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This paper reviews recent advances in applying artificial intelligence (AI) in structural engineering, with a particular focus on methods, challenges, and opportunities. It examines how machine learning, deep learning, and evolutionary algorithms are transforming traditional design, analysis, and maintenance processes. The integration of AI with building information modelling (BIM), digital twins, and sensor-based monitoring systems is creating a more data-driven engineering environment. However, widespread adoption remains limited by issues such as data quality, model transparency, and a lack of validation frameworks. The study highlights the need for explainable models, ethical oversight, and standardised data practices to ensure the safe and reliable use of AI in structural applications.
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Akbari, P., Zamani, M. & Mostafaei, A. (2024). Machine learning prediction of mechanical properties in metal additive manufacturing. Additive Manufacturing, 19, 104320. https://doi.org/10.1016/j.addma.2024.104320
Al-Mattarneh, H., Ismail, R., Trrad, I., Nimer, H., Khodier, M., Jaradat, Y., Malkawi, A. B. & Mohammed, B. S. (2025). Using artificial neural networks and electromagnetic capacitive NDT sensors for wood engineering application. HBRC Journal, 21 (1), 211–233. https://doi.org/10.1080/16874048.2025.2471122
Ao, Y., Li, S. & Duan, H. (2025). Artificial Intelligence-Aided Design (AIAD) for Structures and Engineering: A State-of-the-Art Review and Future Perspectives. Archives of Computational Methods in Engineering, 32 (7), 4197–4224. https://doi.org/10.1007/s11831-025-10264-1
Asadi, K., Ramshankar, H., Pullagurla, H., Bhandare, A., Shanbhag, S., Mehta, P., Kundu, S., Han, K., Lobaton, E. & Wu, T. (2018, July 22). Building an Integrated Mobile Robotic System for Real-Time Applications in Construction. 2018 In Proceedings of the 35th ISARC, Berlin, Germany (pp. 453–461). The International Association for Automation and Robotics in Construction. https://doi.org/10.22260/ISARC2018/0063
Asgarkhani, N., Kazemi, F., Jakubczyk-Gałczyńska, A., Mohebi, B., & Jankowski, R. (2024). Seismic response and performance prediction of steel buckling-restrained braced frames using machine-learning methods. Engineering Applications of Artificial Intelligence, 128, 107388. https://doi.org/10.1016/j.engappai.2023.107388
Azanaw, Mr. G. M. (2024). Revolutionizing Structural Engineering: A Review of Digital Twins, BIM, and AI Applications. Indian Journal of Structure Engineering, 4 (2), 1–8. https://doi.org/10.54105/ijse.B1321.04021124
Aziz, Md. T., Osabel, D. M., Kim, Y., Kim, S., Bae, J. & Tsavdaridis, K. D. (2025). State-of-the-art artificial intelligence techniques in structural engineering: A review of applications and prospects. Results in Engineering, 28, 107882. https://doi.org/10.1016/j.rineng.2025.107882
Badeka, E., Muchla, A., Koulalis, I., Ioannidis, K., Dymarski, P. & Vrochidis, S. (2025). Ladder Walking Detection via Action Recognition for Enhancing Worker Safety in Construction. In 2025 IEEE International Workshop on Metrology for Living Environment (MetroLivEnv) (pp. 345–349). IEEE International. https://doi.org/10.1109/MetroLivEnv64961.2025.11107036
Bahadori-Jahromi, A., Room, S., Paknahad, C., Altekreeti, M., Tariq, Z. & Tahayori, H. (2025). The Role of Artificial Intelligence and Machine Learning in Advancing Civil Engineering: A Comprehensive Review. Applied Sciences, 15 (19), 10499. https://doi.org/10.3390/app151910499
