AI-Driven Digital Twin Framework for Adaptive and Real-Time Structural Health Monitoring of Offshore Marine Structures

James Riffat; Kourosh Nazari; Seyed Reza Samaei

doi:10.65582/aifsc.2026.006

Authors

Professor James Riffat World Society of Sustainable Energy Technologies, Nottingham, United Kingdom https://orcid.org/0000-0003-0374-4526
Mr. Kourosh Nazari Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran https://orcid.org/0009-0005-7325-0283
Dr. Seyed Reza Samaei Department of Marine industries, Science and Research Branch, Islamic Azad University, Tehran, Iran https://orcid.org/0000-0003-1627-1991

DOI:

https://doi.org/10.65582/aifsc.2026.006

Keywords:

Structural Health Monitoring (SHM), Digital Twin, Machine Learning, Deep Learning, Jack-Up Leg, Offshore Structures, Damage Detection

Abstract

Reliable structural health monitoring of offshore jack-up platforms is challenging due to harsh environments, measurement uncertainty, and gradual damage over time. This study proposes an integrated framework combining digital twin technology with a machine learning–based damage identification pipeline for adaptive assessment of jack-up legs. A simplified model using a ten-element Euler–Bernoulli beam represents a 124 m leg with a fixed base. Synthetic datasets were generated by introducing random stiffness reductions in elements 3–8, covering single and multiple damage scenarios with severities of 5–20%. To improve realism, environmental variability and measurement uncertainty were incorporated through temperature variations between −10°C and +30°C together with multiple levels of simulated sensor noise. The signal processing workflow involved detrending, band-pass filtering, Fast Fourier Transform analysis, and adaptive peak detection to extract modal features, including natural frequencies and spectral entropy indicators. These features were used to train a four-layer multilayer perceptron implemented in . Model performance was evaluated using five-fold stratified cross-validation. The classifier achieved an accuracy of 41.0%, a macro-F1 score of 40.1%, and a ROC AUC of 0.8155 on the test dataset, indicating reliable discrimination between healthy and damaged structural states despite environmental variability and measurement noise. In parallel, an adaptive digital twin updating procedure was implemented to refine the numerical model using modal frequency discrepancies. This updating process reduced the root mean square error of frequency prediction from 0.0596 Hz to 0.0554 Hz, corresponding to a 6.98% improvement in predictive consistency between the numerical model and the simulated structural response. The results demonstrate that coupling machine learning based damage classification with digital twin model updating provides a practical pathway toward adaptive monitoring of offshore structures.

Author Biographies

Professor James Riffat , World Society of Sustainable Energy Technologies, Nottingham, United Kingdom

Professor James Riffat is a distinguished scholar in sustainable energy, climate solutions, and global research collaboration. With extensive experience in academic leadership, he has made significant contributions to advancing sustainable energy technologies and intelligent infrastructure systems.

As the CEO of the World Society of Sustainable Energy Technologies (WSSET) and a Professor at Guangzhou Vocational University of Science and Technology, China, his research focuses on integrating cutting-edge innovations to address global challenges in energy, climate resilience, and infrastructure sustainability.

• Specialized in intelligent and innovative Structural Health Monitoring (SHM) and the digital transformation of sustainable infrastructure.
• Actively leading international collaborations in AI-driven sustainability, smart cities, and climate-adaptive systems.

Email: ceo@wsset.org

Mr. Kourosh Nazari , Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

Mr. Kourosh Nazari is a young and dedicated academic researcher at the Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran. His research focuses on the application of artificial intelligence, machine learning, and digital twin technologies in sustainable infrastructure systems.

He conducts his research and academic work under the supervision of Dr. Seyed Reza Samaei, contributing to advanced studies in structural health monitoring (SHM) and intelligent engineering solutions. His recent work explores the integration of computational modeling and AI-driven approaches for improving structural performance and resilience.

Email: k.nazari@iau.ir

Dr. Seyed Reza Samaei, Department of Marine industries, Science and Research Branch, Islamic Azad University, Tehran, Iran

Dr. Seyed Reza Samaei is a civil and coastal structural engineer with over 17 years of combined experience in academia, public-sector engineering, and international research collaboration. He holds a PhD in Coastal, Port, and Marine Structures and serves as a Faculty Member at the Science and Research Branch of Islamic Azad University.

His research focuses on AI-based Structural Health Monitoring (SHM), digital twins, and computational hydrodynamics for smart and sustainable infrastructure. With over 80 peer-reviewed publications and 3,000+ citations (h-index 44), he has contributed significantly to advancing intelligent monitoring and resilience of coastal and offshore systems. He also serves as WSSET Regional Representative, promoting innovation in sustainable engineering and digital transformation.

Email: seyedreza.samaei1984@gmail.com

samaei@srbiau.ac.ir

References

Abugu, C., Anumba, C.J. and Asare, K.A., 2024. ‘Investigation of the costs and benefits of digital twins in construction’, in Computing in Civil Engineering 2023, pp. 544–552. DOI: doi.org/10.1061/9780784485231.065.

