Deep learning for image-based structural element damage assessments in post-earthquake buildings: a systematic review

Abstract

Deep learning (DL) is increasingly used to support image-based post-earthquake damage assessment of local structural elements in buildings, yet the existing literature remains inconsistent in task formulation, dataset design, model evaluation, performance enhancement and field deployment readiness. This paper presents a systematic literature review conducted in accordance with the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA) framework, covering 46 primary studies published between 2015 and 2025 on DL-based structural element level post-earthquake damage assessment. Using a transparent protocol for study identification, screening, eligibility assessment, quality appraisal, and data extraction, the review synthesises the literature through a task-aware taxonomy comprising four DL configurations: damage classification, damage segmentation, damage detection and estimation of quantitative damage severity. The review examines how these task configurations relate to damage assessment targets, local structural elements, DL architectures, data acquisition methods, data modalities, data quality, preprocessing pipelines, evaluation protocols, and model enhancement strategies, including transfer learning, data augmentation, synthetic data generation and multimodal fusion. The synthesis reveals key gaps in the under representation of several structural elements and failure mechanisms, limited use of multimodal and realistic field data, inconsistent task specific evaluation protocols, weak evidence of model generalisation to unseen data, and the absence of structurally grounded damage assessment criteria. To improve dependable field deployment, the review highlights the need for data efficient models, stronger multimodal learning, stricter evaluation on unseen field data, and integration with real-time engineer facing assessment workflows, eventually supporting development of disaster-resilient build environments.

Keywords

Deep learning / Buildings / Damage assessment / Image-based data / Post-earthquake / Synthetic data

References

[1] Al Shafian S, Hu D. Integrating machine learning and remote sensing in disaster management: A decadal review of post-disaster building damage assessment. Buildings, 2024, 14. ArticleID: 2344

[2] Alberto Y, Otsubo’ M, Kyokawa H, et al.. Reconnaissance of the 2017 Puebla, Mexico earthquake. Soils and Foundations, 2018, 58: 1073-1092.

[3] Alexander QG, Hoskere V, Narazaki Y, et al.. Fusion of thermal and RGB images for automated deep learning based crack detection in civil infrastructure. AI in Civil Engineering, 2022, 1. ArticleID: 3

[4] Ali Bakhshi E, Yazdanpanah O, Dolatshahi KM. Pixel-level multicategory semantic segmentation of visible seismic damage in bridge piers using an Attention-Mamba Transformer-based U-Net model. Automation in Construction, 2026.

[5] Ali R, Cha Y-J. Subsurface damage detection of a steel bridge using deep learning and uncooled micro-bolometer. Construction and Building Materials, 2019, 226: 376-387.

[6] Ali R, Cha Y-J. Attention-based generative adversarial network with internal damage segmentation using thermography. Automation in Construction, 2022, 141. ArticleID: 104412

[7] Ali R, Kang D, Suh G, Cha Y-J. Real-time multiple damage mapping using autonomous UAV and deep faster region-based neural networks for GPS-denied structures. Automation in Construction, 2021.

[8] American Society of Civil Engineers (2017) Seismic Evaluation and Retrofit of Existing Buildings (ASCE 41–17). American Society of Civil Engineers

[9] Aung PPW, Sam KM, Kulinan AS, et al.. Enhancing deep learning in structural damage identification with 3D-engine synthetic data. Automation in Construction, 2025, 175. ArticleID: 106203

[10] Bai Z, Liu T, Zou D, et al.. Multi-scale image-based damage recognition and assessment for reinforced concrete structures in post-earthquake emergency response. Engineering Structures, 2024, 314. ArticleID: 118402

[11] Baltrusaitis T, Ahuja C, Morency L-P. Multimodal machine learning: A survey and taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41: 423-443.

[12] Bao Y, Sun H, Xu Y, et al.. Recent advances in structural health diagnosis: A machine learning perspective. Advances in Bridge Engineering, 2025, 6. ArticleID: 7

[13] Barkhordari MS, Armaghani DJ, Asteris PG. Structural damage identification using ensemble deep convolutional neural network models. Computer Modeling in Engineering & Sciences, 2023, 134: 835-855.

