REIF: A novel active-learning function toward adaptive Kriging surrogate models for structural reliability analysis

Zhang, Xufang; Wang, Lei; Sørensen, John Dalsgaard

doi:10.1016/j.ress.2019.01.014

Cited by 210 publications

(70 citation statements)

References 31 publications

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“…Mechanical robustness is the key component restricting the widespread application of superhydrophobic surfaces, because the micro/nano structures are prone to be abraded [31][32][33][34][35][36]. In this research, we performed the sandpaper abrasion test to investigate the mechanical robustness.…”

Section: Resultsmentioning

confidence: 99%

Fabrication of robust conductive and superhydrophobic coating based on carbon nanotubes

Wang

Feng

et al. 2020

Mater. Res. Express

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This paper reported a simple approach to prepare robust conductive/superhydrophobic coating. The hierarchical structure was obtained through the addition of microscale filler (graphite powder and expanded graphite) and nanoscale filler (carbon nanotube). The self-similar structure was obtained through bonding the fillers using the epoxy matrix. Through the combination of the hierarchical and self-similar structures, the as-prepared superhydrophobic coating demonstrated excellent antiabrasion property, good conductivity, excellent self-cleaning performance in both oil and water environment, outstanding anticorrosive property, and superior thermal stability simultaneously. Moreover, this superhydrophobic coating was achieved by a simple casting method, which has the potential to be used in large scale production.

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Section: Resultsmentioning

confidence: 99%

Fabrication of robust conductive and superhydrophobic coating based on carbon nanotubes

Wang

Feng

et al. 2020

Mater. Res. Express

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“…al [21]. Other methods have also been presented to address specific problems such as small failure probabilities (rare events) estimations [3,22,23,24,25,7,26] , multiple failure regions problems [27,28,29,30] or systems failure probabilities assessment [6,31,5,2,9,32].…”

Section: Introductionmentioning

confidence: 99%

Adaptive coupling of reduced basis modeling and Kriging based active learning methods for reliability analyses

Menz

Gogu

Dubreuil

et al. 2020

Reliability Engineering & System Safety

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Running a reliability analysis on engineering problems involving complex numerical models can be computationally very expensive. Hence, advanced methods are required to reduce the number of calls to the expensive computer codes. Adaptive sampling based reliability analysis methods are one promising way to reduce computational costs. Reduced order modelling is another one. In order to further reduce the numerical costs of Kriging based adaptive sampling approaches, the idea developed in this paper consists in coupling both approaches by adaptively deciding whether to use reduced-basis solutions in place of full numerical solutions whenever the performance function needs to be assessed. Thus, a method combining such adaptive sampling based reliability analyses and reduced basis modeling is proposed using on an efficient coupling criterion. The proposed method enabled significant computational cost reductions, while ensuring accurate estimations of failure probabilities.

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“…Lelièvre et al [27] presented an improved AK-MCS method named AK-MCSi. Xufang et al [28] proposed a novel active learning function which is named expected improvement function (REIF).…”

Section: Introductionmentioning

confidence: 99%

An Improved Structural Reliability Analysis Method Based on Local Approximation and Parallelization

Xie

2020

Mathematics

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The Kriging-based reliability method with a sequential design of experiments (DoE) has been developed in recent years for implicit limit state functions. Such methods include the efficient global reliability analysis, the active learning reliability method combining Kriging and MCS Simulations. In this research, a novel local approximation method based on the most probable failure point (MPFP) is proposed to improve such methods. In this method, the MPFP calculated in the last iteration is the center of the next sampling region. The size of the local region depends on the reliability index obtained by the First Order Reliability Method (FORM) and the deviation distance of the standard deviation. The proposed algorithm, which approximates the limit state function accurately near MPFP rather than in the whole design space, can avoid selecting samples in regions that have negligible effects on the reliability analysis results. In addition, a multi-point enrichment technique is also introduced to select multiple sample points in each iteration. After the high-quality approximation of limit state function is obtained, the failure probability is calculated by the Monte Carlo method. Four numerical examples are used to validate the accuracy and efficiency of the proposed method. Results show that the proposed method is very effective for an accurate evaluation of the failure probability.

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REIF: A novel active-learning function toward adaptive Kriging surrogate models for structural reliability analysis

Cited by 210 publications

References 31 publications

Fabrication of robust conductive and superhydrophobic coating based on carbon nanotubes

Fabrication of robust conductive and superhydrophobic coating based on carbon nanotubes

Adaptive coupling of reduced basis modeling and Kriging based active learning methods for reliability analyses

An Improved Structural Reliability Analysis Method Based on Local Approximation and Parallelization

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