3066-280XISSN (Online)
Volume 5 , Issue 2 , PP: 53-91, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Ahmed Mohamed Zaki 1 * , Hala B. Nafea 2 , Hossam El-Din Moustafa 3 , El-Sayed M. El-Kenawy 4
Doi: https://doi.org/10.54216/MOR.050204
This comprehensive review examines the current state of artificial intelligence and computational optimization techniques applied to earthquake engineering challenges. The paper systematically analyzes recent advances across three primary domains: machine learning (ML), deep learning (DL), and optimization methods, each contributing distinct capabilities to seismic hazard mitigation. Through an extensive analysis of peer-reviewed studies, this review synthesizes methodologies employed in earthquake prediction, early warning systems, structural damage assessment, emergency response optimization, and seismic hazard analysis. Machine learning approaches have demonstrated significant effectiveness in liquefaction prediction, slope displacement analysis, and seismic event classification, with models such as XG Boost and Random Forest achieving high predictive accuracy. Deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based models, have revolutionized real-time earthquake detection, P-wave recognition, and landslide susceptibility mapping, with several models achieving accuracy rates exceeding 90%. Optimization techniques, particularly metaheuristic algorithms like Particle Swarm Optimization (PSO) and Gray Wolf Optimizer (GWO), have proven valuable for emergency logistics, shelter allocation, and structural design optimization. The review reveals current trends toward hybrid frameworks integrating multiple computational approaches, enhanced model interpretability, and real-time implementation capabilities. Future research directions emphasize the development of uncertainty-aware models, scalable frameworks for global application, and integration of social and economic factors in disaster preparedness strategies. This review provides researchers and practitioners with a structured understanding of computational methodologies in earthquake engineering and identifies critical gaps requiring further investigation.
Earthquake engineering , Machine learning , Deep learning , Optimization algorithms , Seismic hazard assessment , Early warning systems , Structural damage prediction , Metaheuristic algorithms , Artificial intelligence
[1] V. Macchiarulo, G. Giardina, P. Milillo, Y. D. Aktas, and M. R. Z. Whitworth, “Integrating post-event very high resolution sar imagery and machine learning for building-level earthquake damage assessment.,” Bulletin of earthquake engineering, vol. 23, no. 12, pp. 5021–5047, 2025. DOI: 10 . 1007/s10518-024-01877-1.
[2] N. Alsaaran and A. Soudani, “Deep learning image-based classification for post-earthquake damage level prediction using uavs.,” Sensors (Basel, Switzerland), vol. 25, no. 17, 2025. DOI: 10.3390/ s25175406.
[3] W.-Y. Liao et al., “Recipe for regular machine learning-based earthquake cataloging: A systematic examination in new zealand, from local to regional scale,” Copernicus Publications, DOI: 10.5194/ egusphere-egu24-14134.
[4] J. Zhang, A. Kato, H. Zhu, and J. Zhang, “Small earthquake location via machine learning with insufficient data,” Earth, Planets and Space, vol. 77, no. 1, p. 133, 2025. DOI: 10.1186/s40623- 025-02258-x.
[5] K. C. Onyelowe, V. Kamchoom, T. Gnananandarao, and K. P. Arunachalam, “Developing data driven framework to model earthquake induced liquefaction potential of granular terrain by machine learning classification models.,” Scientific reports, vol. 15, no. 1, p. 21 509, 2025. DOI: 10.1038/s41598- 025-07494-5.
[6] J. Wang, N. M. Shahani, X. Zheng, J. Hongwei, and X. Wei, “Machine learning-based analyzing earthquake-induced slope displacement.,” PloS one, vol. 20, no. 2, e0314977, 2025. DOI: 10.1371/ journal.pone.0314977.
[7] F. Catani, “Deep learning unlocks global prediction of earthquake-triggered landslides.,” National science review, vol. 12, no. 8, nwaf282, 2025. DOI: 10.1093/nsr/nwaf282.
[8] X. Fan et al., “Deep learning can predict global earthquake-triggered landslides.,” National science review, vol. 12, no. 7, nwaf179, 2025. DOI: 10.1093/nsr/nwaf179.
[9] C. Costa and V. Silva, “Estimating road disruptions in urban contexts due to earthquakes using machine learning surrogates,” Earthquake Engineering & Structural Dynamics, vol. 54, no. 7, pp. 1799–1818, 2025. DOI: 10.1002/eqe.4318.
[10] S. Aslan and M. Yildirim, “A novel attention-based deep learning model for improving sentiment classification after the case of the 2023 kahramanmaras/turkey earthquake on twitter.,” PeerJ. Computer science, vol. 11, e2881, 2025. DOI: 10.7717/peerj-cs.2881.