[14] Beijing Municipal Commission of Housing and Urban-Rural Development and Beijing Municipal Bureau of Quality and Technical Supervision. (2015). DB11/637–2015: Comprehensive Safety Appraisal Standards for Building Structures. Beijing, China

[15] Bishop CM, Bishop H. Deep learning, 2024Springer International Publishing.

[16] Cai WZ, Chen SZ, Feng DC, Taciroglu E. Quantitative multi-index residual capacities assessment of structural components through deep-learning-based image processing: A proof-of-concept study on masonry walls. Advanced Engineering Informatics, 2025, 65. ArticleID: 103185

[17] Carrera-Rivera A, Ochoa W, Larrinaga F, Lasa G. How-to conduct a systematic literature review: A quick guide for computer science research. MethodsX, 2022, 9. ArticleID: 101895

[18] Cha Y-J, Ali R, Lewis J, Büyükӧztürk O. Deep learning-based structural health monitoring. Automation in Construction, 2024, 161. ArticleID: 105328

[19] Cha YJ, Choi W, Büyüköztürk O. Deep learning-based crack damage detection using convolutional neural networks. Computer-Aided Civil and Infrastructure Engineering, 2017, 32: 361-378.

[20] Cha Y, Choi W, Suh G, et al.. Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types. Computer-Aided Civil and Infrastructure Engineering, 2018, 33: 731-747.

[21] Chen J, Cho YK. CrackEmbed: Point feature embedding for crack segmentation from disaster site point clouds with anomaly detection. Advanced Engineering Informatics, 2022, 52. ArticleID: 101550

[22] Chen S-Y, Wang Y-W, Ni Y-Q, Zhang Y. When transfer learning meets dictionary learning: A new hybrid method for fast and automatic detection of cracks on concrete surfaces. Structural Control and Health Monitoring, 2024, 2024. ArticleID: 3185640

[23] Cheng M-Y, Khasani RR, Citra RJ. Image-based preliminary emergency assessment of damaged buildings after earthquake. Engineering Applications of Artificial Intelligence, 2023, 126. ArticleID: 107164

[24] Cheng MY, Sholeh MN, Kwek A. Computer vision-based post-earthquake inspections for building safety assessment. Journal of Building Engineering, 2024, 94. ArticleID: 109909

[25] Chida H, Takahashi N. Study on image diagnosis of timber houses damaged by earthquake using deep learning. Japan Architectural Review, 2021, 4: 420-430.

[26] Chun P, Yamane T, Maemura Y. A deep learning-based image captioning method to automatically generate comprehensive explanations of bridge damage. Computer-Aided Civil and Infrastructure Engineering, 2022, 37: 1387-1401.

[27] Dais D, Bal İE, Smyrou E, Sarhosis V. Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning. Automation in Construction, 2021, 125. ArticleID: 103606

[28] Deng, J., Dong, W., & Socher, R., Li, L. J., Li, K., & Fei-Fei, L. (2009). ImageNet: A Large-Scale Hierarchical Image Database. In: IEEE Conference on Computer Vision and Pattern Recognition. pp 248–255

[29] Dogan G, Hakan Arslan M, Ilki A. Detection of damages caused by earthquake and reinforcement corrosion in RC buildings with Deep Transfer Learning. Engineering Structures, 2023, 279. ArticleID: 115629

[30] Dondi P, Gullotti A, Inchingolo M, et al.. Post-earthquake structural damage detection with tunable semi-synthetic image generation. Engineering Applications of Artificial Intelligence, 2025, 147. ArticleID: 110302

[31] European Committee for Standardisation. (2005). Eurocode 8: Design of structures for earthquake resistance - Part 3: Assessment and retrofitting of buildings.

[32] Federal Emergency Management Agency. (2018). Building the Performance You Need, A Guide to State-of-the-Art Tools for Seismic Design and Assessment (FEMA P-58-7)

[33] Federal Emergency Management Agency. (2020). Hazus Earthquake Model Technical Manual (FEMA HAZUS). Washington, DC

[34] Feng D-C, Yi X, Deger ZT, et al.. Rapid post-earthquake damage assessment of building portfolios through deep learning-based component-level image recognition. Journal of Building Engineering, 2024, 98. ArticleID: 111380

[35] Frid-Adar M, Diamant I, Klang E, et al.. GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing, 2018, 321: 321-331.

