Post-Earthquake Bridge Damage Assessment Using Machine Learning (ML) and Artificial Inteligence (AI): A Systematic Literature Review
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Abstract
Bridges are critical infrastructure highly vulnerable to earthquake-induced damage, posing serious risks to transportation continuity and public safety. Traditional methods, such as visual inspection, remain in use; however, they are limited in efficiency, scalability, and accuracy. This highlights the urgent need for more advanced approaches. Machine Learning (ML) and Artificial Intelligence (AI) have emerged as promising alternatives for assessing post-earthquake bridge damage. However, existing studies often lack a systematic synthesis of methodological trends, rely on limited or unstandardized datasets, and insufficiently address real-world implementation challenges. This study conducts a Systematic Literature Review (SLR) to critically examine the application of ML/AI in post-earthquake bridge damage assessment, focusing on methodological trends, commonly used datasets, and implementation challenges. Relevant journal articles published between 2019 and 2025 were selected through structured keyword strategies and filtered based on publication type, relevance, and journal quality (Q1-Q4). The findings indicate that Random Forest (RF) and Convolutional Neural Networks (CNN) are among the most widely applied ML methods, owing to their strengths in classification and visual data analysis. Frequently used datasets include bridge damage records from California, shake table test time-series data, and sensor-based monitoring data. Persistent challenges include data heterogeneity, limited availability of real-time datasets, and the interpretability of ML models. The novelty of this study lies in providing a consolidated synthesis of current research, bridging methodological gaps, and highlighting implementation challenges. Future research should focus on developing real-time datasets, establishing robust model validation frameworks, and enhancing the interpretability of ML techniques to strengthen disaster risk mitigation and improve bridge resilience.
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How to Cite
Mardhiyah, A. (2025). Post-Earthquake Bridge Damage Assessment Using Machine Learning (ML) and Artificial Inteligence (AI): A Systematic Literature Review. Cantilever: Jurnal Penelitian Dan Kajian Bidang Teknik Sipil, 14(2), 105-112. https://doi.org/10.35139/cantilever.v14i2.459
References
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[22] I. M. Mantawy and M. O. Mantawy, ‘Convolutional neural network based structural health monitoring for rocking bridge system by encoding time‐series into images’, Struct Control Health Monit, vol. 29, no. 3, Mar. 2022, doi: 10.1002/stc.2897.
[23] V.-L. Tran, T.-C. Vo, and T.-Q. Nguyen, ‘One-dimensional convolutional neural network for damage detection of structures using time series data’, Asian Journal of Civil Engineering, vol. 25, no. 1, pp. 827–860, Jan. 2024, doi: 10.1007/s42107-023-00816-w.
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[25] L. Shen, Y. Hong, Z. Zhou, Q. Pu, and K. Deng, ‘Research and Modification on the Park-Ang Damage Index for Railway Rectangular Piers’, Journal of Earthquake Engineering, vol. 28, no. 16, pp. 4672–4697, Dec. 2024, doi: 10.1080/13632469.2024.2398553.
[26] X. Liang, ‘Image‐based post‐disaster inspection of reinforced concrete bridge systems using deep learning with Bayesian optimization’, Computer-Aided Civil and Infrastructure Engineering, vol. 34, no. 5, pp. 415–430, May 2019, doi: 10.1111/mice.12425.
[27] Y. Xu, W. Qian, N. Li, and H. Li, ‘Typical advances of artificial intelligence in civil engineering’, Advances in Structural Engineering, vol. 25, no. 16, pp. 3405–3424, Dec. 2022, doi: 10.1177/13694332221127340.
[28] N. Bijelić, D. G. Lignos, and A. Alahi, ‘The automated collapse data constructor technique and the data‐driven methodology for seismic collapse risk assessment’, Earthq Eng Struct Dyn, vol. 52, no. 8, pp. 2452–2479, Jul. 2023, doi: 10.1002/eqe.3865.
[29] Y. Pang, X. Zhou, W. He, J. Zhong, and O. Hui, ‘Uniform Design–Based Gaussian Process Regression for Data-Driven Rapid Fragility Assessment of Bridges’, Journal of Structural Engineering, vol. 147, no. 4, Apr. 2021, doi: 10.1061/(ASCE)ST.1943-541X.0002953.
[30] R. Mo et al., ‘Prediction and correlations estimation of seismic capacities of pier columns: Extended Gaussian process regression models’, Structural Safety, vol. 109, p. 102457, Jul. 2024, doi: 10.1016/j.strusafe.2024.102457.
