Energy Catalyst 3103-9952 Caravel Press 10.61552/EC.2025.010 2025.010 Advances, Applications, and Future Directions of Structural Health Monitoring in Civil Infrastructure: A Comprehensive Review https://orcid.org/0000-0003-0374-4526 RiffatJames ceo@wsset.org https://orcid.org/0000-0003-1627-1991 SamaeiSeyed Reza https://orcid.org/0000-0001-5027-9458 PastakkayaBilsay World Society of Sustainable Energy Technologies, Nottingham, United Kingdom Department of Marine industries, Science and Research Branch, Islamic Azad University, Tehran, Iran Department of Machine & Metal Technology, Orhangazi YAC Vocational College, Bursa Uludag University, Bursa, Turkey 03122025 1 127 140 15 August 2025 10 October 2025 3 December 2025 © 2025 The Author(s). Published by Caravel Press. 2025 This is an open access article under the CC BY 4.0 licence.

Structural Health Monitoring (SHM) has emerged as a core discipline in modern civil engineering, providing a systematic means to safeguard the safety, durability, and long-term functionality of infrastructure. This review synthesizes current advancements in SHM for civil infrastructure, beginning with its foundational concepts and classification schemes, and progressing through key sensing technologies and their applications across the built environment. From bridges, tunnels, and high-rise buildings to culturally significant heritage structures, SHM has proven effective in detecting earlystage damage, optimizing maintenance schedules, and supporting rapid response following natural disasters or other disruptive events. Despite these capabilities, widespread adoption remains constrained by persistent challenges. These include sensor calibration drift over extended service periods, environmental and operational variability that obscures damage signals, the overwhelming volume of monitoring data requiring timely interpretation, and the absence of universal data and system standards. In response, the research community is advancing solutions such as real-time digital twins, machine learning–driven analytics, autonomous and self-powered sensing devices, and seamless integration with Building Information Modelling (BIM) platforms. Looking ahead, the continued evolution of SHM lies in enhancing interoperability, automating data interpretation, and ensuring the reliability of monitoring systems over the entire service life of assets. Achieving these objectives will position SHM as not merely a diagnostic tool, but as an essential component of predictive maintenance, smart asset management, and resilience planning within an increasingly complex and dynamic built environment. This review highlights three dominant trends across recent SHM research: the rapid shift toward data-driven diagnostics, the emergence of digital twin–integrated monitoring frameworks, and the increasing adoption of fibre-optic and wireless sensing technologies in large-scale deployments. Collectively, these trends point to a sector moving steadily toward autonomous, real-time, and lifecycle-oriented structural management.

 Civil Engineering Structural Health Monitoring Civil Infrastructure Sensing Technologies Damage Detection Wireless Sensor Networks Digital Twins Artificial Intelligence Infrastructure Resilience M. Ramesh, M. Tamil Selvan, and A. Saravanakumar, “Evolution and recent advancements of composite materials in structural applications,” in Applications of Composite Materials in Engineering. Elsevier, 2025, pp. 97–117, doi: 10.1016/B978-0-443-13989-5.00004-8. 10.1016/B978-0-443-13989-5.00004-8 G. Wang and J. Ke, “Literature review on the structural health monitoring (SHM) of sustainable civil infrastructure: An analysis of influencing factors in the implementation,” Buildings, vol. 14, p. 402, 2024, doi: 10.3390/buildings14020402. 10.3390/buildings14020402 O. S. Sonbul and M. Rashid, “Algorithms and techniques for the structural health monitoring of bridges: Systematic literature review,” Sensors, vol. 23, p. 4230, 2023, doi: 10.3390/s23094230. 