Review on deep learning in structural health monitoring

Rosso, Marco Martino; Cucuzza, Raffaele; Marano, Giuseppe Carlo; Aloisio, Angelo; Cirrincione, Giansalvo

doi:10.1201/9781003322641-34

Cited by 14 publications

(10 citation statements)

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“…In other words, the structured normative framework, the analysis of the influencing factors, and the proposed influencing factor relationship scheme can enhance the repeatability and variability of SHM. It should be pointed out, however, that due to the limitations of the scope and objectives of the review, this work is not focus on the subcategories of SHM to review the specific steps and techniques involved, related to which a lot of valuable works have already been published, e.g., Rosso et al [20] and so on. Also, due to space constraints, specific case studies are not covered in this review.…”

Section: Rationalitymentioning

confidence: 99%

“…However, most current studies in the field always focus on the first phase. Machine learning tools, especially deep learning algorithms, have provided innovative developments in the field and have become increasingly practical, particularly for the extensive use of vibration-based structural damage diagnosis [10,16,20]. For example, Rosso et al [21] utilize the information contained in the raw vibration data in conjunction with the subspace-based damage indicators and use a machine learning artificial neural network model to perform the first level of the damage detection task.…”

Section: Introductionmentioning

confidence: 99%

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Literature Review on the Structural Health Monitoring (SHM) of Sustainable Civil Infrastructure: An Analysis of Influencing Factors in the Implementation

Wang,

2024

Buildings

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Structural health monitoring (SHM) of civil infrastructure is significant for sustainable development. This review aims to identify the factors influencing sustainable civil infrastructure SHM implementation and analyze the properties, paths, and conditions under which they have an impact. The method adopted is a coding study based on Grounded Theory. First, the SHM implementation process in the literature is categorized through open coding to obtain an implementation framework that contains phase and activity levels. Second, based on this framework, a synthesis is conducted to categorize the influencing factors in dimensions of contents and properties through open coding and axial coding. Finally, selective coding is used to extract the factors that interacted across activities to propose a scheme of influencing factor relationships. The main findings of the synthesis are shown as follows: (1) sensor work scheduling and data transmission are promising endeavors to balance economic and environmental sustainability, while social sustainability is mainly in terms of safety and user experience; (2) the success of sustainable civil infrastructure SHM requires a collaborative technical and organizational effort; (3) since the influencing factors at different phases may interact with each other, the implementation process should emphasize forward-looking and holistic thinking.

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Section: Rationalitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Literature Review on the Structural Health Monitoring (SHM) of Sustainable Civil Infrastructure: An Analysis of Influencing Factors in the Implementation

Wang,

2024

Buildings

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“…The first input feature consists of the power spectrum, the second consists of the principal component scores derived from the power spectrum data, and the third case uses the Hotelling T 2 statistic values as input for the machine learning model. (Rosso et al (2022); Rosso et al (2023))…”

Section: Machine Learning-based Software Sensor Developmentmentioning

confidence: 99%

Machine learning-based soft-sensor development for road quality classification

Nagy

Kummer

Abonyi

et al. 2023

Journal of Vibration and Control

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Vibrations in road vehicles cause several harmful effects, health problems can occur for the passengers, and mechanical damage can occur to the vehicle components. Given the health, safety, and financial issues that arise, keeping the road network in good condition and detecting road defects as early as possible requires an extensive monitoring system. Related to this, our study presents the development of hardware and software for a low-cost, multi-sensor road quality monitoring system for passenger vehicles. The developed monitoring system can classify road sections according to their quality parameters into four classes. In order to detect vibrations in the vehicle, accelerometers and gyroscope sensors are installed at several points. Then, a machine learning-based soft-sensor development is introduced. Besides noise filtering, each data point is resampled by spatial frequency to reduce the velocity dependence. Subsequently, a decision tree-based classification model is trained using features from the power spectrum and principal component analysis. The classification algorithm is validated and tested with measurement data in a real-world environment. In addition to reviewing the accuracy of the model, we examine the correlation of the data measured in the cabin and on the suspension to see how much additional information is provided by the sensor on the axle.

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“…The need to preserve and maintain historical buildings, structural heritage and civil infrastructures combined with improved safety standards has led to the increasing use of structural health monitoring (SHM) techniques [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ]. The adoption of reliable and rigorous monitoring systems is fundamental to reducing maintenance costs and at the same time extending the service life of the existing structures.…”

Section: Introductionmentioning

confidence: 99%

Acoustic Emission and Artificial Intelligence Procedure for Crack Source Localization

Melchiorre

Bertetto

Rosso

et al. 2023

Sensors

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The acoustic emission (AE) technique is one of the most widely used in the field of structural monitoring. Its popularity mainly stems from the fact that it belongs to the category of non-destructive techniques (NDT) and allows the passive monitoring of structures. The technique employs piezoelectric sensors to measure the elastic ultrasonic wave that propagates in the material as a result of the crack formation’s abrupt release of energy. The recorded signal can be investigated to obtain information about the source crack, its position, and its typology (Mode I, Mode II). Over the years, many techniques have been developed for the localization, characterization, and quantification of damage from the study of acoustic emission. The onset time of the signal is an essential information item to be derived from waveform analysis. This information combined with the use of the triangulation technique allows for the identification of the crack location. In the literature, it is possible to find many methods to identify, with increasing accuracy, the onset time of the P-wave. Indeed, the precision of the onset time detection affects the accuracy of identifying the location of the crack. In this paper, two techniques for the definition of the onset time of acoustic emission signals are presented. The first method is based on the Akaike Information Criterion (AIC) while the second one relies on the use of artificial intelligence (AI). A recurrent convolutional neural network (R-CNN) designed for sound event detection (SED) is trained on three different datasets composed of seismic signals and acoustic emission signals to be tested on a real-world acoustic emission dataset. The new method allows taking advantage of the similarities between acoustic emissions, seismic signals, and sound signals, enhancing the accuracy in determining the onset time.

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Review on deep learning in structural health monitoring

Cited by 14 publications

References 0 publications

Literature Review on the Structural Health Monitoring (SHM) of Sustainable Civil Infrastructure: An Analysis of Influencing Factors in the Implementation

Literature Review on the Structural Health Monitoring (SHM) of Sustainable Civil Infrastructure: An Analysis of Influencing Factors in the Implementation

Machine learning-based soft-sensor development for road quality classification

Acoustic Emission and Artificial Intelligence Procedure for Crack Source Localization

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