STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Structural Engineering and Management Original Research

Role of Artificial Intelligence in Structural Health Monitoring-A Brief Evaluation

  1. Suman Pandey Department of Civil Engineering, Techno India University
  2. Madhurima Dolai Department of Civil Engineering, Techno India University
  3. Swapnanil De Department of Civil Engineering, Techno India University

Abstract

Artificial intelligence (AI) refers to the capacity of a machine or a computer to ‘think’ or reason in the way a human would, utilizing experience, learned facts, and flexible rules to solve problems that may not fit the standard outlines for a normal algorithm. From this follows the utilization of AI in various sectors, such as the information technology (IT) industry, media, healthcare and medicine, logistics, environmental sustainability, finance, business, and even judicial systems. Nowadays, the application of AI in the segment of engineering and materials science is also growing fast. Under this sector, AI can be implemented to replace trial-and-error lab work by predicting future models for the computational material discovery subsegment. In another subcategory of this segment, namely structural health and digital twins, AI can be applied for computer vision inspections for identifying cracks, corrosion, spalling, etc. Also, deep learning models can be implemented to analyze real-time data and to predict microscopic cracks, which is a next-to-impossible task for any human being. In this study, we explore the utilization of AI in various fields of civil engineering with a focus on the field of structural health monitoring. In this article, care has been taken to thoroughly investigate all the research done in the structural health monitoring (SHM) sector with the application of AI and its implications. Main emphasis has been given to the detection of different types of damage in a structural body using AI. Further, the research investigation has been narrowed down to the identification of cracks using this latest technology. Concrete conclusions have been drawn in the last segment, which highlights today’s challenges and their limitations.

Keywords

References (35)

