automation in Construction · doi:10.1016/j.autcon.2018.11.028 · Cited by 1,170 · see citing works

Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images

Maeda, H., Sekimoto, Y., Seto, T., Kashiyama, T., & Omata, H. (2018) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12387 · Cited by 1,072 · see citing works

Emerging Artificial Intelligence Methods in Structural Engineering

Salehi, H., & Burgueño, R. (2018) · Engineering Structures · doi:10.1016/j.engstruct.2018.05.084 · Cited by 1,068 · see citing works

Automated Pixel‐Level Pavement Crack Detection on 3D Asphalt Surfaces Using a Deep‐Learning Network

Zhang, A. A., Wang, K. C. P., Li, B., Yang, E., Dai, X., Peng, Y. L., Fei, Y., Liu, Y., Li, Q., & Chen, C. (2017) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12297 · Cited by 972 · see citing works

Crack detection using image processing: A critical review and analysis

Mohan, A. K., & Poobal, S. (2017) · Alexandria Engineering Journal · doi:10.1016/j.aej.2017.01.020 · Cited by 966 · see citing works

Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection

Gopalakrishnan, K., Khaitan, S. K., Choudhary, A., & Agrawal, A. (2017) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2017.09.110 · Cited by 959 · see citing works

NB-CNN: Deep Learning-Based Crack Detection Using Convolutional Neural Network and Naïve Bayes Data Fusion

Chen, F., & Jahanshahi, M. R. (2017) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2764844 · Cited by 933 · see citing works

Analysis of Edge-Detection Techniques for Crack Identification in Bridges

Abdel-Qader, I. M., Abudayyeh, O. Y., & Kelly, M. E. (2003) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)0887-3801(2003)17:4(255 · Cited by 894 · see citing works

Automatic Pixel‐Level Crack Detection and Measurement Using Fully Convolutional Network

Yang, X., Li, H., Yu, Y., Luo, X., Huang, T., & Xu, Y. (2018) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12412 · Cited by 860 · see citing works

Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete

Dorafshan, S., Thomas, R. J., & Maguire, M. (2018) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2018.08.011 · Cited by 795 · see citing works

Deep Transfer Learning for Image‐Based Structural Damage Recognition

Gao, Y., & Mosalam, K. M. (2018) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12363 · Cited by 771 · see citing works

Rethinking the Inception Architecture for Computer Vision

Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2015) · arXiv · doi:10.48550/arxiv.1512.00567 · Cited by 741 · see citing works

Structural Damage Detection with Automatic Feature‐Extraction through Deep Learning

Lin, Y., Nie, Z., & Ma, H. (2017) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12313 · Cited by 642 · see citing works

Computer vision and deep learning–based data anomaly detection method for structural health monitoring

Bao, Y., Tang, Z., Li, H., & Zhang, Y. (2018) · Structural Health Monitoring · doi:10.1177/1475921718757405 · Cited by 619 · see citing works

Vision‐Based Automated Crack Detection for Bridge Inspection

Yeum, C. M., & Dyke, S. J. (2015) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12141 · Cited by 521 · see citing works

1-D CNNs for structural damage detection: Verification on a structural health monitoring benchmark data

Abdeljaber, O., Avci, O., Kiranyaz, M. S., Boashash, B., Sodano, H., & Inman, D. J. (2018) · Neurocomputing · doi:10.1016/j.neucom.2017.09.069 · Cited by 521 · see citing works

The State of the Art of Data Science and Engineering in Structural Health Monitoring

Bao, Y., Chen, Z., Wei, S., Xu, Y., Tang, Z., & Li, H. (2019) · Engineering · doi:10.1016/j.eng.2018.11.027 · Cited by 484 · see citing works

Automated Crack Detection on Concrete Bridges

Prasanna, P., Dana, K., Gucunski, N., Basily, B., La, H. M., Lim, R. S., & Parvardeh, H. (2014) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2014.2354314 · Cited by 474 · see citing works

Review of Unmanned Aerial System (UAS) applications in the built environment: Towards automated building inspection procedures using drones

Rakha, T., & Gorodetsky, A. (2018) · Automation in Construction · doi:10.1016/j.autcon.2018.05.002 · Cited by 469 · see citing works

OpenSeesPy: Python library for the OpenSees finite element framework

Zhu, M., McKenna, F., & Scott, M. H. (2017) · SoftwareX · doi:10.1016/j.softx.2017.10.009 · Cited by 452 · see citing works

Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring

Tang, Z., Chen, Z., Bao, Y., & Li, H. (2018) · Structural Control and Health Monitoring · doi:10.1002/stc.2296 · Cited by 438 · see citing works

Encoder–decoder network for pixel-level road crack detection in black-box images

Bang, S., Park, S., Kim, H., & Kim, H. (2019) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12440 · Cited by 421 · see citing works

Structural damage identification based on autoencoder neural networks and deep learning

Pathirage, C. S. N., Li, J., Li, L., Hao, H., Liu, W., & Ni, P. (2018) · Engineering Structures · doi:10.1016/j.engstruct.2018.05.109 · Cited by 415 · see citing works

Image-based post-disaster inspection of reinforced concrete bridge systems using deep learning with Bayesian optimization

Liang, X. P. (2018) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12425 · Cited by 401 · see citing works

Autonomous UAVs for Structural Health Monitoring Using Deep Learning and an Ultrasonic Beacon System with Geo‐Tagging

Kang, D., & Cha, Y. (2018) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12375 · Cited by 390 · see citing works

Evaluation of deep learning approaches based on convolutional neural networks for corrosion detection

Atha, D., & Jahanshahi, M. R. (2017) · Structural Health Monitoring · doi:10.1177/1475921717737051 · Cited by 383 · see citing works

Detection crack in image using Otsu method and multiple filtering in image processing techniques

Talab, A. M. A., Huang, Z., Fan, X., & HaiMing, L. (2015) · Optik · doi:10.1016/j.ijleo.2015.09.147 · Cited by 341 · see citing works

Recent progress and future trends on damage identification methods for bridge structures

An, Y., Chatzi, E., Sim, S., Laflamme, S., Błachowski, B., & Ou, J. (2019) · Structural Control and Health Monitoring · doi:10.1002/stc.2416 · Cited by 339 · see citing works

Benchmarking State-of-the-Art Deep Learning Software Tools

Shi, S., Wang, Q., Xu, P., & Chu, X. (2016) · doi:10.1109/ccbd.2016.029 · Cited by 334 · see citing works

Vision-based detection of loosened bolts using the Hough transform and support vector machines

Cha, Y., You, K., & Choi, W. (2016) · Automation in Construction · doi:10.1016/j.autcon.2016.06.008 · Cited by 328 · see citing works

Street-view change detection with deconvolutional networks

Alcantarilla, P. F., Stent, S., Ros, G., Arroyo, R., & Gherardi, R. (2018) · Autonomous Robots · doi:10.1007/s10514-018-9734-5 · Cited by 323 · see citing works

Automated Vision-Based Detection of Cracks on Concrete Surfaces Using a Deep Learning Technique

Kim, B., & Cho, S. (2018) · Sensors · doi:10.3390/s18103452 · Cited by 321 · see citing works

Crack and Noncrack Classification from Concrete Surface Images Using Machine Learning

Kim, H., Ahn, E., Shin, M., & Sim, S. (2018) · Structural Health Monitoring · doi:10.1177/1475921718768747 · Cited by 319 · see citing works

A novel deep learning-based method for damage identification of smart building structures

Yu, Y., Wang, C., Gu, X., & Li, J. (2018) · Structural Health Monitoring · doi:10.1177/1475921718804132 · Cited by 312 · see citing works

Vibration-based structural state identification by a 1-dimensional convolutional neural network

Zhang, Y., Miyamori, Y., Mikami, S., & Saito, T. (2019) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12447 · Cited by 307 · see citing works

Deep Learning–Based Fully Automated Pavement Crack Detection on 3D Asphalt Surfaces with an Improved CrackNet

Zhang, A. A., Wang, K. C. P., Fei, Y., Liu, Y., Tao, S., Chen, C., Li, Q., & Li, B. (2018) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000775 · Cited by 306 · see citing works

Automated Pixel-Level Pavement Crack Detection on 3D Asphalt Surfaces with a Recurrent Neural Network

Zhang, A. A., Wang, K. C. P., Fei, Y., Liu, Y., Chen, C., Yang, G., Li, Q., Yang, E., & Qiu, S. (2018) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12409 · Cited by 304 · see citing works

Surface fatigue crack identification in steel box girder of bridges by a deep fusion convolutional neural network based on consumer-grade camera images

