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 …

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 …

In recent years, offshore wind power generation technology has developed rapidly around
the world, making important contributions to the further development of renewable energy …

The classical reliability analysis methods, due to the ever-increasing complexity of
engineering structure, may lead to higher and higher calculation errors and costs. The …

The accurate estimations of the failure probability with low-computational burden play a vital
role in structural reliability analyses. Due to high-calculation cost and time-consuming Monte …

Active learning methods have recently surged in the literature due to their ability to solve
complex structural reliability problems within an affordable computational cost. These …

The present work reviews the implementation of adaptive metamodeling for reliability
analysis with emphasis in four main types of metamodels: response surfaces, polynomial …

Structural safety of functionally graded material considering multi-source uncertainties
significantly impacts its application in thermal protection systems. To accurately quantify the …

Machine learning-based reliability analysis methods have shown great advancements for
their computational efficiency and accuracy. Recently, many efficient learning strategies …

This paper proposes a system active learning Kriging (SALK) method to handle system
reliability-based design optimization (SRBDO) problems, where responses of all constraints …