open access

https://hdl.handle.net/2115/100437

Surrogate-assisted reliability-based design optimization of reinforced concrete gravity-based foundations for offshore wind turbines

英語

reliability index

2026-07-01

Proceedings of the 10th International Symposium on Reliability Engineering and Risk Management (ISRERM 2026)

Given the rising demand for sustainable energy sources, optimizing the design of offshore wind turbines is becoming increasingly important to ensure their efficiency and reliability throughout their service life. Traditional reliability-based design optimization approaches are often extremely computationally intensive. To address this, the paper describes a surrogate-assisted methodology employing an artificial neural network to approximate an inverse reliability function. This approach allows for the direct identification of design parameters satisfying target reliability indicators, such as reliability indices or failure probabilities. The method's effectiveness is demonstrated through a case study of a reinforced concrete gravity-based foundation. The required areas of mild steel reinforcement and prestressing tendons are identified while adhering to reliability constraints for ultimate and serviceability limit states. Results confirm that the methodology offers a transparent and time-efficient framework for the probabilistic design and assessment of engineering structures.

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