Xu, Y., Bao, Y., Chen, J., Zuo, W., & Li, H. (2018) · Structural Health Monitoring · doi:10.1177/1475921718764873 · Cited by 292 · see citing works

Automatic damage detection of historic masonry buildings based on mobile deep learning

Wang, N., Zhao, X., Zhao, P., Zhang, Y., Zou, Z., & Ou, J. (2019) · Automation in Construction · doi:10.1016/j.autcon.2019.03.003 · Cited by 288 · see citing works

Automated defect classification in sewer closed circuit television inspections using deep convolutional neural networks

Kumar, S. S., Abraham, D. M., Jahanshahi, M. R., Iseley, T., & Starr, J. (2018) · Automation in Construction · doi:10.1016/j.autcon.2018.03.028 · Cited by 282 · see citing works

Supervised Deep Restricted Boltzmann Machine for Estimation of Concrete

Rafiei, M. H., Khushefati, W. H., Demirboğa, R., & Adeli, H. (2017) · ACI Materials Journal · doi:10.14359/51689560 · Cited by 277 · see citing works

Application of Crack Identification Techniques for an Aging Concrete Bridge Inspection Using an Unmanned Aerial Vehicle

Kim, I., Jeon, H., Baek, S.-C., Hong, W., & Jung, H. (2018) · Sensors · doi:10.3390/s18061881 · Cited by 277 · see citing works

Performance Comparison of Pretrained Convolutional Neural Networks on Crack Detection in Buildings

Özgenel, Ç. F., & Sorguç, A. G. (2018) · Proceedings of the ... ISARC · doi:10.22260/isarc2018/0094 · Cited by 276 · see citing works

Quality Assessment of Unmanned Aerial Vehicle (UAV) Based Visual Inspection of Structures

Morgenthal, G., & Hallermann, N. (2014) · Advances in Structural Engineering · doi:10.1260/1369-4332.17.3.289 · Cited by 271 · see citing works

Image‐Based Crack Detection for Real Concrete Surfaces

Yamaguchi, T., Nakamura, S., Saegusa, R., & Hashimoto, S. (2007) · IEEJ Transactions on Electrical and Electronic Engineering · doi:10.1002/tee.20244 · Cited by 271 · see citing works

A deep-learning-based approach for fast and robust steel surface defects classification

Fu, G., Sun, P., Zhu, W., Yang, J., Cao, Y., Yang, M. Y., & Cao, Y. (2019) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2019.05.005 · Cited by 271 · see citing works

Vision-based multipoint displacement measurement for structural health monitoring

Feng, D., & Feng, M. Q. (2015) · Structural Control and Health Monitoring · doi:10.1002/stc.1819 · Cited by 270 · see citing works

Adaptive vision-based crack detection using 3D scene reconstruction for condition assessment of structures

Jahanshahi, M. R., & Masri, S. F. (2012) · Automation in Construction · doi:10.1016/j.autcon.2011.11.018 · Cited by 269 · see citing works

Structural Displacement Measurement Using an Unmanned Aerial System

Yoon, H., Shin, J., & Spencer, B. F. (2017) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12338 · Cited by 265 · see citing works

Concrete Cracks Detection Based on Deep Learning Image Classification

da Silva, W. R. L., & de Lucena, D. S. (2018) · International Conference on Experimental Mechanics (ICEM) · doi:10.3390/icem18-05387 · Cited by 260 · see citing works

Deep Learning for Detecting Building Defects Using Convolutional Neural Networks

Perez, H., Tah, J. H. M., & Mosavi, A. H. (2019) · Sensors · doi:10.3390/s19163556 · Cited by 259 · see citing works

Road Damage Detection Using Deep Neural Networks with Images Captured Through a SmartphonePreprint

Maeda, H., Sekimoto, Y., Seto, T., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.09454 · Cited by 250 · see citing works

Automatic classification of pavement crack using deep convolutional neural network

Li, B., Wang, K. C. P., Zhang, A. A., Yang, E., & Wang, G. (2018) · International Journal of Pavement Engineering · doi:10.1080/10298436.2018.1485917 · Cited by 241 · see citing works

Concrete Crack Identification Using a UAV Incorporating Hybrid Image Processing

Kim, H., Lee, J., Ahn, E., Cho, S., Shin, M., & Sim, S. (2017) · Sensors · doi:10.3390/s17092052 · Cited by 237 · see citing works

Unified Approach to Pavement Crack and Sealed Crack Detection Using Preclassification Based on Transfer Learning

Zhang, K., Cheng, H.-D., & Zhang, B. (2018) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000736 · Cited by 235 · see citing works

Wireless and real-time structural damage detection: A novel decentralized method for wireless sensor networks

Avcı, O., Abdeljaber, O., Serkan Kiranyaz, B., Hussein, M. F. M., & Inman, D. J. (2018) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2018.03.008 · Cited by 235 · see citing works

Deep Convolutional Neural Network for Structural Dynamic Response Estimation and System Identification

Wu, R., & Jahanshahi, M. R. (2018) · Journal of Engineering Mechanics · doi:10.1061/(asce)em.1943-7889.0001556 · Cited by 235 · see citing works

Automatic Pavement Crack Detection Based on Structured Prediction with the Convolutional Neural NetworkPreprint

Fan, Z., Wu, Y., Lu, J., & Li, W. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.02208 · Cited by 234 · see citing works

A vision-based method for crack detection in gusset plate welded joints of steel bridges using deep convolutional neural networks

Dung, C. V., Sekiya, H., Hirano, S., Okatani, T., & Chitoshi, M. I. K. I. (2019) · Automation in Construction · doi:10.1016/j.autcon.2019.02.013 · Cited by 234 · see citing works

Image‐Based Concrete Crack Detection Using Convolutional Neural Network and Exhaustive Search Technique

Li, S., & Zhao, X. (2019) · Advances in Civil Engineering · doi:10.1155/2019/6520620 · Cited by 223 · see citing works

Comparative analysis of image binarization methods for crack identification in concrete structures

Kim, H., Ahn, E., Cho, S., Shin, M., & Sim, S. (2017) · Cement and Concrete Research · doi:10.1016/j.cemconres.2017.04.018 · Cited by 221 · see citing works

Structural health monitoring using extremely compressed data through deep learning

Azimi, M., & Pekcan, G. (2019) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12517 · Cited by 216 · see citing works

Deep learning-based automatic volumetric damage quantification using depth camera

Beckman, G. H., Polyzois, D. J., & Cha, Y. (2018) · Automation in Construction · doi:10.1016/j.autcon.2018.12.006 · Cited by 215 · see citing works

Vibration‐based structural condition assessment using convolution neural networks

Khodabandehlou, H., Pekcan, G., & Fadali, M. S. (2018) · Structural Control and Health Monitoring · doi:10.1002/stc.2308 · Cited by 211 · see citing works

Concrete crack detection using context-aware deep semantic segmentation network

Zhang, X., Rajan, D., & STORY, B. A. (2019) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12477 · Cited by 211 · see citing works

Quasi-autonomous bolt-loosening detection method using vision-based deep learning and image processing

Huynh, T., Park, J. H., Jung, H., & Kim, J. (2019) · Automation in Construction · doi:10.1016/j.autcon.2019.102844 · Cited by 209 · see citing works

Rapid entropy-based detection and properties measurement of concrete spalling with machine vision for post-earthquake safety assessments

Paal, S. G., Brilakis, I., & DesRoches, R. (2012) · Advanced Engineering Informatics · doi:10.1016/j.aei.2012.06.005 · Cited by 202 · see citing works

A Robotic Crack Inspection and Mapping System for Bridge Deck Maintenance

Lim, R. S., La, H. M., & Sheng, W. (2014) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2013.2294687 · Cited by 202 · see citing works

Automatic seismic damage identification of reinforced concrete columns from images by a region-based deep convolutional neural network

Xu, Y., Wei, S., Bao, Y., & Li, H. (2019) · Structural Control and Health Monitoring · doi:10.1002/stc.2313 · Cited by 201 · see citing works

Bridge inspection: human performance, unmanned aerial systems and automation

Dorafshan, S., & Maguire, M. (2018) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-018-0285-4 · Cited by 199 · see citing works

Deep learning–based autonomous concrete crack evaluation through hybrid image scanning

Jang, K., Kim, N., & An, Y. (2019) · Structural Health Monitoring · doi:10.1177/1475921718821719 · Cited by 198 · see citing works

Multimedia Big Data Analytics

Pouyanfar, S., Yang, Y., Chen, S., Shyu, M., & Iyengar, S. S. (2018) · ACM Computing Surveys · doi:10.1145/3150226 · Cited by 195 · see citing works

