Prediction of long-term offshore structural responses based on non-linear wave modeling / Nurul ‘Azizah Mukhlas ... [et al.]
Wind-generated random wave loads are the dominant loads to consider for maintaining the reliability of fixed offshore structures. Based on probabilistic techniques, the inherent randomness of the wave loading can be used to predict extreme offshore structural response, which is Gaussian in nature. H...
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my.uitm.ir.856392023-10-12T02:50:56Z https://ir.uitm.edu.my/id/eprint/85639/ Prediction of long-term offshore structural responses based on non-linear wave modeling / Nurul ‘Azizah Mukhlas ... [et al.] jscet Mukhlas, Nurul ‘Azizah Mohd Zaki, Noor Irza Abu Husain, Mohd Khairi Syed Ahmad, Sayyid Zainal Abidin Ng, Chiew Teng Ahmad Shah, Mohamad Shazwan Umar, Sarehati Md Noor, Norhazilan Finite element method Dynamic loading conditions. Structural dynamics. Vibrations Wind-generated random wave loads are the dominant loads to consider for maintaining the reliability of fixed offshore structures. Based on probabilistic techniques, the inherent randomness of the wave loading can be used to predict extreme offshore structural response, which is Gaussian in nature. However, researchers have found that the hydrodynamic component and structural dynamics substantially impact the frequency spectrum, leading to a non-Gaussian stochastic offshore structural response. A finite-memory non-linear system (FMNSNL) has been proven to be an efficient approach to evaluate the non-Gaussian stochastic offshore structural response compared to the conventional method, Monte Carlo time simulation. However, the analysis has been conducted based on short-term distribution only. The most satisfactory analysis is based on long-term distribution. Hence, further investigation in this paper will evaluate the long-term probability distribution of extreme offshore structural responses. As a result, a 100-year extreme offshore structural response prediction achieves 80% to 96% accuracy compared to the Monte Carlo time simulation. The probability distribution has been evaluated using the Gumbel distribution function throughout this investigation. Still, there is a little deviation at the tail end of the distribution between the simulated response values and the fitted line. A different distribution function, such as the Generalised Extreme Value (GEV) distribution, is advised for future work. 2023-09 Article PeerReviewed text en https://ir.uitm.edu.my/id/eprint/85639/1/85639.pdf Prediction of long-term offshore structural responses based on non-linear wave modeling / Nurul ‘Azizah Mukhlas ... [et al.]. (2023) Journal of Sustainable Civil Engineering & Technology (JSCET) <https://ir.uitm.edu.my/view/publication/Journal_of_Sustainable_Civil_Engineering_=26_Technology_=28JSCET=29/>, 2 (2): 2. pp. 14-27. ISSN 2948-4294 |
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Finite element method Dynamic loading conditions. Structural dynamics. Vibrations |
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Finite element method Dynamic loading conditions. Structural dynamics. Vibrations Mukhlas, Nurul ‘Azizah Mohd Zaki, Noor Irza Abu Husain, Mohd Khairi Syed Ahmad, Sayyid Zainal Abidin Ng, Chiew Teng Ahmad Shah, Mohamad Shazwan Umar, Sarehati Md Noor, Norhazilan Prediction of long-term offshore structural responses based on non-linear wave modeling / Nurul ‘Azizah Mukhlas ... [et al.] |
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Wind-generated random wave loads are the dominant loads to consider for maintaining the reliability of fixed offshore structures. Based on probabilistic techniques, the inherent randomness of the wave loading can be used to predict extreme offshore structural response, which is Gaussian in nature. However, researchers have found that the hydrodynamic component and structural dynamics substantially impact the frequency spectrum, leading to a non-Gaussian stochastic offshore structural response. A finite-memory non-linear system (FMNSNL) has been proven to be an efficient approach to evaluate the non-Gaussian stochastic offshore structural response compared to the conventional method, Monte Carlo time simulation. However, the analysis has been conducted based on short-term distribution only. The most satisfactory analysis is based on long-term distribution. Hence, further investigation in this paper will evaluate the long-term probability distribution of extreme offshore structural responses. As a result, a 100-year extreme offshore structural response prediction achieves 80% to 96% accuracy compared to the Monte Carlo time simulation. The probability distribution has been evaluated using the Gumbel distribution function throughout this investigation. Still, there is a little deviation at the tail end of the distribution between the simulated response values and the fitted line. A different distribution function, such as the Generalised Extreme Value (GEV) distribution, is advised for future work. |
format |
Article |
author |
Mukhlas, Nurul ‘Azizah Mohd Zaki, Noor Irza Abu Husain, Mohd Khairi Syed Ahmad, Sayyid Zainal Abidin Ng, Chiew Teng Ahmad Shah, Mohamad Shazwan Umar, Sarehati Md Noor, Norhazilan |
author_facet |
Mukhlas, Nurul ‘Azizah Mohd Zaki, Noor Irza Abu Husain, Mohd Khairi Syed Ahmad, Sayyid Zainal Abidin Ng, Chiew Teng Ahmad Shah, Mohamad Shazwan Umar, Sarehati Md Noor, Norhazilan |
author_sort |
Mukhlas, Nurul ‘Azizah |
title |
Prediction of long-term offshore structural responses based on non-linear wave modeling / Nurul ‘Azizah Mukhlas ... [et al.] |
title_short |
Prediction of long-term offshore structural responses based on non-linear wave modeling / Nurul ‘Azizah Mukhlas ... [et al.] |
title_full |
Prediction of long-term offshore structural responses based on non-linear wave modeling / Nurul ‘Azizah Mukhlas ... [et al.] |
title_fullStr |
Prediction of long-term offshore structural responses based on non-linear wave modeling / Nurul ‘Azizah Mukhlas ... [et al.] |
title_full_unstemmed |
Prediction of long-term offshore structural responses based on non-linear wave modeling / Nurul ‘Azizah Mukhlas ... [et al.] |
title_sort |
prediction of long-term offshore structural responses based on non-linear wave modeling / nurul ‘azizah mukhlas ... [et al.] |
publishDate |
2023 |
url |
https://ir.uitm.edu.my/id/eprint/85639/1/85639.pdf https://ir.uitm.edu.my/id/eprint/85639/ |
_version_ |
1781709286301237248 |