Forsberg, R. & Björnstig, U. One hundred years of railway disasters and recent trends. Prehosp. Disaster Med. 26, 367–373 (2011).
Liu, X., Saat, M. R. & Barkan, C. P. Analysis of causes of major train derailment and their effect on accident rates. Transp. Res. Rec. 2289, 154–163 (2012).
Tracy, A. & Reznik, T. Broken rails are leading cause of train derailments. Sci Am (2015).
Chenariyan Nakhaee, M., Hiemstra, D., Stoelinga, M. & Noort, M. v. The recent applications of machine learning in rail track maintenance: A survey. In International Conference on Reliability, Safety, and Security of Railway Systems. Springer (2019).
Gharehbaghi, V. R. et al. A novel approach for deterioration and damage identification in Building structures based on Stockwell-Transform and deep convolutional neural network. J. Struct. Integr. Mainten. 7, 136–150 (2022).
Moomen, M. & Siddiqui, C. Probabilistic deterioration modeling of Bridge component condition with random effects. J. Struct. Integr. Mainten. 7, 151–160 (2022).
Eljufout, T., Abu Shaqra, M., Jamous, Q., Salameh, R. & Jamous, Z. Structural assessment of the historic ten arches Bridge in Jordan. J. Struct. Integr. Mainten. 7, 168–176 (2022).
Colombani, I. A. & Andrawes, B. A study of multi-target image-based displacement measurement approach for field testing of bridges. J. Struct. Integr. Mainten. 7, 207–216 (2022).
Sharma, S., Dangi, S. K., Bairwa, S. K. & Sen, S. Comparative study on sensitivity of acceleration and strain responses for Bridge health monitoring. J. Struct. Integr. Mainten. 7, 238–251 (2022).
Jeon, G., Kim, S., Ahn, S., Kim, H. & Yoon, H. Vision-based automatic cable displacement measurement using Cable‐ROI net and Uni‐KLT. Struct. Control Health Monit. 29, e2977 (2022).
Lee, Y., Lee, G., Moon, D. S. & Yoon, H. Vision-based displacement measurement using a camera mounted on a structure with stationary background targets outside the structure. Struct. Control Health Monit. 29, e3095 (2022).
Bhange, P. et al. Real-Time fatigue crack growth rate Estimation methodology for structural health monitoring of ships. IEEE Sens. J. 22, 19729–19738 (2022).
Cao, X., Sheng, J., Jiang, C., Yuan, D. & Zhang, H. Concrete dam deformation prediction model considering the time delay of monitoring variables. Sci. Rep. 15, 8458 (2025).
Jia, X. et al. A structural health monitoring data reconstruction method based on VMD and SSA-optimized GRU model. Sci. Rep. 15, 3513 (2025).
Moreh, F., Hasan, Y., Rizvi, Z. H., Tomforde, S. & Wuttke, F. Hybrid neural network method for damage localization in structural health monitoring. Sci. Rep. 15, 7991 (2025).
Marino, F., Distante, A., Mazzeo, P. L. & Stella, E. A real-time visual inspection system for railway maintenance: Automatic hexagonal-headed bolts detection. IEEE Trans. Syst. Man. Cybern. Part. C (Appl. Rev.). 37, 418–428 (2007).
Hodge, V. J., O’Keefe, S. & Weeks, M. Moulds. Wireless sensor networks for condition monitoring in the railway industry: A survey. IEEE Trans. Intell. Transp. Syst. 16, 1088–1106 (2015).
Mukojima, H. et al. Moving camera background-subtraction for obstacle detection on railway tracks. In IEEE international conference on image processing (ICIP), IEEE, 2016). (2016).
Flammini, F., Pragliola, C. & Smarra, G. Railway infrastructure monitoring by drones (– 2016 International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles & International Transportation Electrification Conference (ESARS-ITEC), (2016).
Núñez, A., Hendriks, J., Li, Z., De Schutter, B. & Dollevoet, R. Facilitating maintenance decisions on the Dutch railways using big data: The ABA case study. In IEEE international conference on big data (big data), IEEE, 2014). (2014).
Jamshidi, A. et al. A big data analysis approach for rail failure risk assessment. Risk Anal. 37, 1495–1507 (2017).
Santur, Y., Karaköse, M. & Akin, E. A new rail inspection method based on deep learning using laser cameras. In International Artificial Intelligence and Data Processing Symposium (IDAP), IEEE, 2017. (2017).
Lee, J. S., Park, J. & Ryu, Y. Semantic segmentation of Bridge components based on hierarchical point cloud model. Autom. Constr. 130, 103847 (2021).
Ghiasi, R., Khan, M. A., Sorrentino, D., Diaine, C. & Malekjafarian, A. An unsupervised anomaly detection framework for onboard monitoring of railway track geometrical defects using one-class support vector machine. Eng. Appl. Artif. Intell. 133, 108167 (2024).
Ni, X., Fieguth, P. W., Ma, Z., Shi, B. & Liu, H. Defect detection on multi-type rail surfaces via IoU decoupling and multi-information alignment. Adv. Eng. Inform. 62, 102717 (2024).
Bensalah, M., Elouadi, A. & Mharzi, H. Overview: The opportunity of BIM in railway. Smart Sustain. Built Environment (2019).
Neves, J., Sampaio, Z. & Vilela, M. A case study of BIM implementation in rail track rehabilitation. Infrastructures 4, 8 (2019).
Sresakoolchai, J. & Kaewunruen, S. Railway infrastructure maintenance efficiency improvement using deep reinforcement learning integrated with digital twin based on track geometry and component defects. Sci. Rep. 13, 2439 (2023).
Maturana, D. & Scherer, S. Voxnet: A 3d convolutional neural network for real-time object recognition. In IEEE/RSJ international conference on intelligent robots and systems (IROS), IEEE, 2015. (2015).
Wu, B., Wan, A., Yue, X. & Keutzer, K. Squeezeseg: Convolutional neural nets with recurrent crf for real-time road-object segmentation from 3d lidar point cloud. In IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2018. (2018).
Wu, B., Zhou, X., Zhao, S., Yue, X. & Keutzer, K. Squeezesegv2: Improved model structure and unsupervised domain adaptation for road-object segmentation from a lidar point cloud. In International Conference on Robotics and Automation (ICRA), IEEE, 2019. (2019).
Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (2017).
Qi, C. R., Yi, L., Su, H., Guibas, L. J. & Pointnet Deep hierarchical feature learning on point sets in a metric space. Adv. Neural Inform. Process. Syst. 30 (2017).
Torr, P. H., Zisserman, A. & MLESAC A new robust estimator with application to estimating image geometry. Comput. Vis. Image Underst. 78, 138–156 (2000).
Fischler, M. A. & Bolles, R. C. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM. 24, 381–395 (1981).
Duda, R. O. & Hart, P. E. Use of the Hough transformation to detect lines and curves in pictures. Commun. ACM. 15, 11–15 (1972).
Arthur, D. & Vassilvitskii, S. k-means: The advantages of careful seeding. (2006).
