A Damage Localization Approach for Rahmen Bridge Based on Convolutional Neural Network

  • Kanghyeok Lee
  • Namju Byun
  • Do Hyoung ShinEmail author
Construction Management


Damage localization is the process of detecting the location of damage using a structural health monitoring system. However, existing damage localization methods cannot be used for localizing the damage of bridges in real time because of their slow testing speed. Thus, in this study a damage localization approach was developed using a convolutional neural network (CNN). To develop the CNN model for damage localization, simulation data was generated through a numerical model of a reinforced concrete Rahmen bridge with static loading conditions. The proposed CNN-based approach aims to identify 12 single damage locations or no damage. The approach was trained and tested with three different data set generated with three damage severities, and it was possible to estimate the damage location with an accuracy of 87.3% when the damage severity in the bridge is serious. The results showed that the deep learning has the potential to overcome the limitations of existing damage localization techniques.


Convolutional neural network Damage localization Damage detection Structural health monitoring Deep learning 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



This research was supported by a grant (18CTAP-C117271-03) from Technology Advancement Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government.


  1. Abdeljaber O, Avci O, Kiranyaz S, Gabbouj M, Inman DJ (2017) Realtime vibration-based structural damage detection using one-dimensional convolutional neural networks. Journal of Sound and Vibration 388:154–170, DOI: CrossRefGoogle Scholar
  2. Behmanesh I, Moaveni B, Lombaert G, Papadimitriou C (2015) Hierarchical Bayesian model updating for structural identification. Mechanical Systems and Signal Processing 64:360–376, DOI: CrossRefGoogle Scholar
  3. Boulkaibet I, Mthembu L, Marwala T, Friswell MI, Adhikari S (2015) Finite element model updating using the shadow hybrid Monte Carlo technique. Mechanical Systems and Signal Processing 52: 115–132, DOI: CrossRefGoogle Scholar
  4. Boulkaibet I, Mthembu L, Marwala T, Friswell MI, Adhikari S (2017) Finite element model updating using Hamiltonian Monte Carlo techniques. Inverse Problems in Science and Engineering 25(7): 1042–1070, DOI: MathSciNetCrossRefGoogle Scholar
  5. Cha YJ, Choi W, Büyüköztürk O (2017) Deep learning-based crack damage detection using convolutional neural networks. Computer-Aided Civil and Infrastructure Engineering 32(5):361–378, DOI: CrossRefGoogle Scholar
  6. Ching J, Beck JL (2004) New Bayesian model updating algorithm applied to a structural health monitoring benchmark. Structural Health Monitoring 3(4):313–332, DOI: CrossRefGoogle Scholar
  7. Gao ZF, Chen XJ (2011) Structure data processing and damage identification based on wavelet and artificial neural network. Research Journal of Applied Sciences, Engineering and Technology 3(1):1203–1208Google Scholar
  8. Hao H, Xia Y (2002) Vibration-based damage detection of structures by genetic algorithm. Journal of Computing in Civil Engineering 16(3): 222–229CrossRefGoogle Scholar
  9. Hou R, Xia Y, Zhou X (2018) Structural damage detection based on l1 regularization using natural frequencies and mode shapes. Structural Control and Health Monitoring 25(3):e2107, DOI: CrossRefGoogle Scholar
  10. Kim Y (2017) Development and application of reliability-based structural health monitoring algorithm for existing bridges. PhD Thesis, Inha University, Incheon, KoreaGoogle Scholar
  11. KISTEC (2012) Specific guidelines for safety inspections or safety examinations. Korea Infrastructure Safety and Technology Corporation, Jinju, Korea (in Korean)Google Scholar
  12. Lam HF, Yang J, Au SK (2015) Bayesian model updating of a coupled-slab system using field test data utilizing an enhanced Markov chain Monte Carlo simulation algorithm. Engineering Structures 102: 144–155, DOI: CrossRefGoogle Scholar