Densely connected deep neural network considering connectivity of pixels for automatic crack detection

Mei, Q., Gül, M., & Azim, R. (2019) · Automation in Construction · doi:10.1016/j.autcon.2019.103018 · Cited by 195 · see citing works

Autonomous bolt loosening detection using deep learning

Zhang, Y., Sun, X., Loh, K. J., Su, W., Xue, Z., & Zhao, X. (2019) · Structural Health Monitoring · doi:10.1177/1475921719837509 · Cited by 188 · see citing works

Lost data recovery for structural health monitoring based on convolutional neural networks

Fan, G., Li, J., & Hao, H. (2019) · Structural Control and Health Monitoring · doi:10.1002/stc.2433 · Cited by 188 · see citing works

Convolutional neural networks for automated damage recognition and damage type identification

Modarres, C., Astorga, N., Droguett, E. L., & Meruane, V. (2018) · Structural Control and Health Monitoring · doi:10.1002/stc.2230 · Cited by 185 · see citing works

A survey and evaluation of promising approaches for automatic image-based defect detection of bridge structures

Jahanshahi, M. R., Kelly, J. S., Masri, S. F., & Sukhatme, G. S. (2008) · Structure and Infrastructure Engineering · doi:10.1080/15732470801945930 · Cited by 184 · see citing works

Fully automated vision-based loosened bolt detection using the Viola–Jones algorithm

Ramana, L., Choi, W., & Cha, Y. (2018) · Structural Health Monitoring · doi:10.1177/1475921718757459 · Cited by 183 · see citing works

Vision-Based Modal Survey of Civil Infrastructure Using Unmanned Aerial Vehicles

Hoskere, V., Park, J., Yoon, H., & Spencer, B. F. (2019) · Journal of Structural Engineering · doi:10.1061/(asce)st.1943-541x.0002321 · Cited by 177 · see citing works

Deeper Networks for Pavement Crack Detection

Pauly, L., Peel, H., Luo, S., Hogg, D., & Fuentes, R. (2017) · International Symposium on Automation and Robotics in Construction (ISARC) · doi:10.22260/isarc2017/0066 · Cited by 173 · see citing works

Ensemble of Deep Convolutional Neural Networks for Automatic Pavement Crack Detection and Measurement

Fan, Z., Li, C., Chen, Y., Mascio, P. D., Chen, X., Zhu, G., & Loprencipe, G. (2020) · Coatings · doi:10.3390/coatings10020152 · Cited by 173 · see citing works

The State-of-the-Art on Framework of Vibration-Based Structural Damage Identification for Decision Making

Kong, X., Cai, C. S. S., & Hu, J. (2017) · Applied Sciences · doi:10.3390/app7050497 · Cited by 171 · see citing works

Single-Sensor Acoustic Emission Source Localization in Plate-Like Structures Using Deep Learning

Ebrahimkhanlou, A., & Salamone, S. (2018) · Aerospace · doi:10.3390/aerospace5020050 · Cited by 171 · see citing works

Global Navigation Satellite System‐based positioning technology for structural health monitoring: a review

Yu, J., Meng, X., Yan, B., Xu, B., Fan, Q., & Xie, Y. (2019) · Structural Control and Health Monitoring · doi:10.1002/stc.2467 · Cited by 169 · see citing works

A New Structural Health Monitoring Strategy Based on PZT Sensors and Convolutional Neural Network

Oliveira, M. D., Monteiro, A. V., & Filho, J. V. (2018) · Sensors · doi:10.3390/s18092955 · Cited by 162 · see citing works

Subsurface damage detection of a steel bridge using deep learning and uncooled micro-bolometer

Ali, R., & Cha, Y. (2019) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2019.07.293 · Cited by 158 · see citing works

Time-Frequency-Based Data-Driven Structural Diagnosis and Damage Detection for Cable-Stayed Bridges

Pan, H. X., Azimi, M., Yan, F., & Lin, Z. (2018) · Journal of Bridge Engineering · doi:10.1061/(asce)be.1943-5592.0001199 · Cited by 157 · see citing works

Damage Classification for Masonry Historic Structures Using Convolutional Neural Networks Based on Still Images

Wang, N., Zhao, Q., Li, S., Zhao, X., & Zhao, P. (2018) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12411 · Cited by 157 · see citing works

Vibration signal denoising for structural health monitoring by residual convolutional neural networks

Fan, G., Li, J., & Hao, H. (2020) · Measurement · doi:10.1016/j.measurement.2020.107651 · Cited by 155 · see citing works

Zernike-moment measurement of thin-crack width in images enabled by dual-scale deep learning

Ni, F., Zhang, J., & Chen, Z. (2018) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12421 · Cited by 153 · see citing works

New Crack Detection Method for Bridge Inspection Using UAV Incorporating Image Processing

Lei, B., Wang, N., Xu, P., & Song, G. (2018) · Journal of Aerospace Engineering · doi:10.1061/(asce)as.1943-5525.0000879 · Cited by 150 · see citing works

Deep leaf-bootstrapping generative adversarial network for structural image data augmentation

Gao, Y., Kong, B., & Mosalam, K. M. (2019) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12458 · Cited by 144 · see citing works

Automated crack evaluation of a high‐rise bridge pier using a ring‐type climbing robot

Jang, K., An, Y., Kim, B., & Cho, S. (2020) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12550 · Cited by 144 · see citing works

Digital image processing techniques for the detection and removal of cracks in digitized paintings

Giakoumis, I., Nikolaidis, N., & Pitas, I. (2005) · IEEE Transactions on Image Processing · doi:10.1109/tip.2005.860311 · Cited by 143 · see citing works

Development and application of a deep learning–based sparse autoencoder framework for structural damage identification

Pathirage, C. S. N., Li, J., Li, L., Hao, H., Liu, W., & Wang, R. (2018) · Structural Health Monitoring · doi:10.1177/1475921718800363 · Cited by 142 · see citing works

Recognition of asphalt pavement crack length using deep convolutional neural networks

Zheng, T., Gao, J., Han, Z., & Wang, Z. (2017) · Road Materials and Pavement Design · doi:10.1080/14680629.2017.1308265 · Cited by 140 · see citing works

Patch-Based Crack Detection in Black Box Images Using Convolutional Neural Networks

Park, S., Bang, S., Kim, H., & Kim, H. (2019) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000831 · Cited by 140 · see citing works

Machine vision-based model for spalling detection and quantification in subway networks

Dawood, T., Zhu, Z., & Zayed, T. M. (2017) · Automation in Construction · doi:10.1016/j.autcon.2017.06.008 · Cited by 132 · see citing works

A new methodology for non-contact accurate crack width measurement through photogrammetry for automated structural safety evaluation

Jahanshahi, M. R., & Masri, S. F. (2013) · Smart Materials and Structures · doi:10.1088/0964-1726/22/3/035019 · Cited by 129 · see citing works

Identification framework for cracks on a steel structure surface by a restricted Boltzmann machines algorithm based on consumer-grade camera images

Xu, Y., Li, S., Zhang, D., Jin, Y., Zhang, F., Li, N., & Li, H. (2017) · Structural Control and Health Monitoring · doi:10.1002/stc.2075 · Cited by 129 · see citing works

Flexural cracking behavior of normal strength, high strength and high strength fiber concrete beams, using Digital Image Correlation technique

Hamrat, M., Boulekbache, B., Chemrouk, M., & Amziane, S. (2016) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2015.12.166 · Cited by 128 · see citing works

Micro-cracking monitoring and fracture evaluation for crumb rubber concrete based on acoustic emission techniques

Xu, J., Fu, Z., Han, Q., Lacidogna, G., & Carpinteri, A. (2017) · Structural Health Monitoring · doi:10.1177/1475921717730538 · Cited by 124 · see citing works

Visual data classification in post-event building reconnaissance

Yeum, C. M., Dyke, S. J., & Ramirez, J. (2017) · Engineering Structures · doi:10.1016/j.engstruct.2017.10.057 · Cited by 124 · see citing works

Multifractal analysis of crack patterns in reinforced concrete shear walls

Ebrahimkhanlou, A., Farhidzadeh, A., & Salamone, S. (2016) · Structural Health Monitoring · doi:10.1177/1475921715624502 · Cited by 119 · see citing works

Structural damage identification using an enhanced thermal exchange optimization algorithm

Kaveh, A., & Eslamlou, A. D. (2017) · Engineering Optimization · doi:10.1080/0305215x.2017.1318872 · Cited by 119 · see citing works

Postdisaster image-based damage detection and repair cost estimation of reinforced concrete buildings using dual convolutional neural networks

Pan, X., & Yang, T. Y. (2020) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12549 · Cited by 119 · see citing works

