Machine learning-based methods in structural reliability analysis: A review
Structural Reliability analysis (SRA) is one of the prominent fields in civil and mechanical
engineering. However, an accurate SRA in most cases deals with complex and costly …
engineering. However, an accurate SRA in most cases deals with complex and costly …
[HTML][HTML] Adaptive approaches in metamodel-based reliability analysis: A review
The present work reviews the implementation of adaptive metamodeling for reliability
analysis with emphasis in four main types of metamodels: response surfaces, polynomial …
analysis with emphasis in four main types of metamodels: response surfaces, polynomial …
Modeling, analysis, and optimization under uncertainties: a review
Abstract Design optimization of structural and multidisciplinary systems under uncertainty
has been an active area of research due to its evident advantages over deterministic design …
has been an active area of research due to its evident advantages over deterministic design …
Copula-based reliability and sensitivity analysis of aging dams: Adaptive Kriging and polynomial chaos Kriging methods
Time-dependent reliability and sensitivity analysis, in which the nature of demand, capacity
and the limit state function varies over the life cycle of the structural system, is a challenging …
and the limit state function varies over the life cycle of the structural system, is a challenging …
Optimal sparse polynomial chaos expansion for arbitrary probability distribution and its application on global sensitivity analysis
Polynomial chaos expansion has received considerable attention in uncertainty
quantification since its great modeling capability for complex systems. However, considering …
quantification since its great modeling capability for complex systems. However, considering …
Reliability analysis with stratified importance sampling based on adaptive Kriging
In reliability engineering, estimating the failure probability of a system is one of the most
challenging tasks. Since many applied engineering tasks are computationally expensive, it …
challenging tasks. Since many applied engineering tasks are computationally expensive, it …
Generative adversarial surrogate modeling framework for aerospace engineering structural system reliability design
D Teng, YW Feng, C Lu, B Keshtegar, XF Xue - Aerospace Science and …, 2024 - Elsevier
To effectively realize the reliability design of engineering structural system, a generative
adversarial surrogate modeling (GASM) concept is proposed by innovating generative …
adversarial surrogate modeling (GASM) concept is proposed by innovating generative …
Non-parametric simulation of non-stationary non-gaussian 3D random field samples directly from sparse measurements using signal decomposition and Markov …
With the ever-growing computational power of personal computers over the past few
decades, stochastic simulation of spatially varying three-dimensional (3D) quantities has …
decades, stochastic simulation of spatially varying three-dimensional (3D) quantities has …
[HTML][HTML] A novel fractional moments-based maximum entropy method for high-dimensional reliability analysis
High-dimensional reliability analysis is still an open challenge in structural reliability
community. To address this problem, a new sampling approach, named the good lattice …
community. To address this problem, a new sampling approach, named the good lattice …
Active learning with generalized sliced inverse regression for high-dimensional reliability analysis
It is computationally expensive to predict reliability using physical models at the design
stage if many random input variables exist. This work introduces a dimension reduction …
stage if many random input variables exist. This work introduces a dimension reduction …