An SHM approach using machine learning and statistical indicators extracted from raw dynamic measurements

Structural Health Monitoring using raw dynamic measurements is the subject of several studies aimed at identifying structural modifications or, more specifically, focused on damage assessment. Traditional damage detection methods associate structural modal deviations to damage. Nevertheless, the process used to determine modal characteristics can influence the results of such methods, which could lead to additional uncertainties. Thus, techniques combining machine learning and statistical analysis applied directly to raw measurements are being discussed in recent researches. The purpose of this paper is to investigate statistical indicators, little explored in damage identification methods, to characterize acceleration measurements directly in the time domain. Hence, the present work compares two machine learning algorithms to identify structural changes using statistics obtained from raw dynamic data. The algorithms are based on Artificial Neural Networks and Support Vector Machines. They are initially evaluated through numerical simulations using a simply supported beam model. Then, they are assessed through experimental tests performed on a laboratory beam structure and an actual railway bridge, in France. For all cases, different damage scenarios were considered. The obtained results encourage the development of computational tools using statistical indicators of acceleration measurements for structural alteration assessment.

Unsupervised real‐time SHM technique based on novelty indexes

n/

Editorial: Coaches' role in youth sports performance: early specialization versus long-term development

Youth sports are planned sports programs for children and adolescents with designated coaches, organized practices, and scheduled competitions. Such programs can be organized and implemented at schools (by physical education teachers instead of coaches), as well as in other sports organizations (i.e., federations, associations, local clubs). Primary aim should be to focus on providing young athletes with fundamental motor skills in tandem to their maturation stage. Indeed, these programs are aimed at mass participation rather than on developing elite athletes. The participation in such programs during childhood and adolescence showed to have major benefits in children’s and adolescent’s physical, psychological, and social development. On the other hand, youth sports programs can also serve as a link to talent identification and development programs aiming to identify young athletes with potential for success in adult/elite sport. As they aremass orientated,many youth athletes can be observed which will increase the likelihood of talent identification. Afterwards, these athletes can be guided to high-performance programs aiming to achieve eventually an elite level.info:eu-repo/semantics/publishedVersio

Editorial: Physiological and biomechanical determinants of swimming performance—volume 2

The objective of this Research Topic was to develop and strengthen evidence of training and swimming performance to increase scientific knowledge in the area, considering that understanding the biomechanical, physiological, and neuromuscular determinants of swimming performance is still challenging. This way, 13 manuscripts have been reviewed and approved for this research topic (volume II). We can categorize the 13 manuscripts into three major areas of swimming research: physiology and prescription; biomechanics; performance assessment and prediction. Furthermore, we highlight that 10 of the manuscripts were carried out with the participation of at least two research institutions, often from different countries, which may demonstrate the need for international interchange and exchange of ideas and methodologies across researchers and laboratories.info:eu-repo/semantics/publishedVersio

Análise de dados de solo via métodos de espaço de estado: regressão com coeficientes variáveis

A avaliação da relação entre certas variáveis representando propriedades do solo (tais como nitrogênio total e carbono orgânico) coletadas ao longo de linhas chamadas "transects", é assunto de grande interesse em experimentação agrícola. Este problema tem sido usualmente abordado através de modelos estatísticos padrão de espaço de estado por alguns autores na literatura de ciência do solo. As mais importantes limitações dos procedimentos utilizados na prática são apontados e discutidos neste artigo, sendo relacionadas ao significado dos parâmetros do modelo e a sua interpretação prática. A abordagem padrão de espaço de estado, que é baseada em uma estrutura autoregressiva, não apresenta nenhum parâmetro que expressa a relação entre as variáveis no mesmo ponto do espaço, mas somente em pontos defasados. Além disso, os parâmetros do modelo (na matriz de transição) tem um significado global e não local, não expressando diretamente a heterogeneidade do solo. Desta forma, o objetivo aqui é propor uma abordagem alternativa de espaço de estado, baseada em modelos de regressão com coeficientes variando ao longo do espaço de modo a evitar estas limitações. Dados de nitrogênio total e carbono orgânico do solo foram coletados de um Latossolo. Eles foram medidos na camada de 0 – 0,20 m ao longo de uma transeção de 194 m, totalizando 97 amostras espaçadas entre si de 2 m, entre duas curvas de contorno adjacentes. Os resultados mostram as vantagens comparativas do método proposto em relação ao método de espaço de estados padrão. Tais vantagens estão relacionadas a uma mais adequada incorporação da heterogeneidade do solo ao longo da transeção espacial resultando em um melhor ajuste do modelo e a uma maior flexibilidade no processo de construção do modelo permitindo uma fácil interpretabilidade dos coeficientes estimados.The assessment of the relationship among soil properties (such as total nitrogen and organic carbon) taken along lines called transects is a subject of great interest in agricultural experimentation. This question has been usually approached through standard state-space methods by some authors in the soil science literature. Important limitations of the mentioned procedures used in practice are pointed out and discussed in this paper, specially those related to the model parameters, meaning and practical interpretation. In the standard state-space approach, based on an autoregressive structure, it does not present any parameters that express the variables relationship at the same point in space, but only at lagged points. Also, its model parameters (in the transition matrix) have a global meaning and not a local one, not expressing more directly the soil heterogeneity. Therefore, the objective here is to propose an alternative state-space approach, based on dynamic (space-varying parameters) regression models in order to avoid the mentioned drawbacks. Soil total nitrogen and soil organic carbon samples were collected on a Typic Haplustox. Samples were taken along a line (transect) located in the middle of two adjacent contour lines. The transect samples, totaling 97, were collected in the plow layer (0-0.20 m) at points spaced 2 meters appart. Results show the comparative advantages of the proposed method (based on an alternative state-space approach) in relation to the standard state-space analysis. Such advantages are related to a more adequate incorporation of soil heterogeneity along the spatial transect resulting in a better model fitting, and greater flexibility of the model's building process with an easier interpretability of the local model coefficients