  13. Lam HF, Yang JH, Au SK (2018) Markov chain Monte Carlo-based Bayesian method for structural model updating and damage detection. Structural Control and Health Monitoring 25(4):e2140, DOI: CrossRefGoogle Scholar
  14. Lee Y (2015) A study of improvement and longevity of the aging urban infrastructure in Korea. Journal of the Korean Society of Civil Engineers 63(11):10–19 (in Korean)Google Scholar
  15. Lee JJ, Lee JW, Yi JH, Yun CB, Jung HY (2005) Neural networks-based damage detection for bridges considering errors in baseline finite element models. Journal of Sound and Vibration 280(3–5):555–578, DOI: CrossRefGoogle Scholar
  16. Lee K, Park JH, Oh SM, Shin DH (2017) Methodology for detection of crack location in deteriorated bridges using convolution neural network. Proceedings of 2017 KSCE convention, October 18–20, Busan, Korea, 1263–1264Google Scholar
  17. Levin RI, Lieven NAJ (1998) Dynamic finite element model updating using simulated annealing and genetic algorithms. Mechanical Systems and Signal Processing 12(1):91–120, DOI: CrossRefGoogle Scholar
  18. Lin YZ, Nie ZH, Ma HW (2017) Structural damage detection with automatic feature-extraction through deep learning. Computer-Aided Civil and Infrastructure Engineering 32(12):1025–1046, DOI: CrossRefGoogle Scholar
  19. Marwala T, Sibisi S (2005) Finite element model updating using Bayesian framework and modal properties. Journal of Aircraft 42(1):275–278, DOI: CrossRefGoogle Scholar
  20. Mehrjoo M, Khaji N, Moharrami H, Bahreininejad A (2008) Damage detection of truss bridge joints using artificial neural networks. Expert Systems with Applications 35(3):1122–1131, DOI: CrossRefGoogle Scholar
  21. Meruane V, Heylen W (2011) An hybrid real genetic algorithm to detect structural damage using modal properties. Mechanical Systems and Signal Processing 25(5):1559–1573, DOI: CrossRefGoogle Scholar
  22. MOLIT (2015) Bridge design specifications of Korean government. Ministry of Land, Infrastructure and Transport, Sejong, Korea (in Korean)Google Scholar
  23. MOLIT (2017) Road bridge and tunnel statistics. Ministry of Land, Infrastructure and Transport, Sejong, KoreaGoogle Scholar
  24. Ni YQ, Zhou XT, Ko JM, Wang BS (2000) Vibration-based damage localization in Ting Kau Bridge using probabilistic neural network. Advances in Structural Dynamics 2:1069–1076, DOI: Scholar
  25. Oh BK, Kim D, Park HS (2017) Modal response-based visual system identification and model updating methods for building structures. Computer-Aided Civil and Infrastructure Engineering 32(1):34–56 DOI: CrossRefGoogle Scholar
  26. Pandey AK, Biswas M, Samman MM (1991) Damage detection from changes in curvature mode shapes. Journal of Sound and Vibration 145(2):321–332, DOI: CrossRefGoogle Scholar
  27. Salawu OS (1997) Detection of structural damage through changes in frequency: A review. Engineering Structures 19(9):718–723, DOI: CrossRefGoogle Scholar
  28. Sidhu J, Ewins DJ (1984) Correlation of finite element and modal test studies of a practical structure. Proceedings of the 2nd international modal analysis conference, February 6–9, Orlando, FL, USA, 756–762Google Scholar
  29. Tu Z, Lu Y (2008) FE model updating using artificial boundary conditions with genetic algorithms. Computers & Structures 86(7–8):714–727, DOI: CrossRefGoogle Scholar
  30. Vishay Technique Note (2005) Strain gage selection: Criteria, procedures, recommendations. TN-505-4, Vishay Inc., Malvern, PA, USAGoogle Scholar
  31. Zárate BA, Caicedo JM, Yu J, Ziehl P (2012) Bayesian model updating and prognosis of fatigue crack growth. Engineering Structures 45:53–61, DOI: CrossRefGoogle Scholar
  32. Zhang L, Yang F, Zhang YD, Zhu YJ (2016) Road crack detection using deep convolutional neural network. Proceedings of 2016 IEEE international conference on image processing (ICIP), September 25–28, Phoenix, AZ, USA, 3708–3712, DOI:

Copyright information

© Korean Society of Civil Engineers 2019

Authors and Affiliations

  1. 1.Member, Dept. of Civil EngineeringInha UniversityIncheonKorea
  2. 2.Dept. of Civil, Environmental and Architectural EngineeringKorea UniversitySeoulKorea

Personalised recommendations