Bao, Y., Sun, H., Xu, Y., Guan, X., Pan, Q. & Liu, D. (2025). Recent advances in structural health diagnosis: a machine learning perspective. Advances in Bridge Engineering, 6 (1), 7. https://doi.org/10.1186/s43251-024-00155-z
Canakci, B., Liu, J., Wu, X., Cheriere, N., Costa, P., Legtchenko, S., Narayanan, D. & Rowstron, A. (2025). Good things come in small packages: Should we build AI clusters with Lite-GPUs? In Proceedings of the Workshop on Hot Topics in Operating Systems (pp. 127–135). New York: Association for Computing Machinery. https://doi.org/10.1145/3713082.3730390
Cheetham, A. K. & Seshadri, R. (2024). Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery. Chemistry of Materials, 36 (8), 3490–3495. https://doi.org/10.1021/acs.chemmater.4c00643
Clymer, J., Gabrieli, N., Krueger, D. & Larsen, T. (2024). Safety Cases: How to Justify the Safety of Advanced AI Systems. arXiv:2403.10462. https://doi.org/10.48550/arXiv.2403.10462
Dalrymple, D., Skalse, J., Bengio, Y., Russell, S., Tegmark, M., Seshia, S., Omohundro, S., Szegedy, C., Goldhaber, B., Ammann, N., Abate, A., Halpern, J., Barrett, C., Zhao, D., Zhi-Xuan, T., Wing, J. & Tenenbaum, J. (2024). Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems. arXiv:2405.06624. https://doi.org/10.48550/arXiv.2405.06624
Das, P., Kashem, A., Islam, M., Ahmed, A., Haque, M. A. & Khan, M. (2024). Alkali-activated binder concrete strength prediction using hybrid-deep learning along with shapely additive explanations and uncertainty analysis. Construction and Building Materials, 435, 136711. https://doi.org/10.1016/j.conbuildmat.2024.136711
Deng, M., Menassa, C. C. & Kamat, V. R. (2021). From BIM to digital twins: a systematic review of the evolution of intelligent building representations in the AEC-FM industry. Journal of Information Technology in Construction, 26, 58–83. https://doi.org/10.36680/j.itcon.2021.005
Fei, Y., Lu, X., Liao, W. & Guan, H. (2025). Data enhancement for generative AI design of shear wall structures incorporating structural optimization and diffusion models. Advances in Structural Engineering. https://doi.org/10.1177/13694332251353614
Felderer, M. & Ramler, R. (2021). Quality Assurance for AI-based Systems: Overview and Challenges. https://doi.org/10.1007/978-3-030-65854-0_3
Fischer, L., Ehrlinger, L., Geist, V., Ramler, R., Sobiezky, F., Zellinger, W., Brunner, D., Kumar, M. & Moser, B. (2020). AI System Engineering – Key Challenges and Lessons Learned. Machine Learning and Knowledge Extraction, 3 (1), 56–83. https://doi.org/10.3390/make3010004
Flah, M., Nunez, I., Ben Chaabene, W. & Nehdi, M. L. (2021). Machine Learning Algorithms in Civil Structural Health Monitoring: A Systematic Review. Archives of Computational Methods in Engineering, 28 (4), 2621–2643. https://doi.org/10.1007/s11831-020-09471-9
Foidl, H., Felderer, M. & Ramler, R. (2022). Data Smells: Categories, Causes and Consequences, and Detection of Suspicious Data in AI-based Systems. arXiv:2203.10384. https://doi.org/10.48550/arXiv.2203.1038
Gan, X., Wang, W., Jiang, C., Ye, L., Chen, F., Zhou, T. & Zhao, Y. (2025). Ultrasonic detection and deep learning for high-precision concrete strength prediction. Journal of Building Engineering, 104, 112372. https://doi.org/10.1016/j.jobe.2025.112372
Geyer, P., Singh, M. M. & Chen, X. (2024). Explainable AI for engineering design: A unified approach of systems engineering and component-based deep learning demonstrated by energy-efficient building design. Advanced Engineering Informatics, 62, 102843. https://doi.org/10.1016/j.aei.2024.102843
