Reginaldo Santos

A novel unsupervised approach based on a genetic algorithm for structural damage detection in bridges

This paper proposes 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. This approach is rooted in the search of an optimal number of clusters in the feature space, representing the main state conditions of a structural system, also known as the main structural components. This genetic-based clustering approach is supported by a novel concentric hypersphere algorithm to regularize the number of clusters and mitigate the cluster redundancy. The superiority of the GADBA is compared to state-of-the-art approaches based on the Gaussian mixture models and the Mahalanobis squared distance, on data sets from monitoring systems installed on two bridges: the Z-24 Bridge and the Tamar Bridge. The results demonstrate that the proposed approach is more efficient in the task of fitting the normal condition and its structural components. This technique also revealed to have better classification performance than the alternative ones in terms of false-positive and false-negative indications of damage, suggesting its applicability for real-world structural health monitoring applications.

A global expectation-maximization based on memetic swarm optimization for structural damage detection

During the service life of engineering structures, structural management systems attempt to manage all the information derived from regular inspections, evaluations and maintenance activities. However, the structural management systems still rely deeply on qualitative and visual inspections, which may impact the structural evaluation and, consequently, the maintenance decisions as well as the avoidance of collapses. Meanwhile, structural health monitoring arises as an effective discipline to aid the structural management, providing more reliable and quantitative information; herein, the machine learning algorithms have been implemented to expose structural anomalies from monitoring data. In particular, the Gaussian mixture models, supported by the expectation-maximization (EM) algorithm for parameter estimation, have been proposed to model the main clusters that correspond to the normal and stable state conditions of a structure when influenced by several sources of operational and environmental variations. Unfortunately, the optimal parameters determined by the EM algorithm are heavily dependent on the choice of the initial parameters. Therefore, this paper proposes a memetic algorithm based on particle swarm optimization (PSO) to improve the stability and reliability of the EM algorithm, a global EM (GEM-PSO), in searching for the optimal number of components (or data clusters) and their parameters, which enhances the damage classification performance. The superiority of the GEM-PSO approach over the state-of-the-art ones is attested on damage detection strategies implemented through the Mahalanobis and Euclidean distances, which permit one to track the outlier formation in relation to the main clusters, using real-world data sets from the Z-24 Bridge (Switzerland) and Tamar Bridge (United Kingdom).

A Global Expectation–Maximization Approach Based on Memetic Algorithm for Vibration-Based Structural Damage Detection

This paper proposes a novel unsupervised damage detection approach based on a memetic algorithm that establishes the normal or undamaged condition of a structural system as data clusters through a global xpectation–maximization technique, using only damage-sensitive features extracted from output-only vibration measurements. The health state is then discriminated by considering the Mahalanobis squared distance between the learned clusters and a new observation. The proposed approach is compared with state-of-the-art ones by taking into account real-world data sets from the Z-24 Bridge (Switzerland), where several damage scenarios were performed. The results indicated that the proposed approach can be applied in structural health monitoring applications where life safety, economic, and reliability issues are the most important motivations to consider.

Deep principal component analysis: An enhanced approach for structural damage identification

The structural health monitoring relies on the continuous observation of a dynamic system over time to identify its actual condition, detect abnormal behaviors, and predict future states. The regular changes in environmental factors have been reported as one of the main challenges for the application of structural health monitoring systems. These influences in the structural responses are in general nonlinear, affecting the damage-sensitive features in the most varied forms. The usual process to remove these normal changes is referred to as data normalization. In that regard, principal component analysis is probably the most studied algorithm in structural health monitoring, having numerous versions to learn strong nonlinear normal changes. However, in most cases, not all variability is properly accounted for via the existing nonlinear principal component analysis approaches, resulting in poor damage detection and quantification performances. In this article, a new paradigm based on deep principal component analysis, rooted in the deep learning field, is presented to overcome these limitations. This approach extracts the most salient underlying feature distributions by stacking multiple feedforward neural networks trained to learn an identity mapping of the input variables, where the network inputs are reproduced into the outputs. Similar to the traditional nonlinear principal component analysis–based approach, our approach identifies a nonlinear output-only model of an undamaged structure by comprising modal features into an internal bottleneck layer, which implicitly represents the independent environmental factors. The proposed technique is validated through the application on a progressively damaged prestressed concrete bridge and a three-span suspension bridge. 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.