Convolutional neural network-based wind-induced response estimation model for tall buildings

Oh, B. K., Glišić, B., Kim, Y., & Park, H. S. (2019) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12476 · Cited by 118 · see citing works

Vision-based automated bridge component recognition with high-level scene consistency

Narazaki, Y., Hoskere, V., Hoang, T. A., Fujino, Y., Sakurai, A., & Spencer, B. F. (2019) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12505 · Cited by 117 · see citing works

CRACK DAMAGE DETECTION IN UNMANNED AERIAL VEHICLE IMAGES OF CIVIL INFRASTRUCTURE USING PRE-TRAINED DEEP LEARNING MODEL

(2018) · INTERNATIONAL JOURNAL FOR TRAFFIC AND TRANSPORT ENGINEERING · doi:10.7708/ijtte.2018.8(1).01 · Cited by 117 · see citing works

Fault diagnosis on beam-like structures from modal parameters using artificial neural networks

Hakim, S. J. S., Razak, H. A., & Ravanfar, S. A. (2015) · Measurement · doi:10.1016/j.measurement.2015.08.021 · Cited by 113 · see citing works

Instance-level recognition and quantification for concrete surface bughole based on deep learning

Wei, F., Yao, G., Yang, Y., & Sun, Y. (2019) · Automation in Construction · doi:10.1016/j.autcon.2019.102920 · Cited by 113 · see citing works

Multi-Resolution Feature Fusion for Image Classification of Building Damages with Convolutional Neural Networks

Duarte, D., Nex, F., Kerle, N., & George Vosselman, A. (2018) · Remote Sensing · doi:10.3390/rs10101636 · Cited by 110 · see citing works

Synthesis of Unmanned Aerial Vehicle Applications for Infrastructures

Duque, L., Seo, J., & Wacker, J. P. (2018) · Journal of Performance of Constructed Facilities · doi:10.1061/(asce)cf.1943-5509.0001185 · Cited by 109 · see citing works

Identifying Collapsed Buildings Using Post-Earthquake Satellite Imagery and Convolutional Neural Networks: A Case Study of the 2010 Haiti Earthquake

Ji, M., Liu, L., & Buchroithner, M. F. (2018) · Remote Sensing · doi:10.3390/rs10111689 · Cited by 109 · see citing works

Structural health monitoring of an operational bridge: A case study

Gatti, M. (2019) · Engineering Structures · doi:10.1016/j.engstruct.2019.05.102 · Cited by 108 · see citing works

Improvement of Crack-Detection Accuracy Using a Novel Crack Defragmentation Technique in Image-Based Road Assessment

Wu, L., Mokhtari, S., Nazef, A., Nam, B., & Yun, H. (2014) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000451 · Cited by 107 · see citing works

Automatic Tunnel Crack Detection Based on U-Net and a Convolutional Neural Network with Alternately Updated Clique

Li, G., Ma, B., He, S., Ren, X., & Liu, Q. (2020) · Sensors · doi:10.3390/s20030717 · Cited by 107 · see citing works

Convolutional Neural Network Approach for Robust Structural Damage Detection and Localization

Gulgec, N. S., Takáč, M., & Pakzad, S. N. (2019) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000820 · Cited by 102 · see citing works

Structural crack detection using deep learning–based fully convolutional networks

Ye, X., Jin, T., & Chen, P. (2019) · Advances in Structural Engineering · doi:10.1177/1369433219836292 · Cited by 102 · see citing works

Autonomous damage segmentation and measurement of glazed tiles in historic buildings via deep learning

Wang, N., Zhao, X., Zou, Z., Zhao, P., & Qi, F. (2019) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12488 · Cited by 97 · see citing works

Embedded Image Processing Systems for Automatic Recognition of Cracks using UAVs

Pereira, F. C., & Pereira, C. E. (2015) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2015.08.101 · Cited by 96 · see citing works

Fatigue Crack Detection Using Unmanned Aerial Systems in Fracture Critical Inspection of Steel Bridges

Dorafshan, S., Thomas, R. J., & Maguire, M. (2018) · Journal of Bridge Engineering · doi:10.1061/(asce)be.1943-5592.0001291 · Cited by 94 · see citing works

Pruning deep convolutional neural networks for efficient edge computing in condition assessment of infrastructures

Wu, R., Singla, A., Jahanshahi, M. R., Bertino, E., Ko, B. J., & Verma, D. C. (2019) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12449 · Cited by 93 · see citing works

Comparative Study of Deep Learning Software FrameworksPreprint

Bahrampour, S., Ramakrishnan, N., Schott, L., & Shah, M. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.06435 · Cited by 89 · see citing works

Vibration-based semantic damage segmentation for large-scale structural health monitoring

Sajedi, S. O., & Liang, X. P. (2019) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12523 · Cited by 89 · see citing works

Automated region-of-interest localization and classification for vision-based visual assessment of civil infrastructure

Yeum, C. M., Choi, J. B., & Dyke, S. J. (2018) · Structural Health Monitoring · doi:10.1177/1475921718765419 · Cited by 87 · see citing works

Image-driven structural steel damage condition assessment method using deep learning algorithm

Liu, H., & Zhang, Y. (2018) · Measurement · doi:10.1016/j.measurement.2018.09.081 · Cited by 87 · see citing works

Artificial Intelligence Assisted Infrastructure Assessment using Mixed Reality Systems

Karaaslan, E., Bağcı, U., & Çatbaş, F. N. (2019) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/0361198119839988 · Cited by 87 · see citing works

Deep Learning for Critical Infrastructure Resilience

Dick, K., Russell, L., Dosso, Y. S., Kwamena, F., & Green, J. R. (2019) · Journal of Infrastructure Systems · doi:10.1061/(asce)is.1943-555x.0000477 · Cited by 86 · see citing works

Automatic concrete cracks detection and mapping of terrestrial laser scan data

Rabah, M., El-Hattab, A., & Fayad, A. (2013) · NRIAG Journal of Astronomy and Geophysics · doi:10.1016/j.nrjag.2013.12.002 · Cited by 84 · see citing works

Vision-based Structural Inspection using Multiscale Deep Convolutional Neural NetworksPreprint

Hoskere, V., Narazaki, Y., Hoang, T. A., & Spencer, B. F. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.01055 · Cited by 83 · see citing works

Automated assessment of cracks on concrete surfaces using adaptive digital image processing

Liu, Y.-F., Cho, S., Spencer, B. F., & Fan, J. (2014) · Smart Structures and Systems · doi:10.12989/sss.2014.14.4.719 · Cited by 81 · see citing works

A method of detecting the cracks of concrete undergo high-temperature

Li, L., Wang, Q., Zhang, G., Shi, L. X., Dong, J., & Jia, P. (2017) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2017.12.010 · Cited by 79 · see citing works

SDNET2018: A concrete crack image dataset for machine learning applications

Maguire, M., Dorafshan, S., & Thomas, R. J. (2018) · Digital Commons - USU (Utah State University) · doi:10.15142/t3td19 · Cited by 79 · see citing works

Combined Imaging Technologies for Concrete Bridge Deck Condition Assessment

Vaghefi, K., Ahlborn, T. M., Harris, D. K., & Brooks, C. (2013) · Journal of Performance of Constructed Facilities · doi:10.1061/(asce)cf.1943-5509.0000465 · Cited by 77 · see citing works

Multi-level feature fusion in densely connected deep-learning architecture and depth-first search for crack segmentation on images collected with smartphones

Mei, Q., & Gül, M. (2020) · Structural Health Monitoring · doi:10.1177/1475921719896813 · Cited by 76 · see citing works

Benchmarking Image Processing Algorithms for Unmanned Aerial System-Assisted Crack Detection in Concrete Structures

Dorafshan, S., Thomas, R. J., & Maguire, M. (2019) · Infrastructures · doi:10.3390/infrastructures4020019 · Cited by 72 · see citing works

An enhanced response sensitivity approach for structural damage identification: convergence and performance

Lu, Z., & Wang, L. (2016) · International Journal for Numerical Methods in Engineering · doi:10.1002/nme.5502 · Cited by 70 · see citing works

Benchmarking State-of-the-Art Deep Learning Software ToolsPreprint

Shi, S., Wang, Q., Xu, P., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1608.07249 · Cited by 69 · see citing works

Deep Learning for Asphalt Pavement Cracking Recognition Using Convolutional Neural Network

Wang, K. C. P., Zhang, A. A., Li, Q., Fei, Y., Chen, C., & Li, B. (2017) · ASCE International Airfield and Highway Pavements Conference · doi:10.1061/9780784480922.015 · Cited by 69 · see citing works