[31] Y. Yan, Y. Xia, J. Yang, and L. Sun, ‘Optimal selection of scalar and vector-valued seismic intensity measures based on Gaussian Process Regression’, Soil Dynamics and Earthquake Engineering, vol. 152, p. 106961, Jan. 2022, doi: 10.1016/j.soildyn.2021.106961.
[32] Y. Lou, S. Meng, and Y. Zhou, ‘Deep learning-based three-dimensional crack damage detection method using point clouds without color information’, Struct Health Monit, vol. 24, no. 2, pp. 657–675, Mar. 2025, doi: 10.1177/14759217241236929.
[33] M. Akbarnezhad, M. Salehi, and R. DesRoches, ‘Application of machine learning in seismic fragility assessment of bridges with SMA-restrained rocking columns’, Structures, vol. 50, pp. 1320–1337, Apr. 2023, doi: 10.1016/j.istruc.2023.02.105.
[34] E. Figueiredo, I. Moldovan, P. Alves, H. Rebelo, and L. Souza, ‘Smartphone Application for Structural Health Monitoring of Bridges’, Sensors, vol. 22, no. 21, p. 8483, Nov. 2022, doi: 10.3390/s22218483.
[35] B. Todorov and A. Muntasir Billah, ‘Machine learning driven seismic performance limit state identification for performance-based seismic design of bridge piers’, Eng Struct, vol. 255, p. 113919, Mar. 2022, doi: 10.1016/j.engstruct.2022.113919.
[36] S. Mangalathu, K. Karthikeyan, D.-C. Feng, and J.-S. Jeon, ‘Machine-learning interpretability techniques for seismic performance assessment of infrastructure systems’, Eng Struct, vol. 250, p. 112883, Jan. 2022, doi: 10.1016/j.engstruct.2021.112883.
[37] E. Tubaldi, F. Turchetti, E. Ozer, J. Fayaz, P. Gehl, and C. Galasso, ‘A Bayesian network‐based probabilistic framework for updating aftershock risk of bridges’, Earthq Eng Struct Dyn, vol. 51, no. 10, pp. 2496–2519, Aug. 2022, doi: 10.1002/eqe.3698.
[2] S. A. Mitoulis, M. Domaneschi, G. P. Cimellaro, and J. R. Casas, ‘Bridge and transport network resilience – a perspective’, Proceedings of the Institution of Civil Engineers - Bridge Engineering, vol. 175, no. 3, pp. 138–149, Sep. 2022, doi: 10.1680/jbren.21.00055.
[3] M. Miari, K. K. Choong, and R. Jankowski, ‘Seismic Pounding Between Bridge Segments: A State-of-the-Art Review’, Archives of Computational Methods in Engineering, vol. 28, no. 2, pp. 495–504, Mar. 2021, doi: 10.1007/s11831-019-09389-x.
[4] G. Falsone, A. Recupero, and N. Spinella, ‘Effects of near-fault earthquakes on existing bridge performances’, J Civ Struct Health Monit, vol. 10, no. 1, pp. 165–176, Feb. 2020, doi: 10.1007/s13349-020-00378-4.
[5] R. Zinno, S. S. Haghshenas, G. Guido, and A. VItale, ‘Artificial Intelligence and Structural Health Monitoring of Bridges: A Review of the State-of-the-Art’, IEEE Access, vol. 10, pp. 88058–88078, 2022, doi: 10.1109/ACCESS.2022.3199443.
[6] T.-K. Lin et al., ‘Damage Scenario Prediction for Concrete Bridge Columns Using Deep Generative Networks’, Struct Control Health Monit, vol. 2024, no. 1, Jan. 2024, doi: 10.1155/2024/5526537.
[7] G. Jiang et al., ‘Study on Evaluation Theory of Bridge Damage State and Methodology on Early Warning of Danger’, Advances in Materials Science and Engineering, vol. 2022, pp. 1–11, Mar. 2022, doi: 10.1155/2022/6636959.
[8] S. Mangalathu, S.-H. Hwang, E. Choi, and J.-S. Jeon, ‘Rapid seismic damage evaluation of bridge portfolios using machine learning techniques’, Eng Struct, vol. 201, p. 109785, Dec. 2019, doi: 10.1016/j.engstruct.2019.109785.
