Special Issue on Novel Approaches for Structural Health Monitoring

Surace, Cecilia

doi:10.3390/app11167210

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…However, such composite structures are vulnerable during manufacturing process and in-service * Author to whom any correspondence should be addressed. operation, resulting in a sharp decline in structural strength and posing a serious threat to the safety of engineering structures [1]. To avoid catastrophic disasters, intensive effort over the years have been aroused to develop an online damage monitoring system which can be employed to assess the health status of composite structures accurately and timely.…”

Section: Introductionmentioning

confidence: 99%

A sparse, triangle-shaped sensor array for damage orientation and characterization of composite structures

Qiu

Liao

et al. 2023

Smart Mater. Struct.

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Since numerous sensors are needed to create a sensor array for the structural health monitoring of large-scale structures, the equipment quantity and cost considerably increase. This study proposes a sparse, triangle-shaped sensor array to identify, orient, and assess the degree of structural damage in composite constructions in order to overcome this shortcoming. The damage-scattered Lamb waves are recorded by the sparse sensor array with a variety of features that are then extracted and fed into the Support Vector Machine (SVM) classification method. The location and severity of the damage in composite constructions can be determined by training the SVM model. The principle component analysis (PCA) technique is used to compress the wave feature vectors while maintaining the majority of the damage information because the high dimension of the wave feature vectors required a significant amount of calculation during the training phase. Proof-of-concept tests show that the trained model, by utilizing the many properties of Lamb wave signals, can orient and define the degree of damage with excellent accuracy. Multiple lamb wave properties can be used to make up for the triangle sensor array's loss of damage information. In conjunction with the SVM, the triangle-shaped sensor array that was proposed in this study can efficiently make it easier to identify and characterize damage to large-scale structures while using fewer sensors.

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Section: Introductionmentioning

confidence: 99%

A sparse, triangle-shaped sensor array for damage orientation and characterization of composite structures

Qiu

Liao

et al. 2023

Smart Mater. Struct.

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“…To address these issues, engineers and researchers have developed portable, non-destructive detection equipment and bridge loading experimentation for various types of bridges [22]. Structural health monitoring systems and the field of sensing, data storage, and processing have also made progress [23,24]. In terms of intelligent maintenance