Claudomiro Sales

TL;DR: Four kernel-based algorithms for damage detection under varying operational and environmental conditions, namely based on one-class support vector machine, support vector data description, kernel principal components analysis and greedy kernel principal component analysis are presented.

TL;DR: A novel unsupervised and nonparametric genetic algorithm for decision boundary analysis (GADBA) to support the structural damage detection process, even in the presence of linear and nonlinear effects caused by operational and environmental variability is proposed.

TL;DR: This is the first method that applies a multi-objective approach to data imputation, based on the NSGA-II, which is suitable for mixed-attribute datasets and takes into account information from incomplete instances and the modeling task.

TL;DR: In this paper, a hybrid approach based on a standard genetic algorithm (GA) was proposed to improve the stability of the EM algorithm on the searching of the optimal number of clusters and their parameters, strengthening the damage classification performance.

TL;DR: The experimental results demonstrate that capturing the most slight nonlinear variations in the data can lead to improved data normalization and, consequently, better damage detection and quantification performances.