[11] K. C. Onyelowe, V. Kamchoom, T. Gnananandarao, and K. P. Arunachalam, “Developing data driven framework to model earthquake induced liquefaction potential of granular terrain by machine learning classification models.,” Scientific reports, 2025. DOI: 10.1038/s41598-025-07494-5.
[12] J. Wang, N. M. Shahani, X. Zheng, J. Hongwei, and X. Wei, “Machine learning-based analyzing earthquake-induced slope displacement.,” PloS one, 2025. DOI: 10 . 1371 / journal . pone . 0314977.
[13] S. H. Ye, S. K. Y. Lui, and R. P. Young, “Insights on earthquake nucleation revealed by numerical simulation and unsupervised machine learning of laboratory-scale earthquake.,” Scientific reports, 2024. DOI: 10.1038/s41598-024-80136-4.
[14] C. E. Yavas, L. Chen, C. Kadlec, and Y. Ji, “Improving earthquake prediction accuracy in los angeles with machine learning.,” Scientific reports, 2024. DOI: 10.1038/s41598-024-76483-x.
[15] J. N. Ng’ombe, K. N. Addai, A. Mzyece, J. Han, and O. Temoso, “Uncovering the factors that affect earthquake insurance uptake using supervised machine learning.,” Scientific reports, 2023. DOI: 10. 1038/s41598-023-48568-6.
[16] C. Chi, C. Li, Y. Han, Z. Yu, X. Li, and D. Zhang, “Pre-earthquake anomaly extraction from borehole strain data based on machine learning.,” Scientific reports, 2023. DOI: 10.1038/s41598- 023- 47387-z.
[17] E. A. M. Alcantara and T. Saito, “Machine learning-based rapid post-earthquake damage detection of rc resisting-moment frame buildings.,” Sensors (Basel, Switzerland), 2023. DOI: 10 . 3390 / s23104694.
[18] J. B. Rundle et al., “Optimizing earthquake nowcasting with machine learning: The role of strain hardening in the earthquake cycle.,” Earth and space science (Hoboken, N.J.), 2022. DOI: 10.1029/ 2022EA002343.
[19] M. A. Murti, R. Junior, A. N. Ahmed, and A. Elshafie, “Earthquake multi-classification detection based velocity and displacement data filtering using machine learning algorithms.,” Scientific reports, 2022. DOI: 10.1038/s41598-022-25098-1.
[20] B. Gomez and U. Kadri, “Earthquake source characterization by machine learning algorithms applied to acoustic signals.,” Scientific reports, 2021. DOI: 10.1038/s41598-021-02483-w.
[21] G. C. Beroza, M. Segou, and S. M. Mousavi, “Machine learning and earthquake forecasting-next steps.,” Nature communications, 2021. DOI: 10.1038/s41467-021-24952-6.
[22] P. Xiong et al., “Towards advancing the earthquake forecasting by machine learning of satellite data.,” The Science of the total environment, 2021. DOI: 10.1016/j.scitotenv.2021.145256.
[23] P. A. Johnson et al., “Laboratory earthquake forecasting: A machine learning competition.,” Proceedings of the National Academy of Sciences of the United States of America, 2021. DOI: 10.1073/pnas. 2011362118.
[24] R. Jena et al., “Earthquake hazard and risk assessment using machine learning approaches at palu, indonesia.,” The Science of the total environment, 2020. DOI: 10.1016/j.scitotenv.2020. 141582.
[25] Y. Li et al., “Exploring memory function in earthquake trauma survivors with resting-state fmri and machine learning.,” BMC psychiatry, 2020. DOI: 10.1186/s12888-020-2452-5.
[26] I. Khan, S. Choi, and Y.-W. Kwon, “Earthquake detection in a static and dynamic environment using supervised machine learning and a novel feature extraction method.,” Sensors (Basel, Switzerland), 2020. DOI: 10.3390/s20030800.
[27] F. Ge, Y. Li, M. Yuan, J. Zhang, and W. Zhang, “Identifying predictors of probable posttraumatic stress disorder in children and adolescents with earthquake exposure: A longitudinal study using a machine learning approach.,” Journal of affective disorders, 2019. DOI: 10.1016/j.jad.2019.11.079.
[28] J. Zhang, A. Kato, H. Zhu, and J. Zhang, “Small earthquake location via machine learning with insufficient data,” BioMed Central, vol. 77, no. 1, p. 133, 2025.