[36] Gao Y, Mosalam KM. Deep transfer learning for image-based structural damage recognition. Computer-Aided Civil and Infrastructure Engineering, 2018, 33: 748-768.

[37] Gatti M. Structural health monitoring of an operational bridge: A case study. Engineering Structures, 2019, 195: 200-209.

[38] Ghosh Mondal T, Jahanshahi MR, Wu R, Wu ZY. Deep learning-based multi-class damage detection for autonomous post-disaster reconnaissance. Structural Control Health Monitoring, 2020, 27: 2507.

[39] Goodfellow I, Bengio Y, Courville A. Deep Learning, 2016, 2MIT Press

[40] Guha S, Jana RK, Sanyal MK. Artificial neural network approaches for disaster management: A literature review. International Journal of Disaster Risk Reduction, 2022.

[41] Gultekin B, Dogan G. A novel approach to discriminate between structural and non-structural post-earthquake damage in RC structures. Advances in Civil Engineering, 2024, 2024. ArticleID: 6027701

[42] Hakimi O, Liu H, Abudayyeh O. Deep learning-driven multi-level data fusion framework for predictive maintenance and structural health monitoring of concrete bridge decks. Automation in Construction, 2025, 175. ArticleID: 106180

[43] Hallee MJ, Napolitano RK, Reinhart WF, Glisic B. Crack detection in images of masonry using CNNs. Sensors, 2021, 21: 4929.

[44] Han X, Zhao Z, Chen L, et al.. Structural damage-causing concrete cracking detection based on a deep-learning method. Construction and Building Materials, 2022, 337. ArticleID: 127562

[45] Heusel M, Ramsauer H, Unterthiner T, et al.. GANs trained by a two time-scale update rule converge to a local Nash Equilibrium. Advances in Neural Information Processing Systems, 2017, 30: 6626-6637

[46] Ilki, A., Halici, O. F., Comert, M., & Demir, C. (2021). The Modified Post-earthquake Damage Assessment Methodology for TCIP (TCIP-DAM-2020). pp 85–107

[47] Jiang Y, Wang J, Shen X, Dai K. Large language model for post-earthquake structural damage assessment of buildings. Computer-Aided Civil and Infrastructure Engineering, 2025, 40: 6324-6342.

[48] Kang D, Cha Y-J. Autonomous UAVs for structural health monitoring using deep learning and an ultrasonic beacon system with geo-tagging. Computer-Aided Civil and Infrastructure Engineering, 2018, 33: 885-902.

[49] Kang DH, Cha Y-J. Efficient attention-based deep encoder and decoder for automatic crack segmentation. Structural Health Monitoring, 2022, 21: 2190-2205.

[50] Katsigiannis S, Seyedzadeh S, Agapiou A, Ramzan N. Deep learning for crack detection on masonry façades using limited data and transfer learning. Journal of Building Engineering, 2023, 76. ArticleID: 107105

[51] Khajwal AB, Cheng CS, Noshadravan A. Post-disaster damage classification based on deep multi-view image fusion. Computer-Aided Civil and Infrastructure Engineering, 2023, 38: 528-544.

[52] Kim B, Cho S. Automated vision-based detection of cracks on concrete surfaces using a deep learning technique. Sensors, 2018.

[53] Kim H, Ahn E, Shin M, Sim S-H. Crack and noncrack classification from concrete surface images using machine learning. Structural Health Monitoring, 2019, 18: 725-738.

[54] Kizilay F, Narman MR, Song H, et al.. Evaluating fine-tuned deep learning models for real-time earthquake damage assessment with drone-based images. AI in Civil Engineering, 2024, 3. ArticleID: 15

[55] Kolappan Geetha G, Sim S-H. Fast identification of concrete cracks using 1D deep learning and explainable artificial intelligence-based analysis. Automation in Construction, 2022, 143. ArticleID: 104572

[56] Liang X. Image-based post-disaster inspection of reinforced concrete bridge systems using deep learning with Bayesian optimization. Computer-Aided Civil and Infrastructure Engineering, 2019, 34: 415-430.

