Extreme response prediction for fixed offshore structures by efficient time simulation regression procedures. Part 2: model validation

Ahmad, Samsudin; Husain, M. K. Abu; Zaki, N. I. Mohd; Mukhlas, N. A.; Najafian, G.

doi:10.1080/17445302.2022.2059252

Cited by 3 publications

(1 citation statement)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For fixed offshore structures, wind-generated random wave loads are the dominant load (Hagen, 1996;Aeran et al, 2017) to consider in maintaining reliability. While these structures can be designed by subjecting them to extreme regular waves for a 100-year return period (Abu Husain et al, 2013;Mat Soom et al, 2015;ABS, 2016), using probabilistic techniques to account for the inherent randomness of the wave loading is far more satisfactory (Syed Ahmad et al, 2022;Ladeira et al, 2022;Gadai & Xing, 2022). Prediction of extreme offshore structural response due to wind-generated random wave load is generally based on linear structural response and the Gaussian distribution of the response time history (API RP 2A, 2014).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Long-Term Offshore Structural Responses Based on Nonlinear Wave Modeling

Mukhlas,

Mohd Zaki,

Abu Husain

et al. 2023

JSCET

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Prediction of Long-Term Offshore Structural Responses Based on Nonlinear Wave Modeling

Mukhlas,

Mohd Zaki,

Abu Husain

et al. 2023

JSCET

View full text Add to dashboard Cite

show abstract

An Excellent Relationship-Based Model Development in Deriving the Short-Term Probability Distribution of Offshore Structural Responses

Ahmad,

Musa,

Roslan

et al. 2024

Advanced Structured Materials

View full text Add to dashboard Cite

An Improved Version of ETS-Regression Models in Calculating the Fixed Offshore Platform Responses

Ahmad

Husain

Zaki

et al. 2022

JMSE

View full text Add to dashboard Cite

An offshore structural design should accurately calculate wave loads and structural responses acting on slender cylinders. The hydrodynamic drag-dominated force was always challenging, hence the hydrodynamic wave loading became a complex solution; it led to a nonlinear relation between the wave force and responses caused by the diffracted and radiated waves, which was included in Morison’s equation. For more consistency in the structural assessment, the linearised drag–inertia force was considered in model development, such as an improved version of the efficient time simulation regression (ETS-Reg) procedure that was introduced. The study aimed to improve the prediction of structural responses using the predetermined linear, polynomial, and cubic regression models. These simulations were performed focusing on high sea state conditions without wave-induced current effects. In order to evaluate the level of accuracy, the recent ETS-Reg models were compared and validated using the Monte Carlo time simulation (MCTS) method. An amended ETS-Reg model, known as the ETS-RegLR model, was also compared with the previous results obtained using the conventional ETS-Reg models (ETS-RegSE), leading to better structural response calculations.

show abstract

Extreme response prediction for fixed offshore structures by efficient time simulation regression procedures. Part 2: model validation

Cited by 3 publications

References 33 publications

Prediction of Long-Term Offshore Structural Responses Based on Nonlinear Wave Modeling

Prediction of Long-Term Offshore Structural Responses Based on Nonlinear Wave Modeling

An Excellent Relationship-Based Model Development in Deriving the Short-Term Probability Distribution of Offshore Structural Responses

An Improved Version of ETS-Regression Models in Calculating the Fixed Offshore Platform Responses

Contact Info

Product

Resources

About