Crack Detection in a Concrete Beam using Two Different Camera Techniques

Lecompte, D., Vantomme, J., & Sol, H. (2006) · Structural Health Monitoring · doi:10.1177/1475921706057982 · Cited by 68 · see citing works

Visual change detection on tunnel linings

Stent, S., Gherardi, R., Stenger, B., Soga, K., & Cipolla, R. (2014) · Machine Vision and Applications · doi:10.1007/s00138-014-0648-8 · Cited by 67 · see citing works

Deep Learning Neural Networks for sUAS-Assisted Structural Inspections: Feasibility and Application

Dorafshan, S., Thomas, R. J., Coopmans, C., & Maguire, M. (2018) · International Conference on Unmanned Aircraft Systems (ICUAS) · doi:10.1109/icuas.2018.8453409 · Cited by 66 · see citing works

SHMnet: Condition assessment of bolted connection with beyond human-level performance

Zhang, T., Biswal, S., & Wang, Y. (2019) · Structural Health Monitoring · doi:10.1177/1475921719881237 · Cited by 66 · see citing works

Deep Learning for Detecting Building Defects Using Convolutional Neural NetworksPreprint

Perez, H., Tah, J. H. M., & Mosavi, A. (2019) · Preprints.org · doi:10.20944/preprints201908.0068.v1 · Cited by 63 · see citing works

Region-Based CNN Method with Deformable Modules for Visually Classifying Concrete Cracks

Deng, L., Chu, H.-H., Shi, P., Wang, W., & Kong, X. (2020) · Applied Sciences · doi:10.3390/app10072528 · Cited by 60 · see citing works

Towards Automated Post-Earthquake Inspections with Deep Learning-based Condition-Aware ModelsPreprint

Hoskere, V., Narazaki, Y., Hoang, T. A., & Spencer, B. F. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.09195 · Cited by 56 · see citing works

Crack Detection in Masonry Structures using Convolutional Neural Networks and Support Vector Machines

Chaiyasarn, K., Khan, W., Ali, L., Sharma, M. K., Brackenbury, D., & DeJong, M. J. (2018) · International Symposium on Automation and Robotics in Construction (ISARC) · doi:10.22260/isarc2018/0016 · Cited by 55 · see citing works

FPCNet: Fast Pavement Crack Detection Network Based on Encoder-Decoder ArchitecturePreprint

Liu, W., Huang, Y., Li, Y., & Chen, Q. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.02248 · Cited by 54 · see citing works

Modal Strain Energy-Based Structural Damage Detection Using Convolutional Neural Networks

Teng, S., Chen, G., Liu, G., Lv, J., & Cui, F. (2019) · Applied Sciences · doi:10.3390/app9163376 · Cited by 54 · see citing works

Convolutional neural networks-based crack detection for real concrete surface

Zhao, X., & Li, S. (2018) · Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems · doi:10.1117/12.2296536 · Cited by 53 · see citing works

Structural damage identification by sparse deep belief network using uncertain and limited data

Ding, Z., Li, J., & Hao, H. S. (2020) · Structural Control and Health Monitoring · doi:10.1002/stc.2522 · Cited by 50 · see citing works

Damage Localization of Cable‐Supported Bridges Using Modal Frequency Data and Probabilistic Neural Network

Zhou, X. T., Ni, Y., & Zhang, F. L. (2014) · Mathematical Problems in Engineering · doi:10.1155/2014/837963 · Cited by 48 · see citing works

Processing Digital Images for Crack Localization in Reinforced Concrete Members

Rimkus, A., Podviezko, A., & Gribniak, V. (2015) · Procedia Engineering · doi:10.1016/j.proeng.2015.10.031 · Cited by 48 · see citing works

Development and full-scale validation of high-fidelity data acquisition on a next-generation wireless smart sensor platform

Fu, Y., Mechitov, K. A., Hoang, T., Kim, J. R., Lee, D. H., & Spencer, B. F. (2019) · Advances in Structural Engineering · doi:10.1177/1369433219866093 · Cited by 47 · see citing works

Parametric Performance Evaluation of Wavelet-Based Corrosion Detection Algorithms for Condition Assessment of Civil Infrastructure Systems

Jahanshahi, M. R., & Masri, S. F. (2012) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000225 · Cited by 46 · see citing works

Deep faster R-CNN-based automated detection and localization of multiple types of damage

Suh, G., & Cha, Y. (2018) · Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems · doi:10.1117/12.2295954 · Cited by 46 · see citing works

Structural Damage Detection by Integrating Data Fusion and Probabilistic Neural Network

Jiang, S.-F., Zhang, C., & Koh, C. G. (2006) · Advances in Structural Engineering · doi:10.1260/136943306778812787 · Cited by 45 · see citing works

Probabilistic Neural Network and Fuzzy Cluster Analysis Methods Applied to Impedance-Based SHM for Damage Classification

Palomino, L. V., Steffen, V., & Neto, R. M. F. (2014) · Shock and Vibration · doi:10.1155/2014/401942 · Cited by 43 · see citing works

Detectability of Bridge-Structural Damage Based on Fiber-Optic Sensing through Deep-Convolutional Neural Networks

Li, S., & Sun, L. (2020) · Journal of Bridge Engineering · doi:10.1061/(asce)be.1943-5592.0001531 · Cited by 43 · see citing works

A New Structural Health Monitoring Strategy Based on PZT Sensors and Convolutional Neural NetworkPreprint

Oliveira, M. D., Monteiro, A. V., & Filho, J. V. (2018) · Preprints.org · doi:10.20944/preprints201808.0130.v1 · Cited by 43 · see citing works

Vision-Based Concrete Crack Detection Using a Convolutional Neural Network

Cha, Y., & Choi, W. (2017) · Conference proceedings of the Society for Experimental Mechanics · doi:10.1007/978-3-319-54777-0_9 · Cited by 42 · see citing works

Single-sensor acoustic emission source localization in plate-like structures: a deep learning approach

Ebrahimkhanlou, A., & Salamone, S. (2018) · Health Monitoring of Structural and Biological Systems XII · doi:10.1117/12.2296613 · Cited by 42 · see citing works

Applying Deep Learning to Continuous Bridge Deflection Detected by Fiber Optic Gyroscope for Damage Detection

Li, S., Zuo, X., Li, Z., & Wang, H. (2020) · Sensors · doi:10.3390/s20030911 · Cited by 42 · see citing works

Nonlinear seismic response reconstruction and performance assessment of instrumented wood‐frame buildings—Validation using NEESWood Capstone full‐scale tests

Roohi, M., Hernandez, E. M., & Rosowsky, D. V. (2019) · Structural Control and Health Monitoring · doi:10.1002/stc.2373 · Cited by 40 · see citing works

Automatic Surface Crack Detection in Concrete Structures Using OTSU Thresholding and Morphological Operations

Dorafshan, S., Maguire, M., & Qi, X. (2016) · Digital Commons - USU (Utah State University) · doi:10.13140/rg.2.2.34024.47363 · Cited by 38 · see citing works

Smartphone-Based Mobile Testing Technique for Quick Bridge Cable–Force Measurement

Zhao, X., Han, R., Yu, Y. P., Hu, W., Jiao, D., Mao, X., Li, M., & Ou, J. (2016) · Journal of Bridge Engineering · doi:10.1061/(asce)be.1943-5592.0001011 · Cited by 36 · see citing works

Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights

Malekloo, A., Ozer, E., AlHamaydeh, M., & Girolami, M. (2022) · Structural Health Monitoring · doi:10.1177/14759217211036880 · Cited by 607 · see citing works

Vibration feature extraction using signal processing techniques for structural health monitoring: A review

Zhang, C., Mousavi, A. A., Masri, S. F., Gholipour, G., Yan, K., & Li, X. (2022) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2022.109175 · Cited by 411 · see citing works

3D vision technologies for a self-developed structural external crack damage recognition robot

Hu, K., Chen, Z., Kang, H., & Tang, Y. (2024) · Automation in Construction · doi:10.1016/j.autcon.2023.105262 · Cited by 324 · see citing works

DenseSPH-YOLOv5: An automated damage detection model based on DenseNet and Swin-Transformer prediction head-enabled YOLOv5 with attention mechanism

Roy, A. M., & Bhaduri, J. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.102007 · Cited by 309 · see citing works

Three decades of statistical pattern recognition paradigm for SHM of bridges

Figueiredo, E., & Brownjohn, J. (2022) · Structural Health Monitoring · doi:10.1177/14759217221075241 · Cited by 251 · see citing works

Vision transformer-based autonomous crack detection on asphalt and concrete surfaces