[57] Lin Y-C, Hsiao C-Y, Tong J-H, et al.. Application of Edge Computing in Structural Health Monitoring of simply supported PCI girder bridges. Sensors, 2022, 22. ArticleID: 8711

[58] Liu Y, Yeoh JKW. Robust pixel-wise concrete crack segmentation and properties retrieval using image patches. Automation in Construction, 2021, 123. ArticleID: 103535

[59] Luckey, D., Fritz, H., & Legatiuk, D, Peralta Abadía, J. J., Walther. C., & Smarsly, K. (2022). Explainable Artificial Intelligence to Advance Structural Health Monitoring. pp 331–346

[60] Matin SS, Pradhan B. Challenges and limitations of earthquake-induced building damage mapping techniques using remote sensing images-a systematic review. Geocarto International, 2022, 37: 6186-6212.

[61] Miao Z, Ji X, Okazaki T, Takahashi N. Pixel-level multicategory detection of visible seismic damage of reinforced concrete components. Computer-Aided Civil and Infrastructure Engineering, 2021, 36: 620-637.

[62] Miao Z, Ji X, Wu M, Gao X. Deep learning-based evaluation for mechanical property degradation of seismically damaged RC columns. Earthquake Engineering & Structural Dynamics, 2023, 52: 2498-2519.

[63] Ministry of Housing and Urban-Rural Development of the People’s Republic of China. (2010). GB 50011-2010: Code for Seismic Design of Buildings. Beijing, China.

[64] Ogunjinmi PD, Park SS, Kim B, Lee DE. Rapid post-earthquake structural damage assessment using convolutional neural networks and transfer learning. Sensors, 2022, 22: 3471.

[65] Oktavianus A, Chen PH, Lin JJ. Intelligent post-earthquake building recovery system: A framework combining BIM and deep learning. Journal of Building Engineering, 2024, 98. ArticleID: 111366

[66] Page, M. J., McKenzie, J. E., Bossuyt, P. M., Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A.., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … David Moher. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ; 372:n71. https://doi.org/10.1136/bmj.n71

[67] Pan SJ, Yang Q. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 2010, 22: 1345-1359.

[68] Pan X, Yang TY. Postdisaster image-based damage detection and repair cost estimation of reinforced concrete buildings using dual convolutional neural networks. Computer-Aided Civil and Infrastructure Engineering, 2020, 35: 495-510.

[69] Peng Z, Li J, Hao H, Zhong Y. Smart structural health monitoring using computer vision and edge computing. Engineering Structures, 2024, 319. ArticleID: 118809

[70] Perez H, Tah JHM, Mosavi A. Deep learning for detecting building defects using convolutional neural networks. Sensors, 2019.

[71] Petersen K, Vakkalanka S, Kuzniarz L. Guidelines for conducting systematic mapping studies in software engineering: An update. Information and Software Technology, 2015, 64: 1-18.

[72] Qayyum W, Ehtisham R, Bahrami A, Charles C, Mir J, Ahmad A. Assessment of convolutional neural network pre-trained models for detection and orientation of cracks. Materials, 2023, 16: 826.

[73] Qiu S, Malik M, Ehsan H, et al.. Trends and perspectives in structural health monitoring through edge computing: A review with zero-shot natural language processing categorization. Journal of Railway Science and Technology, 2025, 1: 59-74.

[74] Sandelowski M, Barroso J, Voils CI. Using qualitative metasummary to synthesize qualitative and quantitative descriptive findings. Research in Nursing & Health, 2007, 30: 99-111.

[75] Shorten C, Khoshgoftaar TM. A survey on image data augmentation for deep learning. Journal of Big Data, 2019, 6: 60.

[76] Spencer BF, Hoskere V, Narazaki Y. Advances in computer vision-based civil infrastructure inspection and monitoring. Engineering, 2019, 5: 199-222.

[77] Spencer BF, Sim S-H, Kim RE, Yoon H. Advances in artificial intelligence for structural health monitoring: A comprehensive review. KSCE Journal of Civil Engineering, 2025, 29. ArticleID: 100203

[78] Standardization Administration of China. (2009). GB/T 24335-2009: Classification of earthquake damage to buildings and special structures. China.

[79] Szeliski R. Computer vision, 2022Springer International Publishing.

