Abstract Machine learning (ML) may provide a new methodology to directly learn from raw
data to develop constitutive models for soils by using pure mathematic skills. It has …

Monte Carlo methods have attracted constant and even increasing attention in structural
reliability analysis with a wide variety of developments seamlessly presented over decades …

This paper presents a survey on the development and use of Artificial Neural Network (ANN)
models in structural reliability analysis. The survey identifies the different types of ANNs, the …

This paper examines a methodology for computing the probability of structural failure by
combining neural networks (NN) and Monte Carlo simulation (MCS). MCS is a powerful tool …

The reliability analysis of complex structures is hindered by the implicit nature of the limit-
state function. For their approximation use has been made of the Response Surface Method …

The last decades have witnessed the development of methods for solving struc tural
reliability problems, which emerged from the efforts of numerous re searchers all over the …

In structural reliability, simulation methods are oriented to the estimation of the probability
integral over the failure domain, while solver-surrogate methods are intended to …

This paper proposes the novel full-field order-reduced Gaussian Processes (GPs) emulators
to address the difficult yet underinvestigated problem of quantifying high-dimensional …

A neural network–based methodology is proposed for the rapid evaluation of the seismic
demand using data extracted from ground motion records. Limit-state fragilities for a moment …

In the paper the microstructural phenomena in terms of average grain size occurring in
friction stir welding (FSW) processes are focused. A neural network was linked to a finite …