Adreani, L., Bellini, P., Colombo, C., Fanfani, M., Nesi, P., Pantaleo, G. and Pisanu, R., 2022. ‘Digital twin framework for smart city solutions’, in The 28th International Conference on Distributed Multimedia Systems. DOI: doi.org/10.18293/DMSVIVA2022-012.

Bado, M.F. and Brownjohn, J.M.W., 2022. ‘Digital twin for civil engineering systems’, Sensors, 22(10), 3855. DOI: doi.org/10.3390/s22103855.

Bado, M.F., Tonelli, D., Poli, F., Zonta, D. and Casas, J.R., 2022. ‘Digital twin for civil engineering systems: An exploratory review for distributed sensing updating’, Sensors, 22(9), 3168. DOI: doi.org/10.3390/s22093168.

Belostotsky, A.M. and Akimov, P.A., 2016. ‘Adaptive finite element models coupled with structural health monitoring systems for unique buildings’, Procedia Engineering, 153, pp. 83–88. DOI: doi.org/10.1016/j.proeng.2016.08.084.

Broo, G.D. and Schooling, J., 2021. ‘Digital twins in infrastructure: definitions, current practices, challenges and strategies’, International Journal of Construction Management, 23(7), pp. 1254–1263. DOI: doi.org/10.1080/15623599.2021.1966980.

Callcut, M., Cerceau Agliozzo, J.-P., Varga, L. and McMillan, L., 2021. ‘Digital twins in civil infrastructure systems’, Sustainability, 13(20), 11549. DOI: doi.org/10.3390/su132011549.

Candón, E., Crespo, A., Guillén, A., Gómez, J. and López, J., 2024. ‘Asset digitalization strategy using IoT platforms and asset health model’, IFAC-PapersOnLine, 58(8), pp. 216–221. DOI: doi.org/10.1016/j.ifacol.2024.08.123.

Cheng, R., Hou, L. and Xu, S., 2023. ‘A review of digital twin applications in civil and infrastructure emergency management’, Buildings, 13, 1143. DOI: doi.org/10.3390/buildings13051143.

ElSayed, M., Foda, A. and Mohamed, M., 2024. ‘The impact of civil airspace policies on the viability of adopting autonomous unmanned aerial vehicles in last-mile applications’, Transport Policy, 145, pp. 37–54. DOI: doi.org/10.1016/j.tranpol.2023.10.002.

He, Q., Ghofrani, F., Gao, T., Wang, P., He, C., Li, Y. and Ai, C., 2022. ‘Intelligent construction for the transportation infrastructure: A review’, Intelligent Transportation Infrastructure, 1, liac007. DOI: doi.org/10.1093/iti/liac007.

Hosamo, H. and Hosamo, M., 2022. ‘Digital twin technology for bridge maintenance using 3D laser scanning: A review’, Advances in Civil Engineering, 2022, 2194949. DOI: doi.org/10.1155/2022/2194949.

Huzzat, A., Anpalagan, A., Khwaja, A.S., Woungang, I., Alnoman, A.A. and Pillai, A.S., 2025. ‘A comprehensive review of digital twin technologies in smart cities’, Digital Engineering, 4, 100040. DOI: doi.org/10.1016/j.dte.2025.100040.

Jahangir, M.F., Schultz, C.P.L. and Kamari, A., 2024. ‘A review of drivers and barriers of digital twin adoption in building project development processes’, Journal of Information Technology in Construction, 29(8), pp. 144–164. DOI: doi.org/10.36680/j.itcon.2024.008.

Jiang, F., Ma, L., Broyd, T. and Chen, K., 2021. ‘Digital twin and its implementations in the civil engineering sector’, Automation in Construction, 130, 103838. DOI: doi.org/10.1016/j.autcon.2021.103838.

Johnson, Z. and Saikia, M.J., 2024. ‘Digital twins for healthcare using wearables’, Bioengineering, 11(6), 606. DOI: doi.org/10.3390/bioengineering11060606.

Kaveh, H. and Alhajj, R., 2025. ‘Advancing civil infrastructure with digital twins: A review of applications and challenges’, Journal of Civil Engineering and Management, 31(8), pp. 828–842. DOI: doi.org/10.3846/jcem.2025.24921.

Khan, M.S., Kim, I.S. and Seo, J., 2023. ‘A boundary and voxel-based 3D geological data management system leveraging BIM and GIS’, International Journal of Applied Earth Observation and Geoinformation, 118, 103277. DOI: doi.org/10.1016/j.jag.2023.103277.

Kong, X. and Hucks, R.G., 2023. ‘Preserving our heritage: A photogrammetry-based digital twin framework for monitoring deteriorations of historic structures’, Automation in Construction, 152, 104928. DOI: doi.org/10.1016/j.autcon.2023.104928.

Mardanshahi, A., Sreekumar, A., Yang, X., Barman, S.K. and Chronopoulos, D., 2025. ‘Sensing techniques for structural health monitoring: A state-of-the-art review on performance criteria and new-generation technologies’, Sensors, 25(5), 1424. DOI: doi.org/10.3390/s25051424.