[80] Tavasoli S, Pan X, Yang TY. Real-time autonomous indoor navigation and vision-based damage assessment of reinforced concrete structures using low-cost nano aerial vehicles. Journal of Building Engineering, 2023, 68. ArticleID: 106193

[81] Thomas J, Harden A. Methods for the thematic synthesis of qualitative research in systematic reviews. BMC Medical Research Methodology, 2008, 8. ArticleID: 45

[82] Varma S, Simon R. Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics, 2006, 7. ArticleID: 91

[83] Wang H, Barone G, Smith A. Current and future role of data fusion and machine learning in infrastructure health monitoring. Structure and Infrastructure Engineering, 2024, 20: 1853-1882.

[84] Wang L, Liang Y, Yan S. Post-earthquake damage detection and safety assessment of the ceiling panoramic area in large public buildings using image stitching. Buildings, 2025, 15. ArticleID: 3922

[85] Wang N, Zhao Q, Li S, Zhao X, Zhao P. Damage classification for masonry historic structures using convolutional neural networks based on still images. Computer-Aided Civil and Infrastructure Engineering, 2018, 33: 1073-1089.

[86] Wang R, Shao Y, Li Q, Li L, Li J, Hao H. A novel transformer-based semantic segmentation framework for structural condition assessment. Structural Health Monitoring, 2024, 23: 1170-1183.

[87] Wang S, Wang W, Hou H, Yang Z. Computer vision-aided damage assessment of corrugated steel plate shear links under earthquake excitations. Engineering Structures, 2025, 332. ArticleID: 120023

[88] Wang Y, Jing X, Xu Y, Cui L, Zhang Q, Li H. Geometry-guided semantic segmentation for post-earthquake buildings using optical remote sensing images. Earthquake Engineering & Structural Dynamics, 2023, 52: 3392-3413.

[89] Wen W, Xu T, Hu J, Ji D, Yue Y, Zhai C. Seismic damage recognition of structural and non-structural components based on convolutional neural networks. Journal of Building Engineering, 2025, 102. ArticleID: 112012

[90] Xu Y, Bao Y, Zhang Y, Li H. Attribute-based structural damage identification by few-shot meta learning with inter-class knowledge transfer. Structural Health Monitoring, 2021, 20: 1494-1517.

[91] Xu Y, Fan Y, Bao Y, Li H. Few-shot learning for structural health diagnosis of civil infrastructure. Advanced Engineering Informatics, 2024, 62. ArticleID: 102650

[92] Xu Y, Li Y, Zheng X, Zheng X, Zhang Q. Computer-vision and machine-learning-based seismic damage assessment of reinforced concrete structures. Buildings, 2023, 13: 1258.

[93] Xu Y, Qiao W, Zhao J, Zhang Q, Li H. Vision-based multi-level synthetical evaluation of seismic damage for RC structural components: A multi-task learning approach. Earthquake Engineering and Engineering Vibration, 2023, 22: 69-85.

[94] Xu Y, Zhang C, Li H. Transformer-based large vision model for universal structural damage segmentation. Automation in Construction, 2025, 176. ArticleID: 106256

[95] Ye J, Yu H, Liu G, Zhou J, Shu J. Component identification and depth estimation for structural images based on multi-scale task interaction network. Buildings, 2024, 14. ArticleID: 983

[96] Yeum CM, Dyke SJ, Ramirez J. Visual data classification in post-event building reconnaissance. Engineering Structures, 2018, 155: 16-24.

[97] Yilmaz M, Dogan G, Arslan MH, Ilki A. Categorization of post-earthquake damages in RC structural elements with deep learning approach. Journal of Earthquake Engineering, 2024, 28: 2620-2651.

[98] Zhou, Y., Zhang, H., Huang, X., Yang, S., Babar, M. A., & Tang, H. (2015). Quality assessment of systematic reviews in software engineering: a tertiary study. In: Proceedings of the 19th International Conference on Evaluation and Assessment in Software Engineering. Association for Computing Machinery, New York, NY, USA.

[99] Zou Z, Yang S, Wang M, Song B. A damage assessment method for masonry structures based on multi scale channel shuffle dilated convolution and ReZero-Transformer. Journal of Building Engineering, 2025, 103. ArticleID: 112002

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