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

Year : 2026 | Volume : 13 | 01 | Page :
    By

    Madhurima Dolai,

  • Swapnanil De,

  • Suman Pandey,

Abstract

Artificial Intelligence refers to the capacity of a machine or a computer to ‘think’ or reason in the way a human would, utilising experience, learned facts and flexible rules to solve problems which may not fit the standard outlines for a normal algorithm. From this follows the utilisation of AI in various sectors such as the IT industry, media, healthcare and medicine, logistics, environmental sustainability, finance, Business and even judicial systems. Now a days application of AI in the segment of Engineering and Material Science is also growing fast. Under this sector AI can be implemented for replacing trial-and-error lab work by predicting future models for Computational material discovery sub-segment. In another sub-category of this segment that is 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 analyse real time data and to predict microscopic cracks, which is next to impossible task for any human being. In this particular study we explore the utilisation of Artificial Technology in the various field 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 works done in SHM sector with application of AI and its implications. Main emphasis has been given on detection of different types of damages in structural body using artificial intelligence. Further, research investigation has been narrow down to identification of cracks using this latest technology. Concrete conclusions have been drawn in the last segment which highlights today’s challenges and its limitations.

Keywords: Algorithm, Artificial intelligence, Crack detection, Deep learning, Machine learning, Structural Health Monitoring

How to cite this article:
Madhurima Dolai, Swapnanil De, Suman Pandey. Role of Artificial Intelligence in Structural Health Monitoring-A Brief Evaluation. Journal of Structural Engineering and Management. 2026; 13(01):-.
How to cite this URL:
Madhurima Dolai, Swapnanil De, Suman Pandey. Role of Artificial Intelligence in Structural Health Monitoring-A Brief Evaluation. Journal of Structural Engineering and Management. 2026; 13(01):-. Available from: https://journals.stmjournals.com/josem/article=2026/view=236416