[29] M. Senkaya, S. E. Akhanlı, A. Silahtar, and H. Karaaslan, “Modelling the importance of ground and strong-motion variables on the damage status in the 2023 kahramanmara¸s earthquakes using supervised machine learning,” Wiley-Blackwell, vol. 12, no. 4, e70020, 2025. DOI: 10.1002/gdj3.70020.
[30] R. Fonzetti, A. Govoni, P. De Gori, L. Valoroso, and C. Chiarabba, “Machine learning-based high-resolution data set for the 2009 l’aquila earthquake sequence,” Oxford University Press, vol. 243, no. 1, ggaf286, 2025. DOI: 10.1093/gji/ggaf286.
[31] X. Fan et al., “Deep learning can predict global earthquake-triggered landslides.,” National science review, 2025. DOI: 10.1093/nsr/nwaf179.
[32] R. Jadeja, T. Trivedi, and J. Surve, “Survivor detection approach for post earthquake search and rescue missions based on deep learning inspired algorithms.,” Scientific reports, 2024. DOI: 10 . 1038 / s41598-024-75156-z.
[33] Y. Liu, Q. Zhao, and Y. Wang, “Peak ground acceleration prediction for on-site earthquake early warning with deep learning.,” Scientific reports, 2024. DOI: 10.1038/s41598-024-56004-6.
[34] J. Seo, Y. Kim, J. Ha, D. Kwak, M. Ko, and M. Yoo, “Unsupervised anomaly detection for earthquake detection on korea high-speed trains using autoencoder-based deep learning models.,” Scientific reports, 2024. DOI: 10.1038/s41598-024-51354-7.
[35] P. K. E. S, V. N. Thatha, G. Mamidisetti, S. V. Mantena, P. Chintamaneni, and R. Vatambeti, “Hybrid deep learning model with enhanced sunflower optimization for flood and earthquake detection.,” Heliyon, 2023. DOI: 10.1016/j.heliyon.2023.e21172.
[36] H. Mohammadigheymasi et al., “A data set of earthquake bulletin and seismic waveforms for ghana obtained by deep learning.,” Data in brief, 2023. DOI: 10.1016/j.dib.2023.108969.
[37] S Mancini, M Segou, M. J. Werner, T Parsons, G Beroza, and L Chiaraluce, “On the use of high-resolution and deep-learning seismic catalogs for short-term earthquake forecasts: Potential benefits and current limitations.,” Journal of geophysical research. Solid earth, 2023. DOI: 10.1029/ 2022JB025202.
[38] L. Yang, X. Liu, W. Zhu, L. Zhao, and G. C. Beroza, “Toward improved urban earthquake monitoring through deep-learning-based noise suppression.,” Science advances, 2022. DOI: 10.1126/sciadv. abl3564.
[39] A. Shaheen, U. B. Waheed, M. Fehler, L. Sokol, and S. Hanafy, “Groningennet: Deep learning for low-magnitude earthquake detection on a multi-level sensor network.,” Sensors (Basel, Switzerland), 2021. DOI: 10.3390/s21238080.
[40] Z. Bao, J. Zhao, P. Huang, S. Yong, and X. Wang, “A deep learning-based electromagnetic signal for earthquake magnitude prediction.,” Sensors (Basel, Switzerland), 2021. DOI: 10.3390/s21134434.
[41] W. Kuang, C. Yuan, and J. Zhang, “Real-time determination of earthquake focal mechanism via deep learning.,” Nature communications, 2021. DOI: 10.1038/s41467-021-21670-x.
[42] S. M. Mousavi, W. L. Ellsworth, W. Zhu, L. Y. Chuang, and G. C. Beroza, “Earthquake transformer-an attentive deep-learning model for simultaneous earthquake detection and phase picking.,” Nature communications, 2020. DOI: 10.1038/s41467-020-17591-w.
[43] L. Seydoux, R. Balestriero, P. Poli, M. de Hoop, M. Campillo, and R. Baraniuk, “Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning.,” Nature communications, 2020. DOI: 10.1038/s41467-020-17841-x.
[44] R. Jena, B. Pradhan, A. Al-Amri, C. W. Lee, and H.-J. Park, “Earthquake probability assessment for the indian subcontinent using deep learning.,” Sensors (Basel, Switzerland), 2020. DOI: 10.3390/ s20164369.
[45] J. Chung, M. Manga, T. Kneafsey, T. Mukerji, and M. Hu, “Deep learning forecasts the spatiotemporal evolution of fluid-induced microearthquakes,” Nature Publishing Group, vol. 6, no. 1, p. 643, 2025. DOI: 10.1038/s43247-025-02644-z.
