Application of machine learning in reliability based design optimization of jacket offshore wind turbine using surrogate model

Muhammad Rizky Syarifudin; Rudi Walujo Prastianto; Ferdita Syalsabila; Hudzwah Azzahrawani Darisman; Daniel Rosyid; Zhahara Suci Pratiwi

doi:10.1063/5.0279474

Application of machine learning in reliability based design optimization of jacket offshore wind turbine using surrogate model

Muhammad Rizky Syarifudin
, Rudi Walujo Prastianto^*
, Ferdita Syalsabila
, Hudzwah Azzahrawani Darisman
, Daniel Rosyid
, Zhahara Suci Pratiwi

^*Corresponding author for this work

Institut Teknologi Sepuluh Nopember

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The supporting structure of offshore wind turbines has developed rapidly in recent years due to the demand of renewable energy technology. Compared to land-based, wind turbines prefer to be placed in the ocean due to the availability of ocean space. However, the harsh environment in the sea adds to the complexity of the structure analysis. Thus, this research aims to optimize the structure design of jacket offshore wind turbines. The main goal is to reduce the material cost. The radial basis function as a machine learning tool is applied to reduce the computational time by building the surrogate model. Then, using the constructed sampling surrogate model as a constraint, the Monte Carlo Simulation (MCS) approach is utilized to determine the probability of structural failure. The results indicate that machine learning is feasible to optimize the wind turbine supporting structure. The optimized design and reduction ratio are obtained using the reliability-based design optimization method.

Original language	English
Title of host publication	AIP Conference Proceedings
Editors	Taufiq Ilham Maulana, Satish Paudel, Edisson Alberto Moscoso Alcantara, Boon Chye Rudy Ang, Christopher Amoah, Dian M. Setiawan, Ani Hariani, Muhammad Ibnu Syamsi
Publisher	American Institute of Physics
Edition	1
ISBN (Electronic)	9780735452091
DOIs	https://doi.org/10.1063/5.0279474
Publication status	Published - 15 Jul 2025
Event	3rd International Symposium on Civil, Environmental, and Infrastructure Engineering, ISCEIE 2024 - Yogyakarta, Indonesia Duration: 7 Aug 2024 → 8 Aug 2024

Publication series

Name	AIP Conference Proceedings
Number	1
Volume	3317
ISSN (Print)	0094-243X
ISSN (Electronic)	1551-7616

Conference

Conference	3rd International Symposium on Civil, Environmental, and Infrastructure Engineering, ISCEIE 2024
Country/Territory	Indonesia
City	Yogyakarta
Period	7/08/24 → 8/08/24

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Access to Document

10.1063/5.0279474

Cite this

Syarifudin, M. R., Prastianto, R. W., Syalsabila, F., Darisman, H. A., Rosyid, D., & Pratiwi, Z. S. (2025). Application of machine learning in reliability based design optimization of jacket offshore wind turbine using surrogate model. In T. I. Maulana, S. Paudel, E. A. M. Alcantara, B. C. R. Ang, C. Amoah, D. M. Setiawan, A. Hariani, & M. I. Syamsi (Eds.), AIP Conference Proceedings (1 ed.). Article 070010 (AIP Conference Proceedings; Vol. 3317, No. 1). American Institute of Physics. https://doi.org/10.1063/5.0279474

Syarifudin, Muhammad Rizky ; Prastianto, Rudi Walujo ; Syalsabila, Ferdita et al. / Application of machine learning in reliability based design optimization of jacket offshore wind turbine using surrogate model. AIP Conference Proceedings. editor / Taufiq Ilham Maulana ; Satish Paudel ; Edisson Alberto Moscoso Alcantara ; Boon Chye Rudy Ang ; Christopher Amoah ; Dian M. Setiawan ; Ani Hariani ; Muhammad Ibnu Syamsi. 1. ed. American Institute of Physics, 2025. (AIP Conference Proceedings; 1).

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title = "Application of machine learning in reliability based design optimization of jacket offshore wind turbine using surrogate model",

abstract = "The supporting structure of offshore wind turbines has developed rapidly in recent years due to the demand of renewable energy technology. Compared to land-based, wind turbines prefer to be placed in the ocean due to the availability of ocean space. However, the harsh environment in the sea adds to the complexity of the structure analysis. Thus, this research aims to optimize the structure design of jacket offshore wind turbines. The main goal is to reduce the material cost. The radial basis function as a machine learning tool is applied to reduce the computational time by building the surrogate model. Then, using the constructed sampling surrogate model as a constraint, the Monte Carlo Simulation (MCS) approach is utilized to determine the probability of structural failure. The results indicate that machine learning is feasible to optimize the wind turbine supporting structure. The optimized design and reduction ratio are obtained using the reliability-based design optimization method.",

author = "Syarifudin, \{Muhammad Rizky\} and Prastianto, \{Rudi Walujo\} and Ferdita Syalsabila and Darisman, \{Hudzwah Azzahrawani\} and Daniel Rosyid and Pratiwi, \{Zhahara Suci\}",

