Bayesian inference is a powerful technique for damage/flaw detection in critical structures.
This paper explores the application of Bayesian inference to identify the flaws …

We propose a guided Bayesian inference approach for detection and quantification of
multiple flaws in structures without a priori knowledge on the number of flaws. Uncertainties …

Abstract Combining Scaled Boundary Finite Element Method (SBFEM) and machine
learning algorithm, the flaw information in the structure is quantitatively reversed based on …

We present a novel algorithm based on the extended finite element method (XFEM) and an
enhanced artificial bee colony (EABC) algorithm to detect and quantify multiple flaws in …

A comprehensive framework that combines classification and regression techniques through
machine learning (ML) algorithms to address the inverse problem of flaw identification and …

This paper presents a novel algorithm for detection of multiple flaws in structures as an
inverse process, where the forward problem is based on eXtended Finite Element Method …

We present a novel multiscale algorithm for nondestructive detection of multiple flaws in
structures, within an inverse problem type setting. The key idea is to apply a two‐step …

A novel methodology for multiple flaw detection is presented in this study. It combines the
dynamic extended finite element method (XFEM) with machine learning for the first time. The …

The extended finite element formulation (XFEM) combined with genetic algorithms (GAs)
have previously been shown to be very effective in the detection of flaws in structures. By …

This study proposes a novel anomaly detection approach focusing on structural components
which do not have a baseline model and with complex boundary conditions that cannot be …