A semi-autonomous particle swarm optimizer based on gradient information and diversity control for global optimization

Abstract The deterministic optimization algorithms far outweigh the non-deterministic ones on unimodal functions. However, classical algorithms, such as gradient descent and Newtons method, are strongly dependent on the quality of the initial guess and easily get trapped into local optima of multimodal functions. On the contrary, non-deterministic optimization methods, such as particle swarm optimization and genetic algorithms perform global optimization, however they waste computational time wandering the search space as a result of the random walks influence. This paper presents a semi-autonomous particle swarm optimizer, termed SAPSO, which uses a gradient-based information and diversity control to optimize multimodal functions. The proposed algorithm avoids the drawbacks of deterministic and non-deterministic approaches, by reducing computational efforts of local investigation (fast exploitation with gradient information) and escaping from local optima (exploration with diversity control). The experiments revealed promising results when SAPSO is applied on a suite of test functions based on De Jongs benchmark optimization problems and compared to other PSO-based algorithms.

GAVGA: A Genetic Algorithm for Viral Genome Assembly

Bioinformatics has grown considerably since the development of the first sequencing machine, being today intensively used with the next generation DNA sequencers. Viral genomes represent a great challenge to bioinformatics due to its high mutation rate, forming quasispecies in the same infected host. In this paper, we implement and evaluate the performance of a genetic algorithm, named GAVGA, through the quality of a viral genome assembly. The assembly process works by first clustering the reads that share a common substring called seed and for each cluster, checks if there are overlapping reads with a given similarity percentage using a genetic algorithm. The assembled data are then compared to Newbler, SPAdes and ABySS assemblers, and also to a viral assembler such as VICUNA, which confirms the feasibility of our approach. GAVGA was implemented in python 2.7+ and can be downloaded at https://sourceforge.net/projects/gavga-assembler/.

Binder Identification Using Pattern Recognition on Phantom Measurements

This paper proposes an automatic method for the identification of twisted pairs (TPs) sharing the same binder, based on the analysis of phantom circuit measurements. This type of circuit is often used for improving data transmission rates in communication systems, but in this paper, phantoming is used to reveal if a four-wire loop composed of two TPs is close enough and well balanced in order to be considered in the same binder. The method uses four features extracted from scattering parameter measures in phantom-mode between two TPs. These features are related to the presence of periodicities and impedance mismatch between the measurement device and the four-wire transmission line. The identification is done via application of two pattern recognition techniques, support vector machines and K-means, on scattering parameter obtained from the phantom-mode measurement of two TPs. This paper also describes a method to determine the length of the two TPs that share the same binder. Laboratory results confirm the accuracy of the proposed methods.

Clustering Strategies for Binder Identification Using Phantom Measurements

This paper proposes an automatic method for the identification of twisted pairs sharing the same binder, based on the analysis of phantom circuit measurements. This type of circuit is often used for improving data transmission rates in communication systems, but in this work, phantoming is used to reveal if a 4-wire loop composed by two twisted pairs are close enough and well balanced in order to be considered in the same binder. The identification is done via application of two pattern recognition techniques, K-means and Gaussian Mixture Model, on S11 parameter obtained from the phantom-mode measurement of two twisted pairs. This paper also presents an automatic method to labeling the clusters and a method to estimate the length of the two twisted pairs that share the same binder, using Time-Domain Reflectometry analysis. Laboratory results confirm the accuracy of the proposed methods.