Shamsabadi, E. A., Xu, C., Rao, A. S., Nguyen, T., Ngo, T., & Dias‐da‐Costa, D. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104316 · Cited by 247 · see citing works

Review on computer vision-based crack detection and quantification methodologies for civil structures

Deng, J., Singh, A., Zhou, Y., Lü, Y., & Lee, V. C. S. (2022) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2022.129238 · Cited by 224 · see citing works

Artificial Intelligence and Structural Health Monitoring of Bridges: A Review of the State-of-the-Art

Zinno, R., Haghshenas, S. S., Guido, G., & Vitale, A. (2022) · IEEE Access · doi:10.1109/access.2022.3199443 · Cited by 181 · see citing works

Recent trends in smartphone-based detection for biomedical applications: a review

Banik, S., Melanthota, S. K., Arbaaz, Vaz, J. M., Kadambalithaya, V. M., Hussain, I., Dutta, S., & Mazumder, N. (2021) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-021-03184-z · Cited by 176 · see citing works

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

Eltouny, K., Gomaa, M., & Liang, X. P. (2023) · Sensors · doi:10.3390/s23063290 · Cited by 163 · see citing works

A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic

Cunha, B., Droz, C., Zine, A., Foulard, S., & Ichchou, M. (2022) · Mechanical systems and signal processing · doi:10.1016/j.ymssp.2023.110535 · Cited by 164 · see citing works

Semantics-Empowered Communications: A Tutorial-Cum-Survey

Lu, Z., Li, R., Lu, K., Chen, X., Hossain, E., Zhao, Z., & Zhang, H. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3333342 · Cited by 157 · see citing works

Continuous missing data imputation with incomplete dataset by generative adversarial networks–based unsupervised learning for long-term bridge health monitoring

Jiang, H., Wan, C., Yang, K., Ding, Y., & Xue, S. (2021) · Structural Health Monitoring · doi:10.1177/14759217211021942 · Cited by 154 · see citing works

Application of drones in the architecture, engineering, and construction (AEC) industry

Nwaogu, J. M., Yang, Y., Chan, A. P. C., & Chi, H.-L. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.104827 · Cited by 150 · see citing works

Deep learning-based crack segmentation for civil infrastructure: data types, architectures, and benchmarked performance

Zhou, S., Canchila, C., & Song, W. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104678 · Cited by 149 · see citing works

Deep Learning for Structural Health Monitoring: Data, Algorithms, Applications, Challenges, and Trends

Jia, J., & Li, Y. (2023) · Sensors · doi:10.3390/s23218824 · Cited by 148 · see citing works

A Systematic Review of Optimization Algorithms for Structural Health Monitoring and Optimal Sensor Placement

Hassani, S., & Dackermann, U. (2023) · Sensors · doi:10.3390/s23063293 · Cited by 146 · see citing works

Review of Machine-Learning Techniques Applied to Structural Health Monitoring Systems for Building and Bridge Structures

Gomez-Cabrera, A., & Escamilla-Ambrosio, P. J. (2022) · Applied Sciences · doi:10.3390/app122110754 · Cited by 146 · see citing works

Vibration-based multiclass damage detection and localization using long short-term memory networks

Sony, S., Gamage, S., Sadhu, A., & Samarabandu, J. (2022) · Structures · doi:10.1016/j.istruc.2021.10.088 · Cited by 140 · see citing works

A Review of Data Management and Visualization Techniques for Structural Health Monitoring Using BIM and Virtual or Augmented Reality

Sadhu, A., Peplinski, J. E., Mohammadkhorasani, A., & Moreu, F. (2022) · Journal of Structural Engineering · doi:10.1061/(asce)st.1943-541x.0003498 · Cited by 134 · see citing works

Monitoring of transport infrastructure exposed to multiple hazards: a roadmap for building resilience

Achillopoulou, D. V., Mitoulis, S.-A., Argyroudis, S. A., & Wang, Y. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.141001 · Cited by 133 · see citing works

Machine learning applied to the design and inspection of reinforced concrete bridges: Resilient methods and emerging applications

Fan, W., Chen, Y., Li, J., Sun, Y., Feng, J., Hassanin, H. S., & Sareh, P. (2021) · Structures · doi:10.1016/j.istruc.2021.06.110 · Cited by 133 · see citing works

A Review of Computer Vision-Based Crack Detection Methods in Civil Infrastructure: Progress and Challenges

Yuan, Q., Shi, Y., & Li, M. (2024) · Remote Sensing · doi:10.3390/rs16162910 · Cited by 133 · see citing works

AI in structural health monitoring for infrastructure maintenance and safety

Plevris, V., & Papazafeiropoulos, G. (2024) · Infrastructures · doi:10.3390/infrastructures9120225 · Cited by 132 · see citing works

Autonomous damage recognition in visual inspection of laminated composite structures using deep learning

Fotouhi, S., Pashmforoush, F., Bodaghi, M., & Fotouhi, M. (2021) · Composite Structures · doi:10.1016/j.compstruct.2021.113960 · Cited by 117 · see citing works

A review of ultrasonic sensing and machine learning methods to monitor industrial processes

Bowler, A. L., Pound, M. P., & Watson, N. J. (2022) · Ultrasonics · doi:10.1016/j.ultras.2022.106776 · Cited by 111 · see citing works

Damage detection in slab structures based on two-dimensional curvature mode shape method and Faster R-CNN

Nguyen, D. H., & Wahab, M. M. A. (2022) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2022.103371 · Cited by 106 · see citing works

Deep learning for crack detection on masonry façades using limited data and transfer learning

Katsigiannis, S., Seyedzadeh, S., Agapiou, A., & Ramzan, N. (2023) · Journal of Building Engineering · doi:10.1016/j.jobe.2023.107105 · Cited by 106 · see citing works

A review on structural health monitoring: past to present

Katam, R., Pasupuleti, V. D. K., & Kalapatapu, P. (2023) · Innovative Infrastructure Solutions · doi:10.1007/s41062-023-01217-3 · Cited by 105 · see citing works

Optical Fiber Sensors for Ultrasonic Structural Health Monitoring: A Review

Soman, R., Wee, J., & Peters, K. J. (2021) · Sensors · doi:10.3390/s21217345 · Cited by 103 · see citing works

Deep neural network-based structural health monitoring technique for real-time crack detection and localization using strain gauge sensors

Yoon, J., Lee, J., Kim, G., Ryu, S., & Park, J. (2022) · Scientific Reports · doi:10.1038/s41598-022-24269-4 · Cited by 101 · see citing works

Lightweight semantic segmentation of complex structural damage recognition for actual bridges

Xu, Y., Fan, Y., & Li, H. (2023) · Structural Health Monitoring · doi:10.1177/14759217221147015 · Cited by 101 · see citing works

Explainable 1-D convolutional neural network for damage detection using Lamb wave

Pandey, P., Rai, A., & Mitra, M. (2022) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2021.108220 · Cited by 100 · see citing works

A novel unsupervised deep learning approach for vibration-based damage diagnosis using a multi-head self-attention LSTM autoencoder

Ghazimoghadam, S., & Hosseinzadeh, S. A. A. (2024) · Measurement · doi:10.1016/j.measurement.2024.114410 · Cited by 100 · see citing works

Revolutionizing concrete analysis: An in-depth survey of AI-powered insights with image-centric approaches on comprehensive quality control, advanced crack detection and concrete property exploration

Sarkar, K., Shiuly, A., & Dhal, K. G. (2023) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2023.134212 · Cited by 98 · see citing works

Artificial intelligence-based visual inspection system for structural health monitoring of cultural heritage

Mishra, M., Barman, T., & Ramana, G. V. (2022) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-022-00643-8 · Cited by 97 · see citing works

Recent computer vision applications for pavement distress and condition assessment

Hakea, A. H. E., & Fakhr, M. W. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104664 · Cited by 97 · see citing works

UAV-based deep learning applications for automated inspection of civil infrastructure

Lyu, C., Lin, S., Lynch, A., Zou, Y., & Liarokapis, M. V. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.106285 · Cited by 97 · see citing works

Machine learning and image processing approaches for estimating concrete surface roughness using basic cameras

Valikhani, A., Jahromi, A. J., Pouyanfar, S., Mantawy, I. M., & Azizinamini, A. (2020) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12605 · Cited by 92 · see citing works

Temperature Effect on Vibration Properties and Vibration-Based Damage Identification of Bridge Structures: A Literature Review

Luo, J., Huang, M., & Lei, Y. (2022) · Buildings · doi:10.3390/buildings12081209 · Cited by 90 · see citing works

Supervised Deep Learning with Finite Element simulations for damage identification in bridges