[9] M. Salkhordeh, M. Mirtaheri, N. Rabiee, E. Govahi, and S. Soroushian, ‘A Rapid Machine Learning-Based Damage Detection Technique for Detecting Local Damages in Reinforced Concrete Bridges’, Journal of Earthquake Engineering, vol. 27, no. 16, pp. 4705–4738, Dec. 2023, doi: 10.1080/13632469.2023.2193277.
[10] M. Shokri and K. Tavakoli, ‘A Review on the Artificial Neural Network Approach to Analysis and Prediction of Seismic Damage in Infrastructure’, International Journal of Hydromechatronics, vol. 1, no. 1, p. 1, 2019, doi: 10.1504/IJHM.2019.10026005.
[11] Y. Huang, J. He, and Z. Zhu, ‘Rapid Assessment of Seismic Risk for Railway Bridges Based on Machine Learning’, International Journal of Structural Stability and Dynamics, vol. 24, no. 06, Mar. 2024, doi: 10.1142/S0219455424500561.
[12] D. Gautam, A. Bhattarai, and R. Rupakhety, ‘Machine learning and soft voting ensemble classification for earthquake induced damage to bridges’, Eng Struct, vol. 303, p. 117534, Mar. 2024, doi: 10.1016/j.engstruct.2024.117534.
[13] S.-Q. Li, J.-C. Han, Y.-R. Li, and P.-F. Qin, ‘Intelligent prediction and evaluation models for the seismic risk and vulnerability of reinforced concrete girder bridges in large-scale zones’, Reliab Eng Syst Saf, vol. 256, p. 110743, Apr. 2025, doi: 10.1016/j.ress.2024.110743.
[14] X. Zhang, D. He, J. Wang, S. Wang, and M. Gu, ‘Machine learning for predicting maximum displacement in soil-pile-superstructure systems in laterally spreading ground’, Eng Appl Artif Intell, vol. 139, no. B, p. 109701, Jan. 2025.
[15] M. Zaker Esteghamati and S. Baddipalli, ‘Efficiency and explainability of design‐oriented machine learning models to estimate seismic response, fragility, and loss of a steel building inventory’, Earthq Eng Struct Dyn, vol. 54, no. 2, pp. 618–647, Feb. 2025, doi: 10.1002/eqe.4273.
[16] W. Zhang, J. Wen, H. Dong, Q. Han, and X. Du, ‘Post-earthquake functionality and resilience prediction of bridge networks based on data-driven machine learning method’, Soil Dynamics and Earthquake Engineering, vol. 190, p. 109127, Mar. 2025, doi: 10.1016/j.soildyn.2024.109127.
[17] Z. Liu, S. Li, W. Zhao, and A. Guo, ‘Post-earthquake assessment model for highway bridge networks considering traffic congestion due to earthquake-induced bridge damage’, Eng Struct, vol. 262, p. 114395, Jul. 2022, doi: 10.1016/j.engstruct.2022.114395.
[18] B. Xu, X. Wang, C.-S. W. Yang, and Y. Li, ‘Probabilistic curvature limit states of corroded circular RC bridge columns: Data-driven models and application to lifetime seismic fragility analyses’, Earthquake Spectra, vol. 40, no. 4, pp. 2805–2835, Nov. 2024, doi: 10.1177/87552930241255091.
[19] F. Soleimani and X. Liu, ‘Artificial neural network application in predicting probabilistic seismic demands of bridge components’, Earthq Eng Struct Dyn, vol. 51, no. 3, pp. 612–629, Mar. 2022, doi: 10.1002/eqe.3582.
[20] X. Liang, ‘Enhancing Seismic Damage Detection and Assessment in Highway Bridge Systems: A Pattern Recognition Approach with Bayesian Optimization’, Sensors, vol. 24, no. 2, p. 611, Jan. 2024.
[21] Z. Shi, R. Zhong, and N. Jin, ‘Seismic Damage Identification of Composite Cable-Stayed Bridges Using Support Vector Machines and Wavelet Networks’, Sustainability (Switzerland), vol. 15, no. 1, Jan. 2023, doi: 10.3390/su15010108.
[22] I. M. Mantawy and M. O. Mantawy, ‘Convolutional neural network based structural health monitoring for rocking bridge system by encoding time‐series into images’, Struct Control Health Monit, vol. 29, no. 3, Mar. 2022, doi: 10.1002/stc.2897.
[23] V.-L. Tran, T.-C. Vo, and T.-Q. Nguyen, ‘One-dimensional convolutional neural network for damage detection of structures using time series data’, Asian Journal of Civil Engineering, vol. 25, no. 1, pp. 827–860, Jan. 2024, doi: 10.1007/s42107-023-00816-w.
