Machine learning-based methods in structural reliability analysis: A review

SS Afshari, F Enayatollahi, X Xu, X Liang - Reliability Engineering & System …, 2022 - Elsevier
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 …

Surrogate-assisted reliability-based design optimization: a survey and a unified modular framework

M Moustapha, B Sudret - Structural and Multidisciplinary Optimization, 2019 - Springer
Reliability-based design optimization (RBDO) is an active field of research with an ever
increasing number of contributions. Numerous methods have been proposed for the solution …

Copula-based JPDF of wind speed, wind direction, wind angle, and temperature with SHM data

Y Ding, XW Ye, Y Guo - Probabilistic Engineering Mechanics, 2023 - Elsevier
Structural health monitoring (SHM) systems installed on long-span bridges can obtain
environmental data around them. To deeply mine the correlation between types of data, this …

Data-driven polynomial chaos expansion for machine learning regression

E Torre, S Marelli, P Embrechts, B Sudret - Journal of Computational …, 2019 - Elsevier
We present a regression technique for data-driven problems based on polynomial chaos
expansion (PCE). PCE is a popular technique in the field of uncertainty quantification (UQ) …

Reliability evaluation of a multi-state system with dependent components and imprecise parameters: A structural reliability treatment

L Yang, X Zhang, Z Lu, Y Fu, D Moens… - Reliability Engineering & …, 2024 - Elsevier
Reliability evaluation of a multi-state system (MSS) with dependent components makes
much practical sense because the independent identical assumption (iid) assumption …

An advanced mixed-degree cubature formula for reliability analysis

D Zhang, S Shen, C Jiang, X Han, Q Li - Computer Methods in Applied …, 2022 - Elsevier
Efficient assessment of mechanical system reliability subject to arbitrary probability
distributions and dependent input parameters signifies an important yet challenging task. To …

Polynomial chaos expansions for dependent random variables

JD Jakeman, F Franzelin, A Narayan, M Eldred… - Computer Methods in …, 2019 - Elsevier
Polynomial chaos expansions (PCE) are well-suited to quantifying uncertainty in models
parameterized by independent random variables. The assumption of independence leads to …

Time-coupled day-ahead wind power scenario generation: A combined regular vine copula and variance reduction method

AB Krishna, AR Abhyankar - Energy, 2023 - Elsevier
Advanced stochastic programming-based power system operations planning requires wind
power forecast in the form of scenarios. Generating wind power scenarios reflecting the …

Optimal energy storage allocation for mitigating the unbalance in active distribution network via uncertainty quantification

H Wang, Z Yan, M Shahidehpour… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Voltage unbalance (VU) in an active distribution network (ADN) could result in increased
network losses and even system instability. The additional uncertainties embedded in ADN …

Analysis of multivariate dependent accelerated degradation data using a random-effect general Wiener process and D-vine Copula

F Sun, F Fu, H Liao, D Xu - Reliability Engineering & System Safety, 2020 - Elsevier
A modern product usually shows multiple performance characteristics that degrade
simultaneously. It is quite common that these degradation processes are dependent due to …