References

  1. History of Artificial intelligence. https://en.wikipedia.org/wiki/History_of_artificial_intelligence
  2. Kao Y., Chen M. H., and Huang Y. T., A hybrid algorithm based on ACO and PSO for capacitated vehicle routing problems, Mathematical Problems in Engineering. (2012) 2012, 726564.
  3. Giovanni Tricco, Roser Almenar,, Kaili Ayers, Rihab Ben Moussa, Thomas Graham,Simisola Iyiola,, Sanghoon Lee, Terezie Němcová,Asiimwe Joshua Opota Tushar Sharma,Raelee and Toh Jieyu Yuan, The protection of AI-based space systems from a data-driven governance perspective- Acta Astronautica Volume 234, September 2025, Pages 73-86, https://doi.org/10.1016/j.actaastro.2025.04.063
  4. Junaid Bajwa A, Usman Munir, Aditya Nori and Bryan Williams, Artificial intelligence in healthcare: transforming the practice of medicine, Future Health J. 2021 Jul;8(2):e188–e194. doi: 10.7861/fhj.2021-0095
  5. Avneet Pannu, Artificial Intelligence and its Application in Different Areas, International Journal of Engineering and Innovative Technology (IJEIT) Volume 4, Issue 10, April 2015,ISSN: 2277-3754.
  6. Sean Shun Cao, Wei Jiang, Lijun (Gillian) Lei,and Qing (Clara) Zhou, Applied AI for finance and accounting: Alternative data and opportunities, Pacific-Basin Finance JournalVolume 84, April 2024, 102307, https://doi.org/10.1016/j.pacfin.2024.102307.
  7. Tahereh Saheb, MouwafacSidaoui and Bill Schmarzo, Convergence of artificial intelligence with social media: A bibliometric & qualitative analysis, Telematics and Informatics ReportsVolume 14, June 2024, 100146, https://doi.org/10.1016/j.teler.2024.100146
  8. Goddy Uwa Osimen,OluwamurewaNewoand Oluwakemi Morola Fulani, Artificial intelligence and arms control in modern warfare, Cogent Social Sciences Volume 10, 2024 – Issue 1, https://doi.org/10.1080/23311886.2024.2407514
  9. Lakshmi Priya Gorlamudivetim and Sagee Geetha Sethu, Role of Artificial Intelligence in the Indian Judicial System, International Conference on Computational Intelligence and Knowledge Economy (ICCIKE)-2023, IEEE publication,pp 305-310.
  10. Mohit Sharma, India’s Courts and Artificial Intelligence: A Future Outlook January 2023Lexonomica 15(1) DOI:10.18690/lexonomica.15.1.99-120.2023.
  11. Ahmet Gocen and Fatih Aydemir, Artificial Intelligence in Education and Schools, Research on Education and Media Volume 12 (2020): Issue 1 (June 2020), pp 13-21, DOI: https://doi.org/10.2478/rem-2020-0003.
  12. Anthony D. Scaife, Improve predictive maintenance through the application of artificial intelligence: A systematic review, Results in Engineering Volume 21, March 2024, 101645, https://doi.org/10.1016/j.rineng.2023.101645.
  13. Xiuquan Li and Hongling Jiang, Artificial Intelligence Technology and Engineering Applications, Applied Computational Electromagnetics Society Journal (ACES), 2017: Vol 32(5).
  14. Nelvin Chummar Vincent, Roshan Rajesh Bhakar, Sarath Raj Nadarajan, Assari Syamala and Jerrin Varghese, Impact of Artificial Intelligence in the Aviation and Space Sector, Artificial Intelligence, 2021, eBook ISBN9781003095910.
  15. Bilal Manzoor,Idris Othman,Serdar Durdyev,Syuhaida Ismail and Mohammad Hussaini Wahab, Influence of Artificial Intelligence in Civil Engineering toward Sustainable Development—A Systematic Literature Review, Appl. Syst. Innov. 2021, 4(3), 52; https://doi.org/10.3390/asi4030052.
  16. Narina Thakur, Ved P Mishra, Sardar M N Islam, Zarqua Neyaz, Rachna Jain and Sidarth Roy, IoT based Air Pollution Monitoring System, IEEE Xplore, March 2023, 10.1109/ICCIKE58312.2023.10131745.
  17. Linfei Yang and Enzo Huang, Structural Health Monitoring Data Analysis Using Deep Learning Techniques, EITCE ’24: Proceedings of the 2024 8th International Conference on Electronic Information Technology and Computer Engineering Pages 913 – 927 https://doi.org/10.1145/3711129.3711286,
  18. Smarsly and D. Hartmann, Artificial intelligence in structural health monitoring,Institute for Computational Engineering, Ruhr-University Bochum, Germany .
  19. Kay Smarsly, Karlheinz Lehner, and Dietrich Hartmann, Structural Health Monitoring based on Artificial Intelligence Techniques, Computing in Civil Engineering (2007), 111-118https://doi.org/10.1061/40937(261)14
  20. Hadi Salehi and Rigoberto Burgueño, Emerging artificial intelligence methods in structural engineering, Engineering Structures 171:170-189, DOI:10.1016/j.engstruct.2018.05.084
  21. Shrikant M. Harle, Advancements and challenges in the application of artificial intelligence in civil engineering: a comprehensive review, Asian Journal of Civil Engineering, Volume 25, pages 1061–1078, (2024)
  22. Huang Youqinand Fu Jiyang,Review on Application of Artificial Intelligence in Civil Engineering,  Computer Modeling in Engineering & Sciences, Volume 121, Number 3, December 2019, pp. 845-875(31), Tech Science Press, https://doi.org/10.32604/cmes.2019.07653.
  23. Ying Huang, Simone A. Ludwig and Fodan Deng, Sensor optimization using a genetic algorithm for structural health monitoring in harsh environments, Journal of Civil Structural Health Monitoring, Volume 6, pages 509–519, (2016)
  24. A Rama Mohan Rao and Ganesh Anandakumar, Optimal placement of sensors for structural system identification and health monitoring using a hybrid swarm intelligence technique, Smart Materials and Structures, Volume 16, Number 6, 10.1088/0964-1726/16/6/071
  25. Muhammad Waqas,Latif Jan,Mohammad Haseeb Zafar,Syed Raheel Hassan and Rameez Asif, A Sensor Placement Approach Using Multi-Objective Hypergraph Particle Swarm Optimization to Improve Effectiveness of Structural Health Monitoring Systems, Sensors 2024, 24(5), 1423; https://doi.org/10.3390/s24051423
  26. Fatima Achouri, Abdelwahhab Khatir, Zakaria Smahi, Roberto Capozucca, and Abdelmoumin Ouled Brahim, 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) 45:621 https://doi.org/10.1007/s40430-023-04525.
  27. Neves, I. González, J. Leander and R.Karoumi, Structural health monitoring of bridges: a model-free ANN-based approach to damage detection, Journal of Civil Structural Health Monitoring,Volume 7, pages 689–702, (2017).
  28. Christian Cremona and João Pedro Santos, Structural Health Monitoring as a Big-Data Problem, Structural Engineering International 28(11):1-11, July 2018, DOI: 10.1080/10168664.2018.1461536.
  29. Arman Malekloo, Ekin Ozer and Mark Girolami, Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights, Structural Health Monitoring, Volume 21, Issue 4 https://doi.org/10.1177/14759217211036880.
  30. Farrar C.R. and Worden K. An introduction to structural health monitoring. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 2006;365:303–315. doi: 10.1098/rsta.2006.1928.
  31. Moore EZ, Nichols JM and Murphy KD. Model-based SHM: Demonstration of identification of a crack in a thin plate using free vibration data. Mech Syst Signal Process 2012; 29: 284–295.
  32. Billie F. Spencer Jr., Sung-Han Sim, Robin E. Kim and Hyungchul Yoon, Advances in artificial intelligence for structural health monitoring: A comprehensive review, KSCE Journal of Civil Engineering Volume 29, Issue 3, March 2025, 100203, https://doi.org/10.1016/j.kscej.2025.100203.
  33. Bai, H. Sezen, and A. Yilmaz, Detecting Cracksand Spalling AutomaticallyinExtreme EventsBy End-to-EndDeep Learning Frameworks, SPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2021, 161–168, https://doi.org/10.5194/isprs-annals-V-2-2021-161-2021, 2021.
  34. Farrar CR and Worden K. Structural health monitoring: a machine learning perspective. Chichester, West Sussex, UK; Hoboken, NJ: Wiley, 2013.
  35. Diego A Tibaduiza Burgos, Ricardo C Gomez Vargas, Cesar Pedraza, David Agis, and Francesc Pozo, Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of Data-Driven Applications, Sensors (Basel). 2020 Jan 29;20(3):733. doi: 10.3390/s20030733.
Ahead of Print Subscription Original Research
Volume 13
01
Received 06/12/2025
Accepted 28/01/2026
Published 30/01/2026
Publication Time 55 Days
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