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doi = "10.1063/5.0279474",

language = "English",

series = "AIP Conference Proceedings",

publisher = "American Institute of Physics",

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Syarifudin, MR, Prastianto, RW , Syalsabila, F, Darisman, HA, Rosyid, D & Pratiwi, ZS 2025, Application of machine learning in reliability based design optimization of jacket offshore wind turbine using surrogate model. in TI Maulana, S Paudel, EAM Alcantara, BCR Ang, C Amoah, DM Setiawan, A Hariani & MI Syamsi (eds), AIP Conference Proceedings. 1 edn, 070010, AIP Conference Proceedings, no. 1, vol. 3317, American Institute of Physics, 3rd International Symposium on Civil, Environmental, and Infrastructure Engineering, ISCEIE 2024, Yogyakarta, Indonesia, 7/08/24. https://doi.org/10.1063/5.0279474

Application of machine learning in reliability based design optimization of jacket offshore wind turbine using surrogate model. / Syarifudin, Muhammad Rizky; Prastianto, Rudi Walujo ; Syalsabila, Ferdita et al.
AIP Conference Proceedings. ed. / Taufiq Ilham Maulana; Satish Paudel; Edisson Alberto Moscoso Alcantara; Boon Chye Rudy Ang; Christopher Amoah; Dian M. Setiawan; Ani Hariani; Muhammad Ibnu Syamsi. 1. ed. American Institute of Physics, 2025. 070010 (AIP Conference Proceedings; Vol. 3317, No. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Application of machine learning in reliability based design optimization of jacket offshore wind turbine using surrogate model

AU - Syarifudin, Muhammad Rizky

AU - Prastianto, Rudi Walujo

AU - Syalsabila, Ferdita

AU - Darisman, Hudzwah Azzahrawani

AU - Rosyid, Daniel

AU - Pratiwi, Zhahara Suci

PY - 2025/7/15

Y1 - 2025/7/15

N2 - The supporting structure of offshore wind turbines has developed rapidly in recent years due to the demand of renewable energy technology. Compared to land-based, wind turbines prefer to be placed in the ocean due to the availability of ocean space. However, the harsh environment in the sea adds to the complexity of the structure analysis. Thus, this research aims to optimize the structure design of jacket offshore wind turbines. The main goal is to reduce the material cost. The radial basis function as a machine learning tool is applied to reduce the computational time by building the surrogate model. Then, using the constructed sampling surrogate model as a constraint, the Monte Carlo Simulation (MCS) approach is utilized to determine the probability of structural failure. The results indicate that machine learning is feasible to optimize the wind turbine supporting structure. The optimized design and reduction ratio are obtained using the reliability-based design optimization method.

AB - The supporting structure of offshore wind turbines has developed rapidly in recent years due to the demand of renewable energy technology. Compared to land-based, wind turbines prefer to be placed in the ocean due to the availability of ocean space. However, the harsh environment in the sea adds to the complexity of the structure analysis. Thus, this research aims to optimize the structure design of jacket offshore wind turbines. The main goal is to reduce the material cost. The radial basis function as a machine learning tool is applied to reduce the computational time by building the surrogate model. Then, using the constructed sampling surrogate model as a constraint, the Monte Carlo Simulation (MCS) approach is utilized to determine the probability of structural failure. The results indicate that machine learning is feasible to optimize the wind turbine supporting structure. The optimized design and reduction ratio are obtained using the reliability-based design optimization method.

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U2 - 10.1063/5.0279474

DO - 10.1063/5.0279474

M3 - Conference contribution

AN - SCOPUS:105012127632

T3 - AIP Conference Proceedings

BT - AIP Conference Proceedings

A2 - Maulana, Taufiq Ilham

A2 - Paudel, Satish

A2 - Alcantara, Edisson Alberto Moscoso

A2 - Ang, Boon Chye Rudy

A2 - Amoah, Christopher

A2 - Setiawan, Dian M.

A2 - Hariani, Ani

A2 - Syamsi, Muhammad Ibnu

PB - American Institute of Physics

T2 - 3rd International Symposium on Civil, Environmental, and Infrastructure Engineering, ISCEIE 2024

Y2 - 7 August 2024 through 8 August 2024

ER -

Syarifudin MR, Prastianto RW , Syalsabila F, Darisman HA, Rosyid D, Pratiwi ZS. Application of machine learning in reliability based design optimization of jacket offshore wind turbine using surrogate model. In Maulana TI, Paudel S, Alcantara EAM, Ang BCR, Amoah C, Setiawan DM, Hariani A, Syamsi MI, editors, AIP Conference Proceedings. 1 ed. American Institute of Physics. 2025. 070010. (AIP Conference Proceedings; 1). doi: 10.1063/5.0279474

Application of machine learning in reliability based design optimization of jacket offshore wind turbine using surrogate model

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