Reliability analysis of discrete-state performance functions via adaptive sequential sampling with detection of failure surfaces

Vořechovský, Miroslav

doi:10.1016/j.cma.2022.115606

Cited by 12 publications

(3 citation statements)

References 113 publications

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“…Both methods were used in the present study. We remark that a particularly effective method for the estimation of failure probability is to use active learning strategy for the selection of points for the evaluation of the full model, and then perform importance sampling analysis of a surrogate model build with the help of the optimal training points 38 . In the present case, the evaluation of the full model is so quick, that these advanced techniques are not necessary.…”

Section: Solution Methodsmentioning

confidence: 99%

Shallow foundation design: a comparative study of partial safety factors and full probabilistic methods

Vořechovský,

Miča,

Boštík

2024

Sci Rep

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In the past two decades, Europe has witnessed a significant transition in the design codes used for assessing foundation structures, with the widespread adoption of the Eurocodes (EC). This shift remains a pertinent topic within the engineering community, particularly concerning the transition from traditional design methodologies to those prescribed by the Eurocodes, as well as the potential for fully probabilistic design. While the Eurocodes’ methodology is described as probabilistic, it is crucial to recognize that the achievement of the target reliability level is predominantly facilitated through a system of partial safety factors. These factors are integrated into the calculation algorithm as fixed values, rendering the process essentially deterministic. To refine these calculations for more accurate reliability estimates—expressed in terms of failure probability—a genuinely probabilistic framework is required, termed as fully probabilistic computation. This paper aims to elucidate the fully probabilistic calculation approach for the broader professional community, using the geotechnical application of shallow foundations as an illustrative example. We present a comparative analysis of this advanced approach with the standard foundation design according to EC7 and ČSN 731001, the latter being a precursor in Europe for implementing the partial safety factor method. The discussion extends to a practical demonstration of full probabilistic design juxtaposed against the conventional partial safety factor method, using a shallow foundation case study. Furthermore, the paper delves into the impact of the tail behavior of uncertain or spatially varying soil parameters on the theoretical probability of failure, underscoring its significance in foundation design.

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Section: Solution Methodsmentioning

confidence: 99%

Shallow foundation design: a comparative study of partial safety factors and full probabilistic methods

Vořechovský,

Miča,

Boštík

2024

Sci Rep

Self Cite

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“…Global sensitivity analysis methods mainly include regression-based or correlation-based methods (e.g., Regression coefficients, Pearson correlation coefficient and spearman correlation coefficient (Heijungs & Lenzen, 2014;Chen & Corson, 2014;Geisler et al, 2005)), variance-based methods (e.g., Sobol method (Sobol, 2001)), and fourier amplitude sensitivity test (Koning et al, 2010). In addition, for more complicated failure surfaces, a new efficient and robust method was presented by Vořechovský (2022). Study of Groen et al (2016) has proved that Spearman correlation coefficient and Sobol method have better stability and multi-parameter processing ability than other analytical methods.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Seismic Performance Sensitivity Analysis to Random Variables for Cable Tray System

Fu,

2024

Journal of Civil Engineering and Management

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Random variables introduced in modelling of seismic engineering are often the result of cognitive limitations and the unpredictability of structures, leading to uncertainties in the field. A practical method for dealing with them is to develop sensitivity analysis in the framework of data and probability statistics. Of existing non-structural components, cable tray systems are characterized by a number of uncertainties which may influence their bearing capacity drastically. In this research, the main characteristics of material, geometry, member layout along with the connection stiffness in cable tray are considered as random variables using global sensitivity analysis, with their results relative importance of these potential uncertainties on the seismic performance of cable tray. The sensitivity analysis method developed especially for cable tray under seismic excitation is constructed based on modal analysis and equivalent inertia force method combined with the Latin hypercube sampling method. The final results demonstrate the need to consider the effects of random variables in modeling assumption in seismic performance analyses of cable tray and can be further used in optimization design.

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“…In this section, we demonstrate the relevance of the proposed method for a representative engineering example exhibiting discontinuous response. Consider the shallow two-bar planar truss subjected to a vertical load at its top joint, as presented in [50] and illustrated in Fig. 10a.…”

Section: Asymmetric Shallow Von Mises Trussmentioning

confidence: 99%

Active Learning-based Domain Adaptive Localized Polynomial Chaos Expansion

Novák¹,

Shields²,

Sadílek³

et al. 2023

Preprint

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Effective construction of a general purpose surrogate model based on polynomial chaos expansion.• Novel method for sequential decomposition of the input random space and construction of local approximations.• Sequential domain decomposition and sample size extension based on an active learning methodology.• Active learning is represented by variance-based Θ criterion developed for polynomial chaos expansion.

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Reliability analysis of discrete-state performance functions via adaptive sequential sampling with detection of failure surfaces

Cited by 12 publications

References 113 publications

Shallow foundation design: a comparative study of partial safety factors and full probabilistic methods

Shallow foundation design: a comparative study of partial safety factors and full probabilistic methods

Seismic Performance Sensitivity Analysis to Random Variables for Cable Tray System

Active Learning-based Domain Adaptive Localized Polynomial Chaos Expansion

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