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.

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

DEVELOPMENT OF A HYBRID METHOD TO DETECT STRUCTURAL DAMAGE

Structural damage detection using dynamic measurements has led to the development of several techniques in the last decades. Most of these methods associate modal variations of the structure to damage like methods based on strain energy deviation, methods based on changes in curvature mode shapes, flexibility matrix analysis, etc. Although these techniques aforementioned are mostly efficient to identify structural alterations in numerical models, they have difficulties in practical applications with experimental data. Thus, hybrid methods to detect the presence of damage directly from raw dynamic measurements in addition to structural modal characteristics can be a promising field of research, involving strategies based on artificial intelligence and higher-order statistics. This work aims to present the preliminary results of a hybrid method to detect structural damage. Using modal data and also higher-order statistics of structural time histories as inputs of artificial intelligence algorithms, the viability of the proposed methodology is initially evaluated. Two applications are analyzed: a simply supported numerical beam and an experimental tested prototype concrete slab. The good results achieved motivate the continuous development of the proposed hybrid method

MIDaS - Um sistema computacional baseado em aplicações web para identificação modal de sistemas dinâmicos deformáveis

The increasing development of algorithms for the determination of the modal characteristics of dynamic systems stems from a continuous improvement of vibration tests and computational processing capabilities. By analyzing these modal parameters, i.e., natural vibration mode shapes and associated frequencies and damping ratios, it is possible to infer that some physical conditions of a structure, such as for example, the degree of integrity, the state of damage and safety margin. However, the identification process of deformable dynamic systems demands sometimes high computational costs. Nevertheless, with the increasing expansion of higher transfer rate broad-band internet the concept of web application becomes a prevailing alternative. This work presents a web application - MIDaS (Modal Identification of Deformable Structural Systems) - implemented in Java language capable to perform the modal identification of deformable systems via internet by using only data obtained through experimental dynamic measurements. Available technologies and web development tools, such as JSP(JavaServer Pages) and JSF(JavaServer Faces) and external libraries for the identification algorithms were used to implement MIDaS. The dynamic treatment of the experimental vibration data is accomplished by means of two time-domain modal identification methods: the Random Decrement Method and the Ibrahim Time Domain Method. Finally, in order to evaluate the developed web application and the implemented identification methods, three cases of dynamically tested structures are explored.Com o aperfeiçoamento dos ensaios vibratórios e o aumento da capacidade de processamento dos computadores, nota-se o crescente desenvolvimento dos algoritmos de avaliação dinâmica, cujo principal objetivo é a determinação das características modais de sistemas dinâmicos deformáveis, em um processo denominado identificação modal. A partir da identificação e análise das características modais, isto é, frequências naturais, taxas de amortecimento e modos de vibração, torna-se possível inferir sobre certas condições físicas de uma estrutura, como por exemplo o grau de integridade, o estado de danos e a margem de segurança. Entretanto, o processo de identificação de sistemas dinâmicos deformáveis às vezes requer um custo computacional elevado. Mas, com a expansão da internet aliado à facilidade de seu acesso com velocidades cada vez maiores, o conceito de aplicação web surge como uma solução a ser empregada. No presente trabalho apresenta-se uma aplicação web implementada em linguagem Java com o objetivo de realizar a identificação modal de sistemas deformáveis a partir do envio, através da internet, de dados obtidos de medições dinâmicas. Para a implementação desta aplicação são utilizadas tecnologias e ferramentas de desenvolvimento web, como o JSP (Java Server Pages) e o JSF (Java Server Faces), além de bibliotecas externas empregadas nos algoritmos de identificação. No que tange ao processo de tratamento dos dados vibracionais dos sistemas deformáveis, são implementados dois métodos para a identificação modal no domínio do tempo: o Método do Decremento Aleatório e o Método de Ibrahim. Finalmente, com o objetivo de avaliar a aplicação desenvolvida e os métodos de identificação implementados, três estudos de casos de estruturas ensaiadas são explorados

Application of symbolic data analysis for structural modification assessment.

Structural health monitoring is a problem which can be addressed at many levels. One of the more promising approaches used in damage assessment problems is based on pattern recognition. The idea is to extract features from the data that characterize only the normal condition and to use them as a template or reference. During structural monitoring, data are measured and the appropriate features are extracted as well as compared (in some sense) to the reference. Any significant deviations from the reference are considered as signal novelty or damage. In this paper, the corpus of symbolic data analysis (SDA) is applied on the one hand for classifying different structural behaviors and on the other hand for comparing any structural behavior to the previous classification when new data become available. For this purpose, raw information (acceleration measurements) and also processed information (modal data) are used for feature extraction. Some SDA techniques are applied for data classification: hierarchy divisive methods, dynamic clustering and hierarchy agglomeratives chemes. Resultsregarding experimental performed onarail way bridge in France are presented in order to show the efficiency of the described methodology. The results show that the SDA methods are efficient to classify and to discriminate structural modifications either considering the vibration data orthe modal parameters. In general, both hierarchy divisive and dynamic cloud methods produce better results compared to those obtained by using the hierarchy agglomerative method. More robust results are given by modal data than By measurement dat

Numerical and Experimental Evaluation of Structural Changes Using Sparse Auto-Encoders and SVM Applied to Dynamic Responses

The present work evaluates the deep learning algorithm called Sparse Auto-Encoder (SAE) when applied to the characterization of structural anomalies. This study explores the SAE’s performance in a supervised damage detection approach to consolidate its application in the Structural Health Monitoring (SHM) field, especially when dealing with real-case structures. The main idea is to use the SAE to extract relevant features from the monitored signals and the well-known Support Vector Machine (SVM) to classify such characteristics within the context of an SHM problem. Vibration data from a numerical beam model and a highway viaduct in Brazil are considered to assess the proposed approach. In both analyzed examples, the efficiency of the implemented methodology achieved more than 99% of correct damage structural classifications, supporting the conclusion that SAE can extract relevant characteristics from dynamic signals that are useful for SHM applications