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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 …
Surrogate-assisted reliability-based design optimization: a survey and a unified modular framework
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 …
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 …
environmental data around them. To deeply mine the correlation between types of data, this …
Data-driven polynomial chaos expansion for machine learning regression
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) …
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
Reliability evaluation of a multi-state system (MSS) with dependent components makes
much practical sense because the independent identical assumption (iid) assumption …
much practical sense because the independent identical assumption (iid) assumption …
An advanced mixed-degree cubature formula for reliability analysis
Efficient assessment of mechanical system reliability subject to arbitrary probability
distributions and dependent input parameters signifies an important yet challenging task. To …
distributions and dependent input parameters signifies an important yet challenging task. To …
Polynomial chaos expansions for dependent random variables
Polynomial chaos expansions (PCE) are well-suited to quantifying uncertainty in models
parameterized by independent random variables. The assumption of independence leads to …
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
Advanced stochastic programming-based power system operations planning requires wind
power forecast in the form of scenarios. Generating wind power scenarios reflecting the …
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
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 …
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
A modern product usually shows multiple performance characteristics that degrade
simultaneously. It is quite common that these degradation processes are dependent due to …
simultaneously. It is quite common that these degradation processes are dependent due to …