Fernández-Navamuel, A., Zamora-Sánchez, D., Omella, Á. J., Pardo, D., García-Sánchez, D., & Magalhães, F. (2022) · Engineering Structures · doi:10.1016/j.engstruct.2022.114016 · Cited by 88 · see citing works

Non-Destructive Testing and Evaluation of Hybrid and Advanced Structures: A Comprehensive Review of Methods, Applications, and Emerging Trends

Abdollahi-Mamoudan, F., Ibarra‐Castanedo, C., & Maldague, X. (2025) · Sensors · doi:10.3390/s25123635 · Cited by 88 · see citing works

The Need for Multi-Sensor Data Fusion in Structural Health Monitoring of Composite Aircraft Structures

Broer, A. A. R., Benedictus, R., & Zarouchas, D. (2022) · Aerospace · doi:10.3390/aerospace9040183 · Cited by 86 · see citing works

The application of deep learning in bridge health monitoring: a literature review

Zhang, G.-Q., Wang, B., Li, J., & Xu, Y. L. (2022) · Advances in Bridge Engineering · doi:10.1186/s43251-022-00078-7 · Cited by 86 · see citing works

The State of the Art of Artificial Intelligence Approaches and New Technologies in Structural Health Monitoring of Bridges

Zinno, R., Haghshenas, S. S., Guido, G., Rashvand, K., Vitale, A., & Sarhadi, A. (2022) · Applied Sciences · doi:10.3390/app13010097 · Cited by 82 · see citing works

Bridge health monitoring in environmental variability by new clustering and threshold estimation methods

Sarmadi, H., Entezami, A., Salar, M., & Michele, C. D. (2021) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-021-00472-1 · Cited by 81 · see citing works

Data-Driven Monitoring and Predictive Maintenance for Engineering Structures: Technologies, Implementation Challenges, and Future Directions

Chen, Q., Cao, J., & Zhu, S. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3272535 · Cited by 81 · see citing works

A data-driven structural damage identification approach using deep convolutional-attention-recurrent neural architecture under temperature variations

Fathnejat, H., Ahmadi-Nedushan, B., Hosseininejad, S., Noori, M., & Altabey, W. A. (2023) · Engineering Structures · doi:10.1016/j.engstruct.2022.115311 · Cited by 79 · see citing works

Typical advances of artificial intelligence in civil engineering

Xu, Y., Qian, W., Li, N., & Li, H. (2022) · Advances in Structural Engineering · doi:10.1177/13694332221127340 · Cited by 78 · see citing works

Damage identification of steel bridge based on data augmentation and adaptive optimization neural network

Huang, M., Zhang, J., Li, J., Deng, Z., & Luo, J. (2024) · Structural Health Monitoring · doi:10.1177/14759217241255042 · Cited by 76 · see citing works

Surface Cracking and Fractal Characteristics of Bending Fractured Polypropylene Fiber-Reinforced Geopolymer Mortar

Li, L., Sun, H.-X., Zhang, Y., & Yu, B. (2021) · Fractal and Fractional · doi:10.3390/fractalfract5040142 · Cited by 75 · see citing works

Automated Road Defect and Anomaly Detection for Traffic Safety: A Systematic Review

Rathee, M., Bačić, B., & Doborjeh, M. (2023) · Sensors · doi:10.3390/s23125656 · Cited by 72 · see citing works

U-Net-Based CNN Architecture for Road Crack Segmentation

Benedetto, A. D., Fiani, M., & Gujski, L. M. (2023) · Infrastructures · doi:10.3390/infrastructures8050090 · Cited by 71 · see citing works

Artificial intelligence in real-time diagnostics and prognostics of composite materials and its uncertainties—a review

Elenchezhian, M. R. P., Vadlamudi, V., Raihan, R., Reifsnider, K., & Reifsnider, E. S. (2021) · Smart Materials and Structures · doi:10.1088/1361-665x/ac099f · Cited by 70 · see citing works

Quantitative loosening detection of threaded fasteners using vision-based deep learning and geometric imaging theory

Gong, H., Deng, X., Liu, J., & Huang, J. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.104009 · Cited by 70 · see citing works

Deep generative Bayesian optimization for sensor placement in structural health monitoring

Sajedi, S., & Liang, X. (2022) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12799 · Cited by 68 · see citing works

Classification and regression-based convolutional neural network and long short-term memory configuration for bridge damage identification using long-term monitoring vibration data

Yessoufou, F., & Zhu, J. (2023) · Structural Health Monitoring · doi:10.1177/14759217231161811 · Cited by 68 · see citing works

Barely visible impact damage detection in composite structures using deep learning networks with varying complexities

Tabatabaeian, A., Jerkovic, B., Harrison, P. G., Marchiori, E., & Fotouhi, M. (2023) · Composites Part B Engineering · doi:10.1016/j.compositesb.2023.110907 · Cited by 68 · see citing works

Modal Analysis of Offshore Monopile Wind Turbine: An Analytical Solution

Pezeshki, H., Pavlou, D. G., Adeli, H., & Siriwardane, S. C. (2022) · Journal of Offshore Mechanics and Arctic Engineering · doi:10.1115/1.4055402 · Cited by 67 · see citing works

Artificial intelligence in civil infrastructure health monitoring—Historical perspectives, current trends, and future visions

Mondal, T. G., & Chen, G. (2022) · Frontiers in Built Environment · doi:10.3389/fbuil.2022.1007886 · Cited by 65 · see citing works

A review of deep learning methods for pixel-level crack detection

Li, H., Wang, W., Wang, M., Li, L., & Vimlund, V. (2022) · Journal of Traffic and Transportation Engineering (English Edition) · doi:10.1016/j.jtte.2022.11.003 · Cited by 65 · see citing works

Seismic assessment of bridges through structural health monitoring: a state-of-the-art review

Karakostas, C., Quaranta, G., Chatzi, E., Zülfikar, A. C., Çetindemir, O., Roeck, G. D., Döhler, M., Limongelli, M. P., Lombaert, G., Apaydın, N. M., et al. (2023) · Bulletin of Earthquake Engineering · doi:10.1007/s10518-023-01819-3 · Cited by 65 · see citing works

Advancements and emerging trends in integrating machine learning and deep learning for SHM in mechanical and civil engineering: a comprehensive review

Khatir, A., Capozucca, R., Khatir, S., Magagnini, E., Thanh, C. L., & Riahi, M. K. (2025) · Journal of the Brazilian Society of Mechanical Sciences and Engineering · doi:10.1007/s40430-025-05697-5 · Cited by 64 · see citing works

OUR-Net: A Multi-Frequency Network With Octave Max Unpooling and Octave Convolution Residual Block for Pavement Crack Segmentation

Li, P., Wang, M., Fan, Z., Huang, H. P., Zhu, G., & Zhuang, J. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3405995 · Cited by 61 · see citing works

A deep learning-based bridge damage detection and localization method

Sun, H., Song, L., & Yu, Z. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110277 · Cited by 58 · see citing works

Semi-Supervised Detection of Structural Damage Using Variational Autoencoder and a One-Class Support Vector Machine

Pollastro, A., Testa, G., Bilotta, A., & Prevete, R. (2023) · IEEE Access · doi:10.1109/access.2023.3291674 · Cited by 58 · see citing works

On the explainability of convolutional neural networks processing ultrasonic guided waves for damage diagnosis

Lomazzi, L., Fabiano, S., Parziale, M., Giglio, M., & Cadini, F. (2022) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2022.109642 · Cited by 57 · see citing works

Review of advanced techniques for manufacturing biocomposites: non-destructive evaluation and artificial intelligence-assisted modeling

Preethikaharshini, J., Naresh, K., Rajeshkumar, G., Arumugaprabu, V., Khan, M., & Khan, K. A. (2022) · Journal of Materials Science · doi:10.1007/s10853-022-07558-1 · Cited by 57 · see citing works

UAV Obstacle Avoidance Algorithm to Navigate in Dynamic Building Environments

Aldao, E., González-deSantos, L. M., Michinel, H., & González-Jorge, H. (2022) · Drones · doi:10.3390/drones6010016 · Cited by 56 · see citing works

Deep learning enhanced principal component analysis for structural health monitoring

Fernández-Navamuel, A., Magalhães, F., Zamora-Sánchez, D., Omella, Á. J., García-Sánchez, D., & Pardo, D. (2022) · Structural Health Monitoring · doi:10.1177/14759217211041684 · Cited by 56 · see citing works

Towards Automated Inspections of Tunnels: A Review of Optical Inspections and Autonomous Assessment of Concrete Tunnel Linings