Análise de dados de solo via métodos de espaço de estado: regressão com coeficientes variáveis

The assessment of the relationship among soil properties (such as total nitrogen and organic carbon) taken along lines called transects is a subject of great interest in agricultural experimentation. This question has been usually approached through standard state-space methods by some authors in the soil science literature. Important limitations of the mentioned procedures used in practice are pointed out and discussed in this paper, specially those related to the model parameters, meaning and practical interpretation. In the standard state-space approach, based on an autoregressive structure, it does not present any parameters that express the variables relationship at the same point in space, but only at lagged points. Also, its model parameters (in the transition matrix) have a global meaning and not a local one, not expressing more directly the soil heterogeneity. Therefore, the objective here is to propose an alternative state-space approach, based on dynamic (space-varying parameters) regression models in order to avoid the mentioned drawbacks. Soil total nitrogen and soil organic carbon samples were collected on a Typic Haplustox. Samples were taken along a line (transect) located in the middle of two adjacent contour lines. The transect samples, totaling 97, were collected in the plow layer (0-0.20 m) at points spaced 2 meters appart. Results show the comparative advantages of the proposed method (based on an alternative state-space approach) in relation to the standard state-space analysis. Such advantages are related to a more adequate incorporation of soil heterogeneity along the spatial transect resulting in a better model fitting, and greater flexibility of the model's building process with an easier interpretability of the local model coefficients.A avaliação da relação entre certas variáveis representando propriedades do solo (tais como nitrogênio total e carbono orgânico) coletadas ao longo de linhas chamadas transects, é assunto de grande interesse em experimentação agrícola. Este problema tem sido usualmente abordado através de modelos estatísticos padrão de espaço de estado por alguns autores na literatura de ciência do solo. As mais importantes limitações dos procedimentos utilizados na prática são apontados e discutidos neste artigo, sendo relacionadas ao significado dos parâmetros do modelo e a sua interpretação prática. A abordagem padrão de espaço de estado, que é baseada em uma estrutura autoregressiva, não apresenta nenhum parâmetro que expressa a relação entre as variáveis no mesmo ponto do espaço, mas somente em pontos defasados. Além disso, os parâmetros do modelo (na matriz de transição) tem um significado global e não local, não expressando diretamente a heterogeneidade do solo. Desta forma, o objetivo aqui é propor uma abordagem alternativa de espaço de estado, baseada em modelos de regressão com coeficientes variando ao longo do espaço de modo a evitar estas limitações. Dados de nitrogênio total e carbono orgânico do solo foram coletados de um Latossolo. Eles foram medidos na camada de 0 – 0,20 m ao longo de uma transeção de 194 m, totalizando 97 amostras espaçadas entre si de 2 m, entre duas curvas de contorno adjacentes. Os resultados mostram as vantagens comparativas do método proposto em relação ao método de espaço de estados padrão. Tais vantagens estão relacionadas a uma mais adequada incorporação da heterogeneidade do solo ao longo da transeção espacial resultando em um melhor ajuste do modelo e a uma maior flexibilidade no processo de construção do modelo permitindo uma fácil interpretabilidade dos coeficientes estimados.37137

Experimental and numerical evaluation of viscoelastic sandwich beams

Viscoelastic materials can dissipate a large amount of energy when subjected to cyclic shear deformations, but they have low bearing capacity. Therefore they are often employed as a damping layer in sandwich structures. These sandwich structures present a high damping ratio and simple application. In order to design sandwich structures, many aspects ranging from computer modeling to laboratory testing should be considered. In this study, a test set of experiments were performed and results are compared with a numerical GHM (Golla, Hughes and Mc Tavish method) based model, in order to establish a method to support viscoelastic sandwich beam design. In this way, starting from the dynamic properties of a viscoelastic material, a numerical model is used to evaluate the behavior of these structures. Comparisons with uncontrolled structures are also presented, showing the dissipative characteristics of this passive control.