10.3390/s23094230 J. Riffat, H. Ahadpour Doudran, and S. R. Samaei, “AI- enhanced damage detection in jack-up rig legs using an improved modal strain energy index: A numerical, experimental, and digital twin-based approach,” Green Technology and Innovation, 2025, doi: 10.36922/GTI025340014. 10.36922/GTI025340014 J. Riffat, H. Ahadpour Doudran, and S. R. Samaei, “AI- driven digital twin for uncertainty-aware structural health monitoring of offshore wind turbines considering biofouling effects and reliability prediction,” Green Technology and Innovation, vol. 1, no. 2, p. 025330013, 2025, doi: 10.36922/GTI025330013. 10.36922/GTI025330013 S. R. Samaei and J. Riffat, “Intelligent structural health monitoring of jack-up platform legs using high-density sensor networks, real-time digital twins, and machine learning-based damage detection,” Future Cities and Environment, vol. 11, 2025, doi: 10.70917/fce-2025-031. 10.70917/fce-2025-031 C. Boller, “Structural Health Monitoring—An Introduction and Definitions,” in Encyclopedia of Structural Health Monitoring, C. Boller, F. Chang, and Y. Fujino, Eds. Wiley, 2008, doi: 10.1002/9780470061626.shm204. 10.1002/9780470061626.shm204 D. A. Tibaduiza Burgos, R. C. Gomez Vargas, C. Pedraza, D. Agis, and F. Pozo, “Damage identification in structural health monitoring: A brief review from its implementation to the use of data-driven applications,” Sensors, vol. 20, p. 733, 2020, doi: 10.3390/s20030733. 10.3390/s20030733 A. Mardanshahi, A. Sreekumar, X. Yang, S. K. Barman, and D. Chronopoulos, “Sensing techniques for structural health monitoring: A state-of-the-art review on performance criteria and new- generation technologies,” Sensors, vol. 25, p. 1424, 2025, doi: 10.3390/s25051424. 10.3390/s25051424 Y. Chamaine, Integrating Structural Health Monitoring for Safeguarding Infrastructure Resilience and Durability, 2023, doi: 10.13140/RG.2.2.34068.68489. 10.13140/RG.2.2.34068.68489 S. R. Samaei, M. Ghodsi Hassanabad, M. Asadian Ghahfarrokhi, and M. J. Ketabdari, “Numerical and experimental investigation of damage in environmentally sensitive civil structures using modal strain energy (case study: LPG wharf),” Int. J. Environ. Sci. Technol., vol. 18, no. 6, pp. 1939–1952, 2021, doi: 10.1007/s13762-021-03321-2. 10.1007/s13762-021-03321-2 R. Perera, A. Pérez, M. García-Diéguez, and J. Zapico- Valle, “Active wireless system for structural health monitoring applications,” Sensors, vol. 17, p. 2880, 2017, doi: 10.3390/s17122880. 10.3390/s17122880 S. Cho, J.-W. Park, and S.-H. Sim, “Decentralized system identification using stochastic subspace identification for wireless sensor networks,” Sensors, vol. 15, pp. 8131–8145, 2015, doi: 10.3390/s150408131. 10.3390/s150408131 S. Hassani and U. Dackermann, “A systematic review of advanced sensor technologies for non- destructive testing and structural health monitoring,” Sensors, vol. 23, p. 2204, 2023, doi: 10.3390/s23042204. 10.3390/s23042204 F. Kosova, Ö. Altay, and H. Ö. Ünver, “Structural health monitoring in aviation: A comprehensive review and future directions for machine learning,” Nondestructive Testing and Evaluation, vol. 40, pp. 1–60, 2025, doi: 10.1080/10589759.2024.2350575. 10.1080/10589759.2024.2350575 R. O. Ogunleye, S. Rusnáková, J. Javořík, M. Žaludek, and B. Kotlánová, “Advanced sensors and sensing systems for structural health monitoring in aerospace composites,” Advanced Engineering Materials, vol. 26, p. 2401745, 2024, doi: 10.1002/adem.202401745. 10.1002/adem.202401745 T. M. Fayyad, S. Taylor, K. Feng, and F. K. P. Hui, “A scientometric analysis of drone-based structural health monitoring and new technologies,” Advances in Structural Engineering, vol. 28, pp. 122–144, 2025, doi: 10.1177/13694332241255734. 