Gong, X., Zeng, D., Groeneveld-Meijer, W. & Manogharan, G. (2022). Additive manufacturing: A machine learning model of process-structure-property linkages for machining behavior of Ti-6Al-4V. Materials Science in Additive Manufacturing, 1 (1), 6. https://doi.org/10.18063/msam.v1i1.6
González-Palacio, M., García-Giraldo, J. M. & González-Palacio, L. (2025). Integrating AI and sustainable materials: machine learning approaches to wood structural behavior. Agronomy Research, 23, 2025. https://doi.org/10.15159/AR.25.073
Gómez Plaza, M., Granados Cañete, F. J., López Yajamin, E. X., Díaz Martín, D., Olalla Caballero, B. & Palma Bautista, A. M. (2025). Enhancing AI applications for European public bodies: A data quality-centric approach. International Journal of Engineering Business Management, 17. https://doi.org/10.1177/18479790251367820
Han, N. & Su, B.-L. (2025). AI-driven material discovery for energy, catalysis and sustainability. National Science Review, 12 (5). https://doi.org/10.1093/nsr/nwaf110
Harirchian, E., Kumari, V., Jadhav, K., Raj Das, R., Rasulzade, S. & Lahmer, T. (2020). A Machine Learning Framework for Assessing Seismic Hazard Safety of Reinforced Concrete Buildings. Applied Sciences, 10 (20), 7153. https://doi.org/10.3390/app10207153
Hassija, V., Chamola, V., Mahapatra, A., Singal, A., Goel, D., Huang, K., Scardapane, S., Spinelli, I., Mahmud, M. & Hussain, A. (2024). Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence. Cognitive Computation, 16 (1), 45–74. https://doi.org/10.1007/s12559-023-10179-8
He, Z., Wang, Y-H. & Zhang, J. (2025). Generative AIBIM: An automatic and intelligent structural design pipeline integrating BIM and generative AI. Information Fusion, 114, 102654. https://doi.org/10.1016/j.inffus.2024.102654
Hong, S., Kwon, Y., Shin, D., Park, J. & Kang, N. (2024). DeepJEB: 3D Deep Learning-based Synthetic Jet Engine Bracket Dataset. arXiv:2406.09047. https://doi.org/10.48550/arXiv.2406.09047
Hosseinzadeh, M., Samadvand, H., Hosseinzadeh, A., Mousavi, S. S. & Dehestani, M. (2024). Concrete strength and durability prediction through deep learning and artificial neural networks. Frontiers of Structural and Civil Engineering, 18 (10), 1540–1555. https://doi.org/10.1007/s11709-024-1124-9
Li, M., Liu, Y., Wong, B. C. L., Gan, V. J. L. & Cheng, J. C. P. (2023). Automated structural design optimization of steel reinforcement using graph neural network and exploratory genetic algorithms. Automation in Construction, 146, 104677. https://doi.org/10.1016/j.autcon.2022.104677
Lukovic, M., Ciernik, L., Müller, G., Kluser, D., Pham, T., Burgert, I. & Schubert, M. (2024). Probing the complexity of wood with computer vision: from pixels to properties. Journal of The Royal Society Interface, 21 (213). https://doi.org/10.1098/rsif.2023.0492
Luleci, F., Catbas, F. N. & Avci, O. (2023). Generative adversarial networks for labeled acceleration data augmentation for structural damage detection. Journal of Civil Structural Health Monitoring, 13 (1), 181–198. https://doi.org/10.1007/s13349-022-00627-8
Luo, D., Wang, K., Wang, D., Sharma, A., Li, W. & Choi, I. H. (2025). Artificial intelligence in the design, optimization, and performance prediction of concrete materials: a comprehensive review. Npj Materials Sustainability, 3 (1), 14. https://doi.org/10.1038/s44296-025-00058-8
