10.25673/122114">


Proceedings of International Conference on Applied Innovation in IT
2025/08/29, Volume 13, Issue 4, pp.201-212

Artificial Intelligence-Based Structural Health Monitoring Using CNN and MATLAB: A Case Study on Bridge Safety


Subramani Raja, Shubham Sharma, Dawood Salman Abdulrahman and Vinoth Kanna


Abstract: Structural health monitoring (SHM) is critical to maintaining the safety and lifespan of important infrastructure, such as bridges, that endure constant environmental and mechanical loads. In this paper, an improved SHM system utilizing convolutional neural networks (CNN) developed with MATLAB is demonstrated to identify and classify structural irregularities in bridge structures. A large database of 12,500 labeled images of bridge surfaces, comprising 7,500 images of sound structures and 5,000 images with various defects such as cracks, corrosion, and deformation, was used to train and test the model. The CNN model, comprising 12 convolutional layers, achieved a test dataset accuracy of 98.72%, outperforming conventional vibration-based approaches with an average accuracy of 84.35%. The model also demonstrated a precision of 97.86%, a recall of 98.43%, and an F1-score of 98.14%, ensuring the accurate detection of small structural defects. The system can handle 50 images per second, making it suitable for real-time monitoring. Experimental verification was conducted on a 30-meter steel bridge prototype instrumented with accelerometers and thermal imaging cameras, and tested under various loads ranging from 5 kN to 50 kN. The system, under standard conditions, registered a 1.28% false positive and a 1.57% false negative, indicating its robustness against environmental noise. Moreover, computational expenses were slashed by 22% in MATLAB-based systems when compared to those using Python-based frameworks, ensuring compatibility with established industry standards SHM protocols. This work provides evidence that SHM systems facilitated by AI will significantly enhance the early detection of bridge defects, thereby increasing the efficiency of maintenance and improving public safety.

Keywords: Structural Health Monitoring, Convolutional Neural Networks, MATLAB, Bridge Safety, Defect Detection, Real-Time Monitoring.

DOI: 10.25673/122114

Download: PDF

References:

  1. R. R. Rabi, M. Vailati, and G. Monti, “Effectiveness of vibration-based techniques for damage localization and lifetime prediction in structural health monitoring of bridges: A comprehensive review,” Buildings, vol. 14, no. 4, Art. no. 1183, 2024, doi: 10.3390/buildings14041183.
  2. O. Avci, O. Abdeljaber, S. Kranyaz, M. F. M. Hussein, M. Gabbouj, and D. J. Inman, “A review of vibration-based damage detection in civil structures: From traditional methods to machine learning and deep learning applications,” Mech. Syst. Signal Process., vol. 147, Art. no. 107077, 2020, doi: 10.1016/j.ymssp.2020.107077.
  3. T. Liu, H. Xu, M. Ragulskis, M. Cao, and W. Ostachowicz, “A data-driven damage identification framework based on transmissibility function datasets and one-dimensional convolutional neural networks: Verification on a structural health monitoring benchmark structure,” Sensors, vol. 20, no. 4, Art. no. 1059, 2020, doi: 10.3390/s20041059.
  4. X. Zhang and W. Zhou, “Structural vibration data anomaly detection based on multiple feature information using CNN-LSTM model,” Struct. Control Health Monit., Art. no. 3906180, 2023, doi: 10.1155/2023/3906180.
  5. K. Luo, X. Kong, J. Zhang, J. Hu, J. Li, and H. Tang, “Computer vision-based bridge inspection and monitoring: A review,” Sensors, vol. 23, no. 18, Art. no. 7863, 2023, doi: 10.3390/s23187863.
  6. M. K. Quresh and S. Sehrawat, “Multi-method non-destructive testing for improving bridge health using AI for proactive structural health and predictive maintenance,” 2025, doi: 10.71143/ce6jx847.
  7. Z. Sun, M. Mahmoodian, A. Sidiq, S. Jayasinghe, F. Shahrivar, and S. Setunge, “Optimal sensor placement for structural health monitoring: A comprehensive review,” J. Sens. Actuator Netw., vol. 14, no. 2, Art. no. 22, 2025, doi: 10.3390/jsan14020022.
  8. S. Wan, S. Guan, and Y. Tang, “Advancing bridge structural health monitoring: Insights into knowledge-driven and data-driven approaches,” J. Data Sci. Intell. Syst., 2023, doi: 10.47852/bonviewjdsis3202964.
  9. F. Luleci, F. N. Catbas, and O. Avci, “A literature review: Generative adversarial networks for civil structural health monitoring,” Front. Built Environ., vol. 8, Art. no. 1027379, 2022, doi: 10.3389/fbuil.2022.1027379.
  10. E. Figueiredo and J. Brownjohn, “Three decades of statistical pattern recognition paradigm for SHM of bridges,” Struct. Health Monit., 2022, doi: 10.1177/14759217221075241.
  11. N. Pathak, “Bridge health monitoring using CNN,” in Proc. IEEE Int. Conf. Computational Design and Workflows (ICCDW), 2020, doi: 10.1109/ICCDW45521.2020.9318674.
  12. Z. Rastin, G. G. Amiri, and E. Darvishan, “Unsupervised structural damage detection technique based on a deep convolutional autoencoder,” Adv. Civ. Eng., Art. no. 6658575, 2021, doi: 10.1155/2021/6658575.
  13. Y. J. Cha, W. Choi, and O. Buyukozturk, “Deep learning-based crack damage detection using convolutional neural networks,” Comput.-Aided Civ. Infrastruct. Eng., vol. 32, no. 5, pp. 361–378, 2017, doi: 10.1111/mice.12263.
  14. H. Zoubir, M. Rguig, A. Chehri, M. El Aroussi, and R. Saadane, “Leveraging public safety and enhancing crack detection in concrete bridges using deep convolutional neural networks,” in Proc. IEEE WFPST, 2024, doi: 10.1109/WFPST58552.2024.00029.
  15. Y. J. Cha, W. Choi, G. Suh, S. Mahmoudkhani, and O. Buyukozturk, “Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types,” Comput.-Aided Civ. Infrastruct. Eng., vol. 33, no. 9, pp. 731–747, 2017, doi: 10.1111/mice.12334.
  16. A. Armijo and D. Zamora-Sánchez, “Integration of railway bridge structural health monitoring into the Internet of Things with a digital twin: A case study,” Sensors, vol. 24, no. 7, Art. no. 2115, 2024, doi: 10.3390/s24072115.
  17. A. K. Mishra, G. Gangisetti, and D. Khazanchi, “Integrating edge-AI in structural health monitoring domain,” arXiv preprint arXiv:2304.03718, 2023, doi: 10.48550/arXiv.2304.03718.
  18. A. Moallemi, A. Burrello, D. Brunelli, and L. Benini, “Exploring scalable, distributed real-time anomaly detection for bridge health monitoring,” IEEE Internet Things J., vol. 9, no. 19, pp. 18744–18756, 2022, doi: 10.1109/JIOT.2022.3157532.

