An Efficient Monte-Carlo Simulation for the Dynamic Reliability Analysis of Jacket Platforms Subjected to Random Wave Loads

Wang, Lin; Su, Cheng

doi:10.3390/jmse9040380

Cited by 9 publications

(4 citation statements)

References 36 publications

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“…Strategies such as stratified sampling and Latin hypercube sampling have been employed to enhance efficiency [12]. Lin and Su [13] introduced an explicit time-domain method-based MCS for assessing the first-passage system dynamic reliability of jacket platforms under specific sea conditions, aiming for computational efficiency.…”

Section: Introductionmentioning

confidence: 99%

Active Learning-Based Kriging Model with Noise Responses and Its Application to Reliability Analysis of Structures

Chun

2024

Applied Sciences

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This study introduces a reliability analysis methodology employing Kriging modeling enriched by a hybrid active learning process. Emphasizing noise integration into structural response predictions, this research presents a framework that combines Kriging modeling with regression to handle noisy data. The framework accommodates either constant variance of noise for all observed responses or varying, uncorrelated noise variances. Hyperparameters and the variance of the Kriging model with noisy data are determined through maximum likelihood estimation to address inherent uncertainties in structural predictions. An adaptive hybrid learning function guides design of experiment (DoE) point identification through an iterative enrichment process. This function strategically targets points near the limit-state approximation, farthest from existing training points, and explores candidate points to maximize the probability of misclassification. The framework’s application is demonstrated through metamodel-based reliability analysis for continuum and discrete structures with relatively large degrees of freedom, employing subset simulations. Numerical examples validate the framework’s effectiveness, highlighting its potential for accurate and efficient reliability assessments in complex structural systems.

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Section: Introductionmentioning

confidence: 99%

Active Learning-Based Kriging Model with Noise Responses and Its Application to Reliability Analysis of Structures

Chun

2024

Applied Sciences

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“…[40][41][42][43][44][45][46][47][48][49]. The computational simulation is really useful in ocean industry too, being used in different studies such as, marine and ships structures [50][51][52][53], breakwater analysis [54], offshore wind turbine [55], maintenance operations of Wave Energy Farm [56], etc.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Wave Energy Transformation Capability in Isolated Islands by Using the Monte Carlo Method

Ávila

Marichal

Quiza

et al. 2021

JMSE

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In this work, a mathematical computer simulation model is used to predict the possible energy generated from different Waves Energy Converters (WECs) in the Canary Islands. The Monte Carlo Method is the computer simulation model proposed to predict the generated energy. The Waves Energy Converter systems analyzed in the study were, the Aqua Buoy, Wave Dragon and Pelamis converters. The models were implemented and validated, with the dataset of Gran Canaria deep water buoy. This buoy belongs to a network of buoys belonging to Spain’s State Ports and they cover a dataset period of 22 years. The research has concluded that it is possible to affirm that the achieved model is a strong tool to compute the possible energy of any WECs, when the power matrix is known. The model based on the Monte Carlo simulation can be used in isolated islands of the Atlantic Ocean and can be extrapolated to other regions with the same characteristics.

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“…As an approach considers the randomness of resistance and the load effect, reliability analysis can control the occurrence of failure at an acceptable level according to the structure's reliability requirements to seek the balance between safety and economic benefit of the structure. To avoid the computational difficulty and the lack of accuracy caused by the strong nonlinear equation, scholars use the Monte Carlo sampling (MCS) method to solve offshore structures' reliability [27]. However, MCS usually generates duplicate samples in the random sampling.…”

Section: Introductionmentioning

confidence: 99%

Kriging Model for Reliability Analysis of the Offshore Steel Trestle Subjected to Wave and Current Loads

Liu

Shang

Liu

et al. 2021

JMSE

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Offshore steel trestles (OSTs) are exposed to severe marine environments with stochastic wave and current loads, making structural safety assessment challenging and difficult. Reliability analysis is a suitable way to consider both wave and current loading intensity uncertainties, but the implicit and complex limit state functions of the reliability analysis usually imply huge computational costs. This paper proposes an efficient reliability analysis framework for OST using the kriging model of optimal linear unbiased estimation. The surrogate model is built with stochastic waves, current parameters, and the corresponding load factors. The framework is then used to evaluate the reliability of an example OST subjected to wave and current loads at three limit states of OST, including first yield (FY), full plastic (FP), and collapse initiation (CI). Three different distributions are used for comparison of the results of failure probability and reliability index. The results and the computational cost by the proposed framework are compared with that from the Monte Carlo sampling (MCS) and Latin hypercube sampling (LHS) method. The influences of sample number on the prediction accuracy and reliability index are investigated. The influence of marine growth on the reliability analysis of the OST is discussed using MCS and the kriging model. The results show that the reliability analysis based on the kriging model can obtain the reliability index for the OST efficiently with less calculation time but similar results compared with MCS and LHS. With the increase of the number of samples, the prediction accuracy of the kriging model increases, and the corresponding failure probability fluctuates greatly at first and then tends to be stable. The reliability of the example OST is reduced with the increase of marine growth, regardless of the limit state.

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An Efficient Monte-Carlo Simulation for the Dynamic Reliability Analysis of Jacket Platforms Subjected to Random Wave Loads

Cited by 9 publications

References 36 publications

Active Learning-Based Kriging Model with Noise Responses and Its Application to Reliability Analysis of Structures

Active Learning-Based Kriging Model with Noise Responses and Its Application to Reliability Analysis of Structures

Prediction of Wave Energy Transformation Capability in Isolated Islands by Using the Monte Carlo Method

Kriging Model for Reliability Analysis of the Offshore Steel Trestle Subjected to Wave and Current Loads

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