Ma, J., Cao, B., Dong, S., Tian, Y., Wang, M., Xiong, J. & Sun, S. (2024). MLMD: a programming-free AI platform to predict and design materials. Npj Computational Materials, 10 (1), 59. https://doi.org/10.1038/s41524-024-01243-4
Marey, A., Arjmand, P., Alerab, A. D. S., Eslami, M. J., Saad, A. M., Sanchez, N. & Umair, M. (2024). Explainability, transparency and black box challenges of AI in radiology: impact on patient care in cardiovascular radiology. Egyptian Journal of Radiology and Nuclear Medicine, 55 (1), 183. https://doi.org/10.1186/s43055-024-01356-2
Mengesha, G. (2025). Integrating AI in Structural Health Monitoring (SHM): A Systematic Review on Advances, Challenges, and Future Directions. https://doi.org/10.2139/ssrn.5004977
Merchant, A., Batzner, S., Schoenholz, S. S., Aykol, M., Cheon, G. & Cubuk, E. D. (2023). Scaling deep learning for materials discovery. Nature, 624 (7990), 80–85. https://doi.org/10.1038/s41586-023-06735-9
Mirzaei, S., Mao, H., Al-Nima, R. R. O. & Woo, W. L. (2023). Explainable AI Evaluation: A Top-Down Approach for Selecting Optimal Explanations for Black Box Models. Information, 15 (1), 4. https://doi.org/10.3390/info15010004
Mishra, A., Gangisetti, G. & Khazanchi, D. (2023). Integrating Edge-AI in Structural Health Monitoring domain. arXiv:2304.03718. https://doi.org/10.48550/arXiv.2304.03718
Mohamed, H. S., Qiong, T., Isleem, H. F., Tipu, R. K., Shahin, R. I., Yehia, S. A., Jangir, P., Arpita & Khishe, M. (2024). Compressive behavior of elliptical concrete-filled steel tubular short columns using numerical investigation and machine learning techniques. Scientific Reports, 14 (1), 27007. https://doi.org/10.1038/s41598-024-77396-5
Muthurathinam, R. V., Alruwais, N., Al Mazroa, A. & Alkharashi, A. (2024). Optimizing concrete compressive strength prediction with a deep forest model: an advanced machine learning approach. Matéria (Rio de Janeiro), 29 (4). https://doi.org/10.1590/1517-7076-rmat-2024-0569
Okafor, C. E., Iweriolor, S., Ani, O. I., Ahmad, S., Mehfuz, S., Ekwueme, G. O., Chukwumuanya, O. E., Abonyi, S. E., Ekengwu, I. E. & Chikelu, O. P. (2023). Advances in machine learning-aided design of reinforced polymer composite and hybrid material systems. Hybrid Advances, 2, 100026. https://doi.org/10.1016/j.hybadv.2023.100026
Pakzad, S. S., Roshan, N. & Ghalehnovi, M. (2023). Comparison of various machine learning algorithms used for compressive strength prediction of steel fiber-reinforced concrete. Scientific Reports, 13 (1), 3646. https://doi.org/10.1038/s41598-023-30606-y
Parmar, D. S., Gupta, P., Chouhan, C. & Saran, H. K. (2023). Data-centric AI: Prioritizing Data Quality Over Model Complexity. International Journal of Innovative Research in Computer and Communication Engineering, 11 (07). https://doi.org/10.15680/IJIRCCE.2023.1107004
Plevris, V. & Hosamo, H. (2025). Responsible AI in structural engineering: a framework for ethical use. Frontiers in Built Environment, 11. https://doi.org/10.3389/fbuil.2025.1612575
Porter, Z., Habli, I., McDermid, J. & Kaas, M. (2024). A principles-based ethics assurance argument pattern for AI and autonomous systems. AI and Ethics, 4 (2), 593–616. https://doi.org/10.1007/s43681-023-00297-2
Quinteros-Navarro, D., Quinteros, A., Muñoz, V., Ramirez, H. & Ramirez, W. (2025). Learning Model Based on Artificial Intelligence to Determine Wood Quality: A Systematic Review. Ingénierie Des Systèmes d Information, 30 (2), 427–436. https://doi.org/10.18280/isi.300214