A viscoelastic sandwich beam finite element model

Entre os sistemas de controle passivo para atenuação de vibrações em estruturas, aqueles que usam materiais visco-elásticos como núcleo dissipador de energia de vibração em vigas sanduíche são abordados neste trabalho. Apresenta-se um modelo numérico baseado numa formulação denominada GHM (Golla-Hughes Method) que simula o comportamento dinâmico de materiais visco-elásticos. Os parâmetros do GHM usados na caracterização do material visco-elástico foram determinados experimentalmente e um modelo de elemento finito sanduíche foi obtido e validado através de comparações entre resultados numéricos e experimentais, demonstrando um desempenho favorável do modelo proposto

Redes neurais e modelos de espaço de estados para o estudo da relação entre propriedades do solo

O estudo da relação entre as propriedades do solo é de grande importância na área agronômica objetivando um manejo racional dos recursos naturais do meio ambiente e um aumento na produtividade agrícola. Tradicionalmente este estudo tem sido realizado usando modelos de regressão estática os quais não levam em consideração a estrutura espacial envolvida. Este trabalho teve o objetivo de avaliar a relação entre uma variável de determinação mais cara e demorada (por exemplo, nitrogênio total do solo) e outras de mais barata e rápida determinação (p.e., carbono orgânico do solo, pH, etc.). Duas importantes classes de modelos (espaço de estados linear e redes neurais) são usadas para predição e comparadas aos modelos de regressão uni- e multivariados aqui usados como referência. Para tal, em uma área experimental cultivada com aveia, situada em Jaguariúna, SP (22º41' S e 47º00' W), amostras de um solo classificado como Latossolo foram coletadas na camada arável ao longo de uma transeção espacial de 194 m, eqüidistantes de 2 m. Os modelos de rede neural recorrente e de espaço de estados padrão tiveram uma melhor performance preditiva da variável nitrogênio total do solo quando comparados aos modelos de regressão padrão. Entre os modelos de regressão padrão o Autoregressivo Vetorial teve um melhor desempenho preditivo da variável nitrogênio total do solo.The study of soil property relationships is of great importance in agronomy aiming for a rational management of environmental resources and an improvement of agricultural productivity. Studies of this kind are traditionally performed using static regression models, which do not take into account the involved spatial structure. This work has the objective of evaluating the relation between a time-consuming and "expensive" variable (like soil total nitrogen) and other simple, easier to measure variables (as for instance, soil organic carbon, pH, etc.). Two important classes of models (linear state-space and neural networks) are used for prediction and compared with standard uni- and multivariate regression models, used as reference. For an oat crop cultivated area, situated in Jaguariuna, SP, Brazil (22º41' S, 47º00' W) soil samples of a Typic Haplustox were collected from the plow layer at points spaced 2 m apart along a 194 m spatial transect. Recurrent neural networks and standard state-space models had a better predictive performance of soil total nitrogen as compared to the standard regression models. Among the standard regression models the Vector Auto-Regression model had a better predictive performance for soil total nitrogen

A novel natural frequency-based technique to detect structural changes using computational intelligence

Structural changes are usually associated to damage occurrence, which can be caused by design flaws, constructive problems, unexpected loading, natural events or even natural aging. The structural degrading process affects the dynamic behavior, leading to modifications in modal characteristics. In general, natural frequencies are sensitive indicators of structural integrity and tend to become slightly smaller in the presence of damage. Despite this, it is very difficult to state the relationship between decreasing values of natural frequencies and structural damage, since the dynamic properties are also influenced by uncertainty on experimental data and temperature variation. In order to contribute to improving the quality of natural frequency-based methods used for damage identification, this paper presents a simple and efficient strategy to detect structural changes in a set of experimental tests from a real structure using a computational intelligence method. For a full time monitored structure, the evolution of natural frequencies and temperature are used as input data for a Support Vector Machine (SVM) algorithm. The technique consists on detecting structural changes and when they occur based on the structural dynamic behavior. The results obtained on a historic tower show the capacity of the proposed methodology for damage identification and structural health monitoring