Support vector machine (SVM) is a powerful machine learning technique relying on the
structural risk minimization principle. The applications of SVM in structural reliability analysis …

Reliability-based Optimization is a most appropriate and advantageous methodology for
structural design. Its main feature is that it allows determining the best design solution (with …

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 main task of structural reliability analysis is to estimate failure probability of a studied
structure taking randomness of input variables into account. To consider structural behavior …

An important challenge in structural reliability is to keep to a minimum the number of calls to
the numerical models. Engineering problems involve more and more complex computer …

Applying reliability methods to a complex structure is often delicate for two main reasons.
First, such a structure is fortunately designed with codified rules leading to a large safety …

In structural reliability, an important challenge is to reduce the number of calling the
performance function, especially a finite element model in engineering problem which …

To enhance computational efficiency in reliability analysis, metamodeling has been widely
adopted for reliability assessment. This work develops an efficient reliability method which …

Estimating small probabilities of failure remains quite a challenging task in structural
reliability when models are computationally demanding. FORM/SORM are very suitable …

The aim of the present paper is to develop a strategy for solving reliability-based design
optimization (RBDO) problems that remains applicable when the performance models are …