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

Finotti, Rafaelle Piazzaroli; Cury, Alexandre; Barbosa, Fernando Policarpo

doi:10.1590/1679-78254942

Cited by 52 publications

(38 citation statements)

References 15 publications

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“…Thus, strategies that consider both acceleration measurements using statistical analysis and computational intelligence to assess the structure's dynamic behavior have been taken as a promising field of research in recent years. 18,19 The direct use of acceleration measurements may ease the problem of damage detection as the procedure becomes more straightforward since the need for a modal identification process is ruled out. Moreover, there is no need to know the excitation source to assess the structure's behavior since it is possible to deal with the output measurements directly.…”

Section: Introductionmentioning

confidence: 99%

A hybrid learning strategy for structural damage detection

Nunes

Finotti

Barbosa

et al. 2020

Structural Health Monitoring

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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.

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Section: Introductionmentioning

confidence: 99%

A hybrid learning strategy for structural damage detection

Nunes

Finotti

Barbosa

et al. 2020

Structural Health Monitoring

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“…Though many types of ANNs are available, simple feedforward networks are still very popular for the structure condition monitoring of buildings. An exemplary application of ANNs in the considered field can be found in many areas, e.g., in damage detection based on time signals registered during dynamic measurements [51], in an assessment of building damage and safety after an earthquake [52], and in the identification of internal forces in pretensioned bolts [4].…”

Section: Dic Measurements Under Harmonic Excitationmentioning

confidence: 99%

Detection of Anomaly in a Pretensioned Bolted Beam-to-Column Connection Node Using Digital Image Correlation and Neural Networks

2020

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Bolted connections, commonly applied in civil engineering structures, have many advantages. According to current trends, bolted connections in steel structures are designed as prestressed ones. Unfortunately, precise control of the prestressing forces is difficult, while the loosening (due to, e.g., dynamic interactions) may be dangerous for the entire structure. There are many control methods applied in the determination of the tightening level, among which are vision-based methods. The methods described so far enable—thanks to image processing—damage detection in connections with visible connectors. The level of the considered loosening was significant—in many cases, changes in connectors were visible with the naked eye, whereas the procedure presented here enables the detection of very small changes, impossible to detect without manual inspection of every single connector. It is not necessary to observe the connectors directly, but the near surrounding of the node should be visible. As a measurement technique, Digital Image Correlation (DIC) was used. The applied measurement method and the high sensitivity of the presented procedure makes the presented research original. The currently presented procedure, employing Artificial Neural Networks, based on the laboratory examination of an example of one selected beam-to-column connection of a two-story steel portal frame, was perfect in the detection of a change and in the determination of the number of loosened rows, 95%, and their location, 94%, with the number of false alarms below 1%.

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“…The degrading process alters the physical properties of the structure, such as mass and stiffness, which influence its natural frequencies, mode shapes, and damping ratios. In the last few years, due to the evolution of computer and information technologies, increasing attention has been given to the application of Computational Intelligence (CI) in structural novelty identification [4,5,6]. Among all possible CI algorithms, those based on deep learning appear as promising alternatives to traditional techniques.…”

Section: Introductionmentioning

confidence: 99%

Vibration-Based Anomaly Detection Using Sparse Auto-Encoder and Control Charts

Finotti¹,

Gentile²,

Barbosa³

et al. 2020

XI International Conference on Structural Dynamics

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

Cited by 52 publications

References 15 publications

A hybrid learning strategy for structural damage detection

A hybrid learning strategy for structural damage detection

Detection of Anomaly in a Pretensioned Bolted Beam-to-Column Connection Node Using Digital Image Correlation and Neural Networks

Vibration-Based Anomaly Detection Using Sparse Auto-Encoder and Control Charts

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