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 & Innovation | Caravel Press
AI-enhanced damage detection in jack-up rig legs using an improved modal strain energy index: A numerical, experimental, and digital twin-based approach
, ,
1World Society of Sustainable Energy Technologies, Nottingham, United Kingdom
2Department of Marine Industries, Science and Research Branch, Islamic Azad University, Tehran, Iran
Jack-up rigs work in challenging marine environments where conditions such as cyclic loading, corrosion, and hydrodynamic forces can quickly cause structural damage. This deterioration not only affects the safety of the operation but also shortens the rig’s service life. To address these issues, this study proposes a new way to monitor the health of these structures using artificial intelligence (AI). The approach combines the improved modal strain energy (IMSE) index with machine learning to improve how damage in the legs of the rigs is detected. Unlike traditional methods that often rely on static thresholds to identify problems, the proposed system uses AI and digital twin technology to provide more flexible and timely diagnostics, which ultimately helps with better maintenance planning and safer operations. Validation was conducted through a combination of finite element modeling and experimental testing using a 1:22 scale laboratory prototype of the SA20 jack-up rig. The results show that the AI-driven IMSE method performs better than the traditional Stubbs Index, improving accuracy by 12.5%. It also outperforms the standard IMSE method, boosting damage detection by 8.7%. Remarkably, this approach can identify damage as small as 1%, with an average deviation of <4%. On top of that, the framework has shown potential in improving multi-damage localization reliability and cutting down on false positives. This structural health monitoring (SHM) approach integrates real-time sensor data with deep learning algorithms and digital twin simulations to provide a highly scalable and adaptive solution for offshore structural integrity monitoring. The solution applies to offshore wind turbines, floating platforms, subsea pipelines, in addition to jack-up rigs, securing the long-term resilience of valuable marine infrastructure. Thus, emphasizes that AI has a truly transformational role in offshore SHM, ushering in maintenance that is intelligent, reliable, and cost-effective, especially under extreme marine conditions.
Black IM, Yete B, Häckell MW, Kolios A. Assessing structural homogeneity and heterogeneity in offshore wind farms: A population-based structural health monitoring approach. Ocean Eng. 2024;311(1):118842. doi: 10.1016/j.oceaneng.2024.118842
Xu JT, Luo L, Saw J, et al. Structural health monitoring of offshore wind turbines using distributed acoustic sensing (DAS). J Civil Struct Health Monit. 2024;15:445-463. doi: 10.1007/s13349-024-00883-w
Sahu SK, Kumar V, Dutta SC, Sarkar R, Bhattacharya S, Debnath P. Structural safety of offshore wind turbines: Present state of knowledge and future challenges. Ocean Eng. 2024;309(1):118383. doi: 10.1016/j.oceaneng.2024.118383
Yang Y, Liang F, Zhu Q, Zhang H. An overview on structural health monitoring and fault diagnosis of offshore wind turbine support structures. J Mar Sci Eng. 2024;12(3):377. doi: 10.3390/jmse12030377
Bailey H, Brookes KL, Thompson PM. Assessing environmental impacts of offshore wind farms: Lessons learned and recommendations for the future. Aquatic Biosyst. 2014;10(1):8. doi: 10.1186/2046-9063-10-8
Global Wind Energy Council. Record 6.1 GW of New Offshore Wind Capacity Installed Globally in 2019. GWEC. Available from: https://gwec.net/record-6-1-gw-of-new-offshore-wind-capacity-installed-globally-in-2019 [Last accessed on 2020 Feb 24].
Health and Safety Executive. Offshore Hydrocarbon Release Statistics and Analysis 1992-2015. Bootle, UK: Health and Safety Executive; 2016.
Devriendt C, Magalhães F, Guillaume P. Structural health monitoring of offshore wind turbines using automated operational modal analysis. Struct Health Monit. 2014;13(6):644-659. doi: 10.1177/1475921714556568
Weijtjens W, Verbelen T, Capello E, Devriendt C. Vibration- based structural health monitoring of the substructures of five offshore wind turbines. Proced Eng. 2017;199:2294-2299. doi: 10.1016/j.proeng.2017.09.353
Li Y, Wang S, Zhang M, Zheng C. An improved modal strain energy method for damage detection in offshore platform structures. J Marine Sci Appl. 2016;15(2):182-192. doi: 10.1007/s11804-016-1350-0
Nguyen CU, Huynh TC, Kim JT. Vibration-based damage detection in wind turbine towers using artificial neural networks. Struct Monit Maint. 2018;5(4):507-519. doi: 10.12989/smm.2018.5.4.507
Tong Y, Liu W, Liu X, et al. Materials design and structural health monitoring of horizontal axis offshore wind turbines: A state-of-the-art review. Materials. 2025;18(2):329. doi: 10.3390/ma18020329
Samaei SR, Riffat J. Intelligent structural health monitoring of jack-up platform legs using high-density sensor networks, real-time digital twins, and machine learning-based damage detection. Fut Cities Environ. 2025;11. doi: 10.70917/fce-2025-031
Rolfes R, Zerbst S, Haake G, Reetz J, Lynch JP. Integral SHM-system for offshore wind turbines using smart wireless sensors. In: Proceedings of the 6th International Workshop on Structural Health Monitoring. Stanford, CA: Springer; 2007. p. 1-8.
Janda I. Directional Wave Data for Structural Health Monitoring of Offshore Wind Farms. Miros Cloud; 2024. Available from: https://www.miros-group.com
Samaei SR, Ghodsi Hassanabad M, Asadian Ghahfarrokhi M, Ketabdari MJ. Numerical and experimental investigation of damage in environmentally-sensitive civil structures using modal strain energy (case study: LPG wharf). Int J Environ Sci Technol. 2021;18:1939-1952. doi: 10.1007/s13762-021-03321-2
Melchers RE. Probabilistic model for marine corrosion of steel for structural reliability assessment. J Struct Eng. 2003;129(11):1484-1493. doi: 10.1061/(ASCE)0733-9445(2003)129:11(1484)
Apolinario M, Coutinho R. Understanding the biofouling of offshore and deep-sea structures. In: Hellio C, Yebra D, editors. Advances in Marine Antifouling Coatings and Technologies. Delhi: Woodhead Publishing; 2009. p. 132-147. doi: 10.1533/9781845696313.1.132
Bouty C, Schafhirt S, Ziegler L, Muskulus M. Lifetime extension for large offshore wind farms: Is it enough to reassess fatigue for selected design positions? Energy Proced. 2017;137:523-530. doi: 10.1016/j.egypro.2017.10.377
American Petroleum Institute. Recommended Practice for Planning, Designing, and Constructing Fixed Offshore Platforms-Working Stress Design. 21st ed. United States: American Petroleum Institute; 2005.
How to cite this article
Riffat, J., Doudran, H.A. and Samaei, S.R. (2025). '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 & Innovation, 1, pp. 1-10. https://doi.org/10.36922/GTI025340014