Ryan, P., Porter, Z., Al-Qaddoumi, J., McDermid, J. & Habli, I. (2023). What’s my role? Modelling responsibility for AI-based safety-critical systems. arXiv:2401.09459. https://doi.org/10.48550/arXiv.2401.09459
Salman, M. R., Al-Shaikhli, M., Ali Abbas, H., Ahmad, H. H. & Kudus, S. A. (2025). A critical review of deep learning applications, challenges, and future directions in structural engineering. International Journal for Computational Civil and Structural Engineering, 21 (1), 146–156. https://doi.org/10.22337/2587-9618-2025-21-1-146-156
Sherif, M., Nassar, K., Hosny, O., Safar, S. & Abotaleb, I. (2022). Automated BIM-based structural design and cost optimization model for reinforced concrete buildings. Scientific Reports, 12 (1), 21616. https://doi.org/10.1038/s41598-022-26146-6
Spencer, B. F., Sim, S.-H., Kim, R. E. & Yoon, H. (2025). Advances in artificial intelligence for structural health monitoring: A comprehensive review. KSCE Journal of Civil Engineering, 29 (3), 100203. https://doi.org/10.1016/j.kscej.2025.100203
Tyvoniuk, V., Trach, R. & Trach, Y. (2025). Integration of Probability Maps into Machine Learning Models for Enhanced Crack Segmentation in Concrete Bridges. Applied Sciences, 15 (6), 3201. https://doi.org/10.3390/app15063201
V Khadake. (2024). AI Ethics and Responsible AI Development: Navigating the Ethical Landscape of Artificial Intelligence. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10 (6), 1494–1508. https://doi.org/10.32628/CSEIT241061187
Varshney, R. K., Pandey, N. K., Pandey, A. K. & Singh, A. N. (2025). Exploring Artificial Intelligence & Machine Learning Applications in Structural Engineering Trends and Challenges. 2025 International Conference on Next Generation Information System Engineering (NGISE), 1–6. https://doi.org/10.1109/NGISE64126.2025.11085231
Wala, R. (2025). An ethical framework for AI in structural engineering: from promise to practice. The Structural Engineer, 103 (10), 10. https://doi.org/10.56330/PTFH2233
Wang, Q., Dai, Y., Ma, Z., Ni, Y., Tang, J., Xu, X. & Wu, Z. (2022). Towards probabilistic data‐driven damage detection in SHM using sparse Bayesian learning scheme. Structural Control and Health Monitoring, 29 (11). https://doi.org/10.1002/stc.3070
Xie, H., Mei, Q. & Chui, Y. H. (2025). AI applications for structural design automation. Automation in Construction, 179, 106496. https://doi.org/10.1016/j.autcon.2025.106496
Zaker Esteghamati, M., Bean, B., Burton, H. V. & Naser, M. Z. (2025). Beyond Development: Challenges in Deploying Machine-Learning Models for Structural Engineering Applications. Journal of Structural Engineering, 151 (6). https://doi.org/10.1061/JSENDH.STENG-13301
Zhang, M., Guo, T., Zhu, R., Zong, Y., Liu, Z. & Xu, W. (2023). Damage identification of seismic-isolated structure based on CAE network using vibration monitoring data. Engineering Structures, 283, 115873. https://doi.org/10.1016/j.engstruct.2023.115873
Zhang, Y., Ren, W., Chen, Y., Mi, Y., Lei, J. & Sun, L. (2024). Predicting the compressive strength of high-performance concrete using an interpretable machine learning model. Scientific Reports, 14 (1), 28346. https://doi.org/10.1038/s41598-024-79502-z
Zheng, H., Huang, Y., Wu, K., Wang, B., Hu, Q., Wang, D., Sun, B. & Antwi-Afari, M. F. (2025). A comparative analysis of explainable artificial intelligence (XAI) models for predicting concrete elastic dynamic modulus. Smart Construction. https://doi.org/10.55092/sc20250025
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