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.

Several approaches can be found in the scientific literature when the subject is damage detection based on vibration signals. In the last few years, increasing attention has been given to the application of Computational Intelligence algorithms in structural novelty identification. In more details, the powerful data mapping capability of computational deep learning methods has been recently exploited to develop strategies of structural health monitoring through appropriate characterization of dynamic responses. Therefore, the present work is aimed at investigating the capability of a deep learning algorithm called Sparse Auto-Encoder (SAE) to identify structural alterations of the Z24 bridge, a classical benchmark for integrity assessment studies. The main idea is to characterize the Z24 dynamic responses via SAE models and, subsequently, to detect the onset of abnormal behavior through the well-known Shewhart T control chart (T 2 -statistic), calculated with SAE extracted features. An advantage of the proposed methodology is that data are processed directly in the time domain, avoiding modal parameters estimation and tracking analysis. Moreover, control charts are considered suitable tools for continuous monitoring due to their relatively simple implementation. The obtained results demonstrate that the proposed strategy based on SAE and Shewhart T control chart has potential to be explored in structural damage detection problems, since it is able to distinguish between the two investigated scenarios (i.e., undamaged and damaged) of Z24 bridge.

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Over the past decades, several methods for structural health monitoring have been developed and employed in various practical applications. Some of these techniques aimed to use raw dynamic measurements to detect damage or structural changes. Desirably, structural health monitoring systems should rely on computational tools capable of evaluating the information acquired from the structure continuously, in real time. However, most damage detection techniques fail to identify novelties automatically (e.g. damage, abnormal behaviors, and among others), rendering human decisions necessary. Recent studies have shown that the use of statistical parameters extracted directly from raw time domain data, such as acceleration measurements, could provide more sensitive responses to damage with less computational effort. In addition, machine learning techniques have never been more in trend than nowadays. In this context, this article proposes an original approach based on the combination of statistical indicators—to characterize acceleration measurements in the time domain—and computational intelligence techniques to detect damage. The methodology consists in the combined use of supervised (artificial neural networks) and unsupervised ( k-means clustering) learning classification methods for the construction of a hybrid classifier. The objective is to detect not only structural states already known but also dynamic behaviors that have not been identified yet, that is, novelties. The main purpose is to allow a real-time structural integrity monitoring, providing responses in an automatic and continuous way while the structure is under operation. The robustness of the proposed approach is evaluated using data obtained from numerical simulations and experimental tests performed in laboratory and in situ. Results achieved so far attest a promising performance of the hybrid classifier.

Automated modal identification procedures are attracting the interest of the Structural Health Monitoring (SHM) community as those techniques are capable of continuously providing information which are useful to timely assess the health state of a structure. Within this context, the paper presents the development and application of a vibration-based novelty detection strategy using automatically identified resonant frequencies and the Support Vector Machine (SVM) approach. The SVM is a popular technique for forming decision boundaries that separate data into two or more classes without any prior assumptions on the propriety of the data. The developed procedure is exemplified using frequency data collected during the continuous dynamic monitoring of a historic masonry tower that underwent slight permanent variation of the natural frequencies after the occurrence of a far-field earthquake. The obtained results highlight the capability of the novelty strategy to reveal slight damage and to detect anomalies in the structural behaviour.

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.

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