[46] Y. Zhang, X. Li, X. Zeng, J. Liu, and C. Bai, “Uncertainty-aware deep learning methods for robust discrimination between earthquakes and explosions,” Oxford University Press, vol. 243, no. 1, ggaf303, 2025. DOI: 10.1093/gji/ggaf303.
[47] Z. Bai et al., “Rapid detection and segmentation of landslide hazards in loess tableland areas using deep learning: A case study of the 2023 jishishan ms 6.2 earthquake in gansu, china,” MDPI, vol. 17, no. 15, p. 2667, 2025. DOI: 10.3390/rs17152667.
[48] Z. Jiang, Z. Zhu, G. Lacidogna, L. F. Friedrich, and I. Iturrioz, “Earthquake precursors based on rock acoustic emission and deep learning,” MDPI, vol. 7, no. 3, p. 103, 2025. DOI: 10 . 3390 / sci7030103.
[49] S. Aslan and M. Yildirim, “A novel attention-based deep learning model for improving sentiment classification after the case of the 2023 kahramanmaras/turkey earthquake on twitter.,” PeerJ. Computer science, 2025. DOI: 10.7717/peerj-cs.2881.
[50] D. Zhexebay et al., “Deep learning for early earthquake detection: Application of convolutional neural networks for p-wave detection,” Applied Sciences, vol. 15, no. 7, p. 3864, Jan. 2025, Publisher: Multidisciplinary Digital Publishing Institute, ISSN: 2076-3417. DOI: 10 . 3390 / app15073864. Accessed: Oct. 13, 2025. [Online]. Available: https://www.mdpi.com/2076-3417/15/7/ 3864.
[51] H. Yang, P. Zhang, P. Zhang, C. Zhang, and X. Yan, “Optimization of a two-stage emergency logistics system considering public psychological risk perception under earthquake disaster.,” Scientific reports, 2025. DOI: 10.1038/s41598-024-83670-3.
[52] R. Aghataher, H. Rabieifar, N. N. Samany, and H. Rezayan, “The suitability mapping of an urban spatial structure for earthquake disaster response using a gradient rain optimization algorithm (groa).,” Heliyon, 2023. DOI: 10.1016/j.heliyon.2023.e20525.
[53] P. K. E. S, V. N. Thatha, G. Mamidisetti, S. V. Mantena, P. Chintamaneni, and R. Vatambeti, “Hybrid deep learning model with enhanced sunflower optimization for flood and earthquake detection.,” Heliyon, 2023. DOI: 10.1016/j.heliyon.2023.e21172.
[54] Y. Zhou, Y. Gong, and X. Hu, “Robust optimization for casualty scheduling considering injury deterioration and point-edge mixed failures in early stage of post-earthquake relief.,” Frontiers in public health, 2023. DOI: 10.3389/fpubh.2023.995829.
[55] Z. Wei, X.-A. Zhang, F. Sun, and W. Y. Wang, “Digital twin assistant active design and optimization of steel mega-sub controlled structural system under severe earthquake waves.,” Materials (Basel, Switzerland), 2022. DOI: 10.3390/ma15186382.
[56] H. Zhang, F. Wang, H. Tang, and Y. Dong, “An optimization-based approach to social network group decision making with an application to earthquake shelter-site selection.,” International journal of environmental research and public health, 2019. DOI: 10.3390/ijerph16152740.
[57] K. Lee, J. Oh, H. Lee, and K. You, “Earthquake magnitude estimation using a total noise enhanced optimization model.,” Sensors (Basel, Switzerland), 2019. DOI: 10.3390/s19061454.
[58] X. Zhao, W. Xu, Y. Ma, and F. Hu, “Scenario-based multi-objective optimum allocation model for earthquake emergency shelters using a modified particle swarm optimization algorithm: A case study in chaoyang district, beijing, china.,” PloS one, 2015. DOI: 10.1371/journal.pone.0144455.
[59] C. Wang, X. Zhang, X. Wang, and G. Chang, “Prediction of earthquake death toll based on principal component analysis, improved whale optimization algorithm, and extreme gradient boosting,” MDPI, vol. 15, no. 15, p. 8660, 2025. DOI: 10.3390/app15158660.
[60] N. Djafar-Henni, N. Djedoui, R. Chebili, G. Bekdas, and S. Nigdeli, “Intelligent shear wall optimization in reinforced concrete buildings subjected to earthquake excitations,” Wiley-Blackwell, vol. 34, no. 10, e70054, 2025. DOI: 10.1002/tal.70054.
