A combined projection-outline-based active learning Kriging and adaptive importance sampling method for hybrid reliability analysis with small failure probabilities

Zhang, Jinhao; Xiao, Mi; Gao, Liang; Chu, Sheng

doi:10.1016/j.cma.2018.10.003

Cited by 120 publications

(30 citation statements)

References 29 publications

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“…As a result, they are not applicable for practical uses ( Melchers and Beck, 2018 ). The-state-of-the-art methods based on surrogate models and neural networks proved to be efficient and easy to implement ( Xiao et al, 2018 ; Zhang et al, 2019c ; Oparaji et al, 2017 ). The majority of these methods however, do not consider the dependency between components and/or are difficult to be extended to time-dependent system reliability cases ( Yuan et al, 2020 ; Jiang et al, 2020 ).…”

Section: Reliability Analysis Of the Integrated Systemmentioning

confidence: 99%

Reliability of multi-purpose offshore-facilities: Present status and future direction in Australia

Aryai

Abdussamie

Salehi

et al. 2021

Process Safety and Environmental Protection

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Sustainable use of the ocean for food and energy production is an emerging area of research in different countries around the world. This goal is pursued by the Australian aquaculture, offshore engineering and renewable energy industries, research organisations and the government through the “Blue Economy Cooperative Research Centre”. To address the challenges of offshore food and energy production, leveraging the benefits of co-location, vertical integration, infrastructure and shared services, will be enabled through the development of novel Multi-Purpose Offshore-Platforms (MPOP). The structural integrity of the designed systems when being deployed in the harsh offshore environment is one of the main challenges in developing the MPOPs. Employing structural reliability analysis methods for assessing the structural safety of the novel aquaculture-MPOPs comes with different limitations. This review aims at shedding light on these limitations and discusses the current status and future directions for structural reliability analysis of a novel aquaculture-MPOP considering Australia’s unique environment. To achieve this aim, challenges which exist at different stages of reliability assessment, from data collection and uncertainty quantification to load and structural modelling and reliability analysis implementation, are discussed. Furthermore, several solutions to these challenges are proposed based on the existing knowledge in other sectors, and particularly from the offshore oil and gas industry. Based on the identified gaps in the review process, potential areas for future research are introduced to enable a safer and more reliable operation of the MPOPs.

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Section: Reliability Analysis Of the Integrated Systemmentioning

confidence: 99%

Reliability of multi-purpose offshore-facilities: Present status and future direction in Australia

Aryai

Abdussamie

Salehi

et al. 2021

Process Safety and Environmental Protection

View full text Add to dashboard Cite

show abstract

“…Two components of the mechano‐probabilistic problem may affect the accuracy of the reliability assessment: epistemic uncertainties on some parameters (including model parameters) and the inaccuracy of the numerical solution to the mechanical problem. To deal with epistemic uncertainties on parameter, References 14 and 15 propose a projection method to propagate through the mechanical model bounds on these variable to obtain bounds on the probability of failure. Few people consider the error due to the discretization method (mainly the finite element method).…”

Section: Introductionmentioning

confidence: 99%

Multifidelity adaptive kriging metamodel based on discretization error bounds

Mell

Rey

Schoefs

2020

Numerical Meth Engineering

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Summary This article presents an approach to build a multifidelity kriging metamodel from finite element computations on different meshes for stuctural reliability assessment. The proposed method takes advantage of the computation of bounds on the discretization error, which enables to guarantee the state (safe or failure) of each computation of the performance function. An algorithm to build the metamodel from the different levels of fidelity and estimate the failure probability is provided. Illustrations are presented on a two dimensional mechanical crack opening problem. Bounds on the failure probability are also post‐processed.

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“…The fusion of ALK model with the kernel‐density‐estimation‐based IS method (KDE‐IS) was researched and the method named ALK‐KDE‐IS was proposed in Reference 40. The fusion of ALK model and KDE‐IS for hybrid reliability analysis was researched in Reference 41. Recently, a hybrid method combining AK‐MCS and IS (AK‐MCS‐IS) was proposed in Reference 42.…”

Section: Introductionmentioning

confidence: 99%

Active learning method combining Kriging model and multimodal‐optimization‐based importance sampling for the estimation of small failure probability

Yuan

Cheng

2020

Numerical Meth Engineering

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A novel method which combines the active learning Kriging (ALK) model with important sampling is proposed in this paper. The main aim of the proposed method is to solve problems with very small failure probability and multiple failure regions. A surrogate limit state surface (LSS) which strikes a balance between the Kriging mean and variance is proposed. In each iteration, important samples of the surrogate LSS are generated, optimal training points are chosen, the Kriging model is updated and the surrogate LSS is refined. After several iterations, the surrogate LSS will converge to the true LSS. To obtain all the local and global most probable points (MPPs) on the surrogate LSS in each iteration, a recently proposed evolutionary algorithm from the field of multimodal optimization is introduced. In this way, none of the potential failure regions is missed and the unbiasedness of the proposed method is guaranteed. The contribution factor of each MPP is defined and a weighted multimodal instrumental sampling density is formulated. In this way, more attention is paid to the more important failure regions and training points are further saved. The performance of the proposed method is verified by six case studies.

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A combined projection-outline-based active learning Kriging and adaptive importance sampling method for hybrid reliability analysis with small failure probabilities

Cited by 120 publications

References 29 publications

Reliability of multi-purpose offshore-facilities: Present status and future direction in Australia

Reliability of multi-purpose offshore-facilities: Present status and future direction in Australia

Multifidelity adaptive kriging metamodel based on discretization error bounds

Active learning method combining Kriging model and multimodal‐optimization‐based importance sampling for the estimation of small failure probability

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