Robert J. Barthorpe

TL;DR: In this article, machine learning algorithms based on Artificial Neural Networks (ANNs) including an Auto-Associative Neural Network (AANN) based on a standard ANN form and a novel approach to auto-association with Radial Basis Functions (RBFs) networks are used, which are optimised for fast and efficient runs.

TL;DR: This paper presents a review of the state-of-the-art for digital twins in the application domain of engineering dynamics, with a focus on applications in dynamics because they offer some of the most challenging aspects of creating an effective digital twin.

TL;DR: This paper introduces a new scheme for SHM by exploiting robust multivariate outlier statistics in order to investigate if the selected features are free from multiple outliers before such features can be selected for either supervised or unsupervised analysis.

TL;DR: It is argued here that more general frameworks are now emerging for nonlinear system identification, which are capable of addressing substantial ranges of problems and one of these frameworks is based on evolutionary optimisation (EO); it is a framework developed by the authors in previous papers and extended here.

TL;DR: In this article, the authors explore the potential of a simple experimental strategy, which involves adding masses to the structure, in the attempt to extract features for novelty detection, and show similar patterns in both cases which suggests a potential use of the method for higher level damage detection.