  1. (2026). History of artificial intelligence. [Online]. Wikipedia. Available from: https://en.wikipedia.org/wiki/History_of_artificial_intelligence
  2. Kao Y, Chen MH, Huang YT. A Hybrid Algorithm Based on ACO and PSO for Capacitated Vehicle Routing Problems. Mathematical Problems in Engineering. 2012;2012(1). doi:10.1155/2012/726564
  3. Tricco G, Almenar R, Ayers K, Ben Moussa R, Graham T, Iyiola S, et al. The protection of AI-based space systems from a data-driven governance perspective. Acta Astronautica. 2025;234:73-86. doi:10.1016/j.actaastro.2025.04.063
  4. Bajwa J, Munir U, Nori A, Williams B. Artificial intelligence in healthcare: transforming the practice of medicine. Future Healthcare Journal. 2021;8(2):e188-e194. doi:10.7861/fhj.2021-0095
  5. Pannu A. Artificial intelligence and its application in different areas. Int J Eng Innov Technol. 2015 Apr;4(10):79-84.
  6. Cao SS, Jiang W, Lei LG, Zhou QC. Applied AI for finance and accounting: Alternative data and opportunities. Pacific-Basin Finance Journal. 2024;84:102307. doi:10.1016/j.pacfin.2024.102307
  7. Saheb T, Sidaoui M, Schmarzo B. Convergence of artificial intelligence with social media: A bibliometric & qualitative analysis. Telematics and Informatics Reports. 2024;14:100146. doi:10.1016/j.teler.2024.100146
  8. Osimen GU, Newo O, Fulani OM. Artificial intelligence and arms control in modern warfare. Cogent Social Sciences. 2024;10(1). doi:10.1080/23311886.2024.2407514
  9. Gorlamudiveti LP, Sethu SG. Role of Artificial Intelligence in the Indian Judicial System. 2023 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE). 2023:305-310. doi:10.1109/iccike58312.2023.10131795
  10. Sharma M. India's Courts and Artificial Intelligence: A Future Outlook. LeXonomica. 2023;15(1). doi:10.18690/lexonomica.15.1.99-120.2023
  11. Gocen A, Aydemir F. Artificial Intelligence in Education and Schools. Research on Education and Media. 2020;12(1):13-21. doi:10.2478/rem-2020-0003
  12. Scaife AD. Improve predictive maintenance through the application of artificial intelligence: A systematic review. Results in Engineering. 2024;21:101645. doi:10.1016/j.rineng.2023.101645
  13. Li X, Jiang H. Artificial intelligence technology and engineering applications. ACES J. 2017 May;32(5):381-8.
  14. Vincent NC, Bhakar RR, Nadarajan SR, Syamala A, Varghese J. Impact of artificial intelligence in the aviation and space sector. In: Artificial Intelligence. 2021. ISBN: 9781003095910.
  15. Manzoor B, Othman I, Durdyev S, Ismail S, Wahab M. Influence of Artificial Intelligence in Civil Engineering toward Sustainable Development—A Systematic Literature Review. Applied System Innovation. 2021;4(3):52. doi:10.3390/asi4030052
  16. Thakur N, Mishra VP, Islam SMN, Neyaz Z, Jain R, Roy S. IoT based Air Pollution Monitoring System. 2023 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE). 2023:1-6. doi:10.1109/iccike58312.2023.10131745
  17. Yang L, Huang E. Structural Health Monitoring Data Analysis Using Deep Learning Techniques. Proceedings of the 2024 8th International Conference on Electronic Information Technology and Computer Engineering. 2024:913-927. doi:10.1145/3711129.3711286
  18. Smarsly K, Hartmann D. Artificial intelligence in structural health monitoring. In: Proceedings of the 3rd European Workshop on Structural Health Monitoring; 2006; Granada, Spain. p. 429-36.
  19. Smarsly K, Lehner K, Hartmann D. Structural Health Monitoring based on Artificial Intelligence Techniques. Computing in Civil Engineering (2007). 2007:111-118. doi:10.1061/40937(261)14
  20. Salehi H, Burgueño R. Emerging artificial intelligence methods in structural engineering. Engineering Structures. 2018;171:170-189. doi:10.1016/j.engstruct.2018.05.084
  21. Harle SM. Advancements and challenges in the application of artificial intelligence in civil engineering: a comprehensive review. Asian Journal of Civil Engineering. 2023;25(1):1061-1078. doi:10.1007/s42107-023-00760-9
  22. Huang Y, Li J, Fu J. Review on Application of Artificial Intelligence in Civil Engineering. Computer Modeling in Engineering & Sciences. 2019;121(3):845-875. doi:10.32604/cmes.2019.07653
  23. Huang Y, Ludwig SA, Deng F. Sensor optimization using a genetic algorithm for structural health monitoring in harsh environments. Journal of Civil Structural Health Monitoring. 2016;6(3):509-519. doi:10.1007/s13349-016-0170-y
  24. Rao ARM, Anandakumar G. Optimal placement of sensors for structural system identification and health monitoring using a hybrid swarm intelligence technique. Smart Materials and Structures. 2007;16(6):2658-2672. doi:10.1088/0964-1726/16/6/071
  25. Waqas M, Jan L, Zafar MH, Hassan SR, Asif R. A Sensor Placement Approach Using Multi-Objective Hypergraph Particle Swarm Optimization to Improve Effectiveness of Structural Health Monitoring Systems. Sensors. 2024;24(5):1423. doi:10.3390/s24051423
  26. Achouri F, Khatir A, Smahi Z, Capozucca R, Ouled Brahim A. Structural health monitoring of beam model based on swarm intelligence-based algorithms and neural networks employing FRF. Journal of the Brazilian Society of Mechanical Sciences and Engineering. 2023;45(12). doi:10.1007/s40430-023-04525-y
  27. Neves AC, González I, Leander J, Karoumi R. Structural health monitoring of bridges: a model-free ANN-based approach to damage detection. Journal of Civil Structural Health Monitoring. 2017;7(5):689-702. doi:10.1007/s13349-017-0252-5
  28. Cremona C, Santos J. Structural Health Monitoring as a Big-Data Problem. Structural Engineering International. 2018;28(3):243-254. doi:10.1080/10168664.2018.1461536
  29. Malekloo A, Ozer E, AlHamaydeh M, Girolami M. Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights. Structural Health Monitoring. 2021;21(4):1906-1955. doi:10.1177/14759217211036880
  30. Farrar CR, Worden K. An introduction to structural health monitoring. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2006;365(1851):303-315. doi:10.1098/rsta.2006.1928
  31. Moore EZ, Nichols JM, Murphy KD. Model-based SHM: Demonstration of identification of a crack in a thin plate using free vibration data. Mechanical Systems and Signal Processing. 2012;29:284-295. doi:10.1016/j.ymssp.2011.09.022
  32. Spencer BF, Sim SH, Kim RE, Yoon H. Advances in artificial intelligence for structural health monitoring: A comprehensive review. KSCE Journal of Civil Engineering. 2025;29(3):100203. doi:10.1016/j.kscej.2025.100203
  33. Bai Y, Sezen H, Yilmaz A. DETECTING CRACKS AND SPALLING AUTOMATICALLY IN EXTREME EVENTS BY END-TO-END DEEP LEARNING FRAMEWORKS. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2021;V-2-2021:161-168. doi:10.5194/isprs-annals-v-2-2021-161-2021
  34. Farrar CR, Worden K. Structural health monitoring: A machine learning perspective. Chichester (UK); Hoboken (NJ): John Wiley & Sons; 2013.
  35. Tibaduiza Burgos DA, Gomez Vargas RC, Pedraza C, Agis D, Pozo F. Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of Data-Driven Applications. Sensors. 2020;20(3):733. doi:10.3390/s20030733
Support