Osunsanmi, T.O., Okafor, C.C. and Aigbavboa, C.O., 2025. ‘Critical success factors for implementing smart maintenance in the fourth industrial revolution era: A bibliometric analysis within the built environment’, Journal of Facilities Management, 23(2), pp. 209–230. DOI: doi.org/10.1108/JFM-01-2023-0006.

Pang, Y., He, T., Liu, S., Zhu, X. and Lee, C., 2024. ‘Triboelectric nanogenerator-enabled digital twins in civil engineering infrastructure 4.0: A comprehensive review’, Advanced Science, 11, 2306574. DOI: doi.org/10.1002/advs.202306574.

Prasittisopin, L., 2024. ‘How 3D printing technology makes cities smarter: A review, thematic analysis, and perspectives’, Smart Cities, 7(6), pp. 3458–3488. DOI: doi.org/10.3390/smartcities7060135.

Riffat, J., Ahadpour Doudran, H. and Samaei, S.R., 2025a. ‘AI-driven digital twin for uncertainty-aware structural health monitoring of offshore wind turbines considering biofouling effects and reliability prediction’, Green Technology and Innovation, 1(2), 025330013. DOI: doi.org/10.36922/GTI025330013.

Riffat, J., Ahadpour Doudran, H. and Samaei, S.R., 2025b. ‘AI-enhanced damage detection in jack-up rig legs using an improved modal strain energy index: A numerical, experimental, and digital twin-based approach’, Green Technology and Innovation. DOI: doi.org/10.36922/GTI025340014.

Riffat, J., Samaei, S.R. and Pastakkaya, B., 2025. ‘Advances, applications, and future directions of structural health monitoring in civil infrastructure: A comprehensive review’, Energy Catalyst, 1, pp. 127–140. DOI: doi.org/10.61552/EC.2025.010.

Samaei, S.R. and Riffat, J., 2025. ‘Intelligent structural health monitoring of jack-up platform legs using high-density sensor networks, real-time digital twins, and machine learning-based damage detection’, Future Cities and Environment, 11. DOI: doi.org/10.70917/fce-2025-031.

Shokravi, H., Vafaei, M., Samali, B. and Bakhary, N., 2024. ‘In-fleet structural health monitoring of roadway bridges using connected and autonomous vehicles data’, Computer-Aided Civil and Infrastructure Engineering, 39, pp. 2122–2139. DOI: doi.org/10.1111/mice.13180.

Sun, Z., Jayasinghe, S., Sidiq, A., Shahrivar, F., Mahmoodian, M. and Setunge, S., 2025a. ‘Approach towards the development of digital twin for structural health monitoring of civil infrastructure: A comprehensive review’, Sensors, 25, 59. DOI: doi.org/10.3390/s25010059.

Sun, Z., Liang, B., Liu, S. and Liu, Z., 2025b. ‘Data and knowledge-driven bridge digital twin modeling for smart operation and maintenance’, Applied Sciences, 15(1), 231. DOI: doi.org/10.3390/app15010231.

White, S., 2025. ‘Predictive analytics for urban environments with digital twins’, Urban Planning Review.

Wynne, Z., Stratford, T. and Reynolds, T.P.S., 2022. ‘Perceptions of long-term monitoring for civil and structural engineering’, Structures, 41, pp. 1616–1623. DOI: doi.org/10.1016/j.istruc.2022.05.090.

Zain, M., Dackermann, U. and Prasittisopin, L., 2022. ‘Artificial intelligence-based seismic fragility assessment of RC buildings’, Nordic and Baltic Journal of Engineering, 22(3), pp. 365–388. DOI: doi.org/10.1515/nleng-2022-0365.

Zain, M., Dackermann, U. and Prasittisopin, L., 2024a. ‘Machine learning algorithms for seismic vulnerability assessment of school buildings in high-intensity seismic zones’, Structures, 70, 107639. DOI: doi.org/10.1016/j.istruc.2024.107639.

Zain, M., Mahmood, Q., Asim, M., Qadir, M. and Qadir, B., 2024b. ‘A hybrid machine learning framework for seismic vulnerability assessment of high-rise tubular structures’, Engineered Science, 27, 1008. DOI: doi.org/10.30919/es1008.

Zinke, T., Reymer, S., Kosse, S., Hagedorn, P., Kšnig, M., Wedel, F., Schneider, S. and Marx, S., 2023. ‘Digital twins for bridges: Concept of a modular digital twin based on the linked data approach’, in Biondini, F. and Frangopol, D. (eds.) Life-cycle of structures and infrastructure systems, pp. 214–228. DOI: doi.org/10.1201/9781003323020-214.

Zirpoli, A. and Sattamino, P., 2024. ‘A digital and interoperable support for the risk assessment of existing bridges’, Procedia Structural Integrity, 62, pp. 217–224. DOI: doi.org/10.1016/j.prostr.2024.09.036.