[24] L. Liu, S. Miao, Y. Song, and H. Luo, ‘Rapid Seismic Damage Assessment of RC Bridges Considering Time–Frequency Characteristics of Ground Motions’, Iranian Journal of Science and Technology, Transactions of Civil Engineering, vol. 48, no. 6, pp. 4367–4381, Dec. 2024, doi: 10.1007/s40996-023-01328-y.
[25] L. Shen, Y. Hong, Z. Zhou, Q. Pu, and K. Deng, ‘Research and Modification on the Park-Ang Damage Index for Railway Rectangular Piers’, Journal of Earthquake Engineering, vol. 28, no. 16, pp. 4672–4697, Dec. 2024, doi: 10.1080/13632469.2024.2398553.
[26] X. Liang, ‘Image‐based post‐disaster inspection of reinforced concrete bridge systems using deep learning with Bayesian optimization’, Computer-Aided Civil and Infrastructure Engineering, vol. 34, no. 5, pp. 415–430, May 2019, doi: 10.1111/mice.12425.
[27] Y. Xu, W. Qian, N. Li, and H. Li, ‘Typical advances of artificial intelligence in civil engineering’, Advances in Structural Engineering, vol. 25, no. 16, pp. 3405–3424, Dec. 2022, doi: 10.1177/13694332221127340.
[28] N. Bijelić, D. G. Lignos, and A. Alahi, ‘The automated collapse data constructor technique and the data‐driven methodology for seismic collapse risk assessment’, Earthq Eng Struct Dyn, vol. 52, no. 8, pp. 2452–2479, Jul. 2023, doi: 10.1002/eqe.3865.
[29] Y. Pang, X. Zhou, W. He, J. Zhong, and O. Hui, ‘Uniform Design–Based Gaussian Process Regression for Data-Driven Rapid Fragility Assessment of Bridges’, Journal of Structural Engineering, vol. 147, no. 4, Apr. 2021, doi: 10.1061/(ASCE)ST.1943-541X.0002953.
[30] R. Mo et al., ‘Prediction and correlations estimation of seismic capacities of pier columns: Extended Gaussian process regression models’, Structural Safety, vol. 109, p. 102457, Jul. 2024, doi: 10.1016/j.strusafe.2024.102457.
[31] Y. Yan, Y. Xia, J. Yang, and L. Sun, ‘Optimal selection of scalar and vector-valued seismic intensity measures based on Gaussian Process Regression’, Soil Dynamics and Earthquake Engineering, vol. 152, p. 106961, Jan. 2022, doi: 10.1016/j.soildyn.2021.106961.
[32] Y. Lou, S. Meng, and Y. Zhou, ‘Deep learning-based three-dimensional crack damage detection method using point clouds without color information’, Struct Health Monit, vol. 24, no. 2, pp. 657–675, Mar. 2025, doi: 10.1177/14759217241236929.
[33] M. Akbarnezhad, M. Salehi, and R. DesRoches, ‘Application of machine learning in seismic fragility assessment of bridges with SMA-restrained rocking columns’, Structures, vol. 50, pp. 1320–1337, Apr. 2023, doi: 10.1016/j.istruc.2023.02.105.
[34] E. Figueiredo, I. Moldovan, P. Alves, H. Rebelo, and L. Souza, ‘Smartphone Application for Structural Health Monitoring of Bridges’, Sensors, vol. 22, no. 21, p. 8483, Nov. 2022, doi: 10.3390/s22218483.
[35] B. Todorov and A. Muntasir Billah, ‘Machine learning driven seismic performance limit state identification for performance-based seismic design of bridge piers’, Eng Struct, vol. 255, p. 113919, Mar. 2022, doi: 10.1016/j.engstruct.2022.113919.
[36] S. Mangalathu, K. Karthikeyan, D.-C. Feng, and J.-S. Jeon, ‘Machine-learning interpretability techniques for seismic performance assessment of infrastructure systems’, Eng Struct, vol. 250, p. 112883, Jan. 2022, doi: 10.1016/j.engstruct.2021.112883.
[37] E. Tubaldi, F. Turchetti, E. Ozer, J. Fayaz, P. Gehl, and C. Galasso, ‘A Bayesian network‐based probabilistic framework for updating aftershock risk of bridges’, Earthq Eng Struct Dyn, vol. 51, no. 10, pp. 2496–2519, Aug. 2022, doi: 10.1002/eqe.3698.
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