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 …
Recent advances and applications of surrogate models for finite element method computations: a review
The utilization of surrogate models to approximate complex systems has recently gained
increased popularity. Because of their capability to deal with black-box problems and lower …
increased popularity. Because of their capability to deal with black-box problems and lower …
Digital twin: Values, challenges and enablers from a modeling perspective
Digital twin can be defined as a virtual representation of a physical asset enabled through
data and simulators for real-time prediction, optimization, monitoring, controlling, and …
data and simulators for real-time prediction, optimization, monitoring, controlling, and …
Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial
On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an
essential layer of safety assurance that could lead to more principled decision making by …
essential layer of safety assurance that could lead to more principled decision making by …
Floating offshore wind turbines: Current status and future prospects
Offshore wind energy is a sustainable renewable energy source that is acquired by
harnessing the force of the wind offshore, where the absence of obstructions allows the wind …
harnessing the force of the wind offshore, where the absence of obstructions allows the wind …
A survey on high-dimensional Gaussian process modeling with application to Bayesian optimization
Bayesian Optimization (BO), the application of Bayesian function approximation to finding
optima of expensive functions, has exploded in popularity in recent years. In particular, much …
optima of expensive functions, has exploded in popularity in recent years. In particular, much …
Failure mode classification and bearing capacity prediction for reinforced concrete columns based on ensemble machine learning algorithm
DC Feng, ZT Liu, XD Wang, ZM Jiang… - Advanced Engineering …, 2020 - Elsevier
Failure mode (FM) and bearing capacity of reinforced concrete (RC) columns are key
concerns in structural design and/or performance assessment procedures. The failure types …
concerns in structural design and/or performance assessment procedures. The failure types …
Rare event estimation using polynomial-chaos kriging
Structural reliability analysis aims at computing the probability of failure of systems whose
performance may be assessed by using complex computational models (eg, expensive-to …
performance may be assessed by using complex computational models (eg, expensive-to …
A surrogate-assisted stochastic optimization inversion algorithm: Parameter identification of dams
Dynamic monitoring data plays an essential role in the structural health monitoring of dams.
This study presents a surrogate-assisted stochastic optimization inversion (SASOI) …
This study presents a surrogate-assisted stochastic optimization inversion (SASOI) …
Enhanced Kriging leave-one-out cross-validation in improving model estimation and optimization
Leave-one-out cross-validation (LOOCV) is a widely used technique in model estimation
and selection of the Kriging surrogate model for engineering problems, such as structural …
and selection of the Kriging surrogate model for engineering problems, such as structural …