    HOME

       - Conference
       - Journal
       - Paper Submission to Journal
       - Paper Submission to Conference
       - For Authors
       - For Reviewers
       - Important Dates
       - Conference Committee
       - Editorial Board
       - Reviewers
       - Last Proceedings


    PROCEEDINGS

       - Volume 13, Issue 4 (ICAIIT 2025)
       - Volume 13, Issue 3 (ICAIIT 2025)
       - Volume 13, Issue 2 (ICAIIT 2025)
       - Volume 13, Issue 1 (ICAIIT 2025)
       - Volume 12, Issue 2 (ICAIIT 2024)
       - Volume 12, Issue 1 (ICAIIT 2024)
       - Volume 11, Issue 2 (ICAIIT 2023)
       - Volume 11, Issue 1 (ICAIIT 2023)
       - Volume 10, Issue 1 (ICAIIT 2022)
       - Volume 9, Issue 1 (ICAIIT 2021)
       - Volume 8, Issue 1 (ICAIIT 2020)
       - Volume 7, Issue 1 (ICAIIT 2019)
       - Volume 7, Issue 2 (ICAIIT 2019)
       - Volume 6, Issue 1 (ICAIIT 2018)
       - Volume 5, Issue 1 (ICAIIT 2017)
       - Volume 4, Issue 1 (ICAIIT 2016)
       - Volume 3, Issue 1 (ICAIIT 2015)
       - Volume 2, Issue 1 (ICAIIT 2014)
       - Volume 1, Issue 1 (ICAIIT 2013)


    PAST CONFERENCES

       ICAIIT 2025
         - Photos
         - Reports

       ICAIIT 2024
         - Photos
         - Reports

       ICAIIT 2023
         - Photos
         - Reports

       ICAIIT 2021
         - Photos
         - Reports

       ICAIIT 2020
         - Photos
         - Reports

       ICAIIT 2019
         - Photos
         - Reports

       ICAIIT 2018
         - Photos
         - Reports

    ETHICS IN PUBLICATIONS

    ACCOMODATION

    CONTACT US

 

        

         Proceedings of the International Conference on Applied Innovations in IT by Anhalt University of Applied Sciences is licensed under CC BY-SA 4.0


                                                   This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License


           ISSN 2199-8876
           Publisher: Edition Hochschule Anhalt
           Location: Anhalt University of Applied Sciences
           Email: leiterin.hsb@hs-anhalt.de
           Phone: +49 (0) 3496 67 5611
           Address: Building 01 - Red Building, Top floor, Room 425, Bernburger Str. 55, D-06366 Köthen, Germany

        site traffic counter

Creative Commons License
Except where otherwise noted, all works and proceedings on this site is licensed under Creative Commons Attribution-ShareAlike 4.0 International License.