Sjölander, A., Belloni, V., Ansell, A., & Nordström, E. (2023) · Sensors · doi:10.3390/s23063189 · Cited by 56 · see citing works

Influence of Smart Sensors on Structural Health Monitoring Systems and Future Asset Management Practices

Preethichandra, D. M. G., Suntharavadivel, T. G., Kalutara, P., Piyathilaka, L., & Izhar, U. (2023) · Sensors · doi:10.3390/s23198279 · Cited by 55 · see citing works

Recent progress and future outlook of digital twins in structural health monitoring of civil infrastructure

Sakr, M., & Sadhu, A. (2024) · Smart Materials and Structures · doi:10.1088/1361-665x/ad2bd7 · Cited by 55 · see citing works

Unsupervised data normalization for continuous dynamic monitoring by an innovative hybrid feature weighting-selection algorithm and natural nearest neighbor searching

Sarmadi, H., Entezami, A., & Magalhães, F. (2023) · Structural Health Monitoring · doi:10.1177/14759217231166116 · Cited by 54 · see citing works

Data Driven Vibration Control: A Review

Yang, W., Li, S., & Luo, X. (2024) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2024.124431 · Cited by 54 · see citing works

Application of region-based video surveillance in smart cities using deep learning

Zahra, A., Ghafoor, M., Munir, K., Ullah, A., & Ul Abideen, Z. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11468-w · Cited by 53 · see citing works

Effect of different imaging modalities on the performance of a CNN: An experimental study on damage segmentation in infrared, visible, and fused images of concrete structures

Pozzer, S., de Souza, M. P. V., Hena, B., Hesam, S., Rezayiye, R. K., Azar, E. R., López, F., & Maldague, X. (2022) · NDT & E International · doi:10.1016/j.ndteint.2022.102709 · Cited by 52 · see citing works

Advances in Wearable Piezoelectric Sensors for Hazardous Workplace Environments

Mokhtari, F., Cheng, Z., Wang, C., & Foroughi, J. (2023) · Global Challenges · doi:10.1002/gch2.202300019 · Cited by 52 · see citing works

Machine learning and deep learning for safety applications: Investigating the intellectual structure and the temporal evolution

Leoni, L., BahooToroody, A., Abaei, M. M., Cantini, A., BahooToroody, F., & Carlo, F. D. (2023) · Safety Science · doi:10.1016/j.ssci.2023.106363 · Cited by 52 · see citing works

From data to insight, enhancing structural health monitoring using physics-informed machine learning and advanced data collection methods

Rizvi, S. H. M., & Abbas, M. (2023) · Engineering Research Express · doi:10.1088/2631-8695/acefae · Cited by 51 · see citing works

Image-Based Structural Health Monitoring: A Systematic Review

Payawal, J. M. G., & Kim, D. K. (2023) · Applied Sciences · doi:10.3390/app13020968 · Cited by 50 · see citing works

2D Digital Image Correlation and Region-Based Convolutional Neural Network in Monitoring and Evaluation of Surface Cracks in Concrete Structural Elements

Słoński, M., & Tekieli, M. (2020) · Materials · doi:10.3390/ma13163527 · Cited by 50 · see citing works

Application of supervised learning to validation of damage detection

Sarmadi, H., & Entezami, A. (2020) · Archive of Applied Mechanics · doi:10.1007/s00419-020-01779-z · Cited by 50 · see citing works

On the application of kernelised Bayesian transfer learning to population-based structural health monitoring

Gardner, P. A., Bull, L. A., Dervilis, N., & Worden, K. (2021) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2021.108519 · Cited by 50 · see citing works

Lightweight Neural Architecture Search for Temporal Convolutional Networks at the Edge

Risso, M., Burrello, A., Conti, F., Lamberti, L., Chen, Y., Benini, L., Macii, E., Poncino, M., & Pagliari, D. J. (2022) · IEEE Transactions on Computers · doi:10.1109/tc.2022.3177955 · Cited by 49 · see citing works

Neural operator for structural simulation and bridge health monitoring

Kaewnuratchadasorn, C., Wang, J., & Kim, C. (2023) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.13105 · Cited by 49 · see citing works

A novel CNN architecture for robust structural damage identification via strain measurements and its validation via full-scale experiments

Duran, B., Emory, D., Azam, S. E., & Linzell, D. G. (2024) · Measurement · doi:10.1016/j.measurement.2024.115393 · Cited by 49 · see citing works

Emerging Trends in the Integration of Smart Sensor Technologies in Structural Health Monitoring: A Contemporary Perspective

Sivasuriyan, A., Vijayan, D. S., Devarajan, P., Stefańska, A., Dixit, S., Podlasek, A., Sitek, W., & Koda, E. (2024) · Sensors · doi:10.3390/s24248161 · Cited by 49 · see citing works

Machine Learning Meets Compressed Sensing in Vibration-Based Monitoring

Zonzini, F., Carbone, A., Romano, F., Zauli, M., & Marchi, L. D. (2022) · Sensors · doi:10.3390/s22062229 · Cited by 48 · see citing works

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

Luleci, F., & Çatbaş, F. N. (2023) · AI in Civil Engineering · doi:10.1007/s43503-023-00017-z · Cited by 48 · see citing works

A transformer and self-cascade operation-based architecture for segmenting high-resolution bridge cracks

Chu, H.-H., Deng, L., Yuan, H., Long, L., & Guo, J. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.105194 · Cited by 48 · see citing works

1D-TICapsNet: An audio signal processing algorithm for bolt early looseness detection

Wang, F., & Song, G. (2020) · Structural Health Monitoring · doi:10.1177/1475921720976989 · Cited by 48 · see citing works

Bias-variance tradeoff in machine learning: Theoretical formulation and implications to structural engineering applications

Guan, X., & Burton, H. (2022) · Structures · doi:10.1016/j.istruc.2022.10.004 · Cited by 47 · see citing works

Robust crack detection in masonry structures with Transformers

Shamsabadi, E. A., Xu, C., & Dias‐da‐Costa, D. (2022) · Measurement · doi:10.1016/j.measurement.2022.111590 · Cited by 47 · see citing works

Optimal tuning of three deep learning methods with signal processing and anomaly detection for multi-class damage detection of a large-scale bridge

Doroudi, R., Lavassani, S. H. H., & Shahrouzi, M. (2024) · Structural Health Monitoring · doi:10.1177/14759217231216694 · Cited by 47 · see citing works

Interpretable Machine Learning in Damage Detection Using Shapley Additive Explanations

Movsessian, A., Cava, D. G., & Tcherniak, D. (2021) · ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B Mechanical Engineering · doi:10.1115/1.4053304 · Cited by 46 · see citing works

Artificial Intelligence-Based Bolt Loosening Diagnosis Using Deep Learning Algorithms for Laser Ultrasonic Wave Propagation Data

Tran, D. Q., Kim, J., Tola, K. D., Kim, W., & Park, S. (2020) · Sensors · doi:10.3390/s20185329 · Cited by 46 · see citing works

Investigation of Temperature Effects into Long-Span Bridges via Hybrid Sensing and Supervised Regression Models

Behkamal, B., Entezami, A., Michele, C. D., & Arslan, A. N. (2023) · Remote Sensing · doi:10.3390/rs15143503 · Cited by 45 · see citing works

Revolutionizing Structural Engineering: Applications of Machine Learning for Enhanced Performance and Safety

Chitkeshwar, A. K. (2024) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-024-10117-3 · Cited by 45 · see citing works

Deep learning-based structural health monitoring

Cha, Y., Ali, R., Lewis, J., & Büyüköztürk, O. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105328 · Cited by 464 · see citing works

Deep Learning Techniques for Damage Detection in Structural Health Monitoring: A Review

Fidma, M. A., Schmidt, F., & Bercher, J. (2025) · Structural Engineering International · doi:10.1080/10168664.2025.2584752 · Cited by 6 · see citing works

Deep Learning for Structural Health Monitoring: Data, Algorithms, Applications, Challenges, and Trends

Jia, J., & Li, Y. (2023) · Sensors · doi:10.3390/s23218824 · Cited by 148 · see citing works

Vibration-based structural health monitoring via deep learning techniques: A review

Wang, H., Wang, B., Jing, Y., & Wang, P. (2023) · International Conference on Cyber-Physical Social Intelligence (ICCSI) · doi:10.1109/iccsi58851.2023.10303900 · Cited by 3 · see citing works

Deep Learning Methods for Vibration-Based Structural Health Monitoring: A Review

Wang, H., Wang, B., & Cui, C. (2023) · Iranian Journal of Science and Technology Transactions of Civil Engineering · doi:10.1007/s40996-023-01287-4 · Cited by 6 · see citing works