10.1177/13694332241255734 Z. Deng, M. Huang, N. Wan, and J. Zhang, “The current development of structural health monitoring for bridges: A review,” Buildings, vol. 13, p. 1360, 2023, doi: 10.3390/buildings13061360. 10.3390/buildings13061360 European Commission Joint Research Centre, Indirect Structural Health Monitoring (iSHM) of Transport Infrastructure in the Digital Age: MITICA workshop report. LU: Publications Office, 2023. M. Mishra, P. B. Lourenço, and G. V. Ramana, “Structural health monitoring of civil engineering structures by using the internet of things: A review,” Journal of Building Engineering, vol. 48, p. 103954, 2022, doi: 10.1016/j.jobe.2021.103954. 10.1016/j.jobe.2021.103954 Y. Zhang, G. Nie, and D. Wang, “Structural health monitoring based on three-dimensional point cloud technology: A systematic review,” Results in Engineering, vol. 27, p. 106552, 2025, doi: 10.1016/j.rineng.2025.106552. 10.1016/j.rineng.2025.106552 V. Plevris and G. Papazafeiropoulos, “AI in structural health monitoring for infrastructure maintenance and safety,” Infrastructures, vol. 9, p. 225, 2024, doi: 10.3390/infrastructures9120225. 10.3390/infrastructures9120225 M. Rossi and D. Bournas, “Structural health monitoring and management of cultural heritage structures: A state-of-the-art review,” Applied Sciences, vol. 13, p. 6450, 2023, doi: 10.3390/app13116450. 10.3390/app13116450 Y. Yang, T. Chen, W. Lin, M. Jing, and W. Xu, “Research progress on calibration of bridge structural health monitoring sensing system,” Advanced Bridge Engineering, vol. 5, p. 32, 2024, doi: 10.1186/s43251-024-00143-3. 10.1186/s43251-024-00143-3 A. Keshmiry, S. Hassani, M. Mousavi, and U. Dackermann, “Effects of environmental and operational conditions on structural health monitoring and non-destructive testing: A systematic review,” Buildings, vol. 13, p. 918, 2023, doi: 10.3390/buildings13040918. 10.3390/buildings13040918 H. Pezeshki, H. Adeli, D. Pavlou, and S. C. Siriwardane, “State of the art in structural health monitoring of offshore and marine structures,” Proc. ICE – Maritime Engineering, vol. 176, pp. 89– 108, 2023, doi: 10.1680/jmaen.2022.027. 10.1680/jmaen.2022.027 J. Bardiani, C. Mazzolatti, A. Manes, and C. Sbarufatti, “A hybrid approach to enhance decision-making in marine structures: Combining sensor data with human perception,” Results in Engineering, vol. 27, p. 105670, 2025, doi: 10.1016/j.rineng.2025.105670. 10.1016/j.rineng.2025.105670 F. Lorenzoni, F. Casarin, M. Caldon, K. Islami, and C. Modena, “Uncertainty quantification in structural health monitoring: Applications on cultural heritage buildings,” Mechanical Systems and Signal Processing, vol. 66–67, pp. 268–281, 2016, doi: 10.1016/j.ymssp.2015.04.032. 10.1016/j.ymssp.2015.04.032 E. García-Macías, I. A. Hernández-González, and F. Ubertini, “Incorporating digital twins and artificial intelligence for next-generation SHM software,” in IOMAC 2024, Springer, 2024, pp. 435–447, doi: 10.1007/978-3-031-61421-7_43. 10.1007/978-3-031-61421-7_43 H. Hasani, F. Freddi, and R. Piazza, “AI-driven automated and integrated structural health monitoring under environmental and operational variations,” Automation in Construction, vol. 176, p. 106222, 2025, doi: 10.1016/j.autcon.2025.106222. 10.1016/j.autcon.2025.106222 D. Jiao, S. Gu, L. Cheng, S. Li, and C. Liu, “Flexible, self- healing and portable supramolecular visualization smart sensors for monitoring and quantifying structural damage,” Materials Horizons, vol. 12, pp. 190–204, 2025, doi: 10.1039/D4MH01233J. 10.1039/D4MH01233J C. Gragnaniello, G. Mariniello, T. Pastore, and D. Asprone, “BIM-based design and setup of structural health monitoring systems,” Automation in Construction, vol. 158, p. 105245, 2024, doi: 10.1016/j.autcon.2023.105245. 10.1016/j.autcon.2023.105245 J. Juarez-Quispe et al., “Advancing sustainable infrastructure management: Insights from system dynamics,” Buildings, vol. 15, p. 210, 2025, doi: 10.3390/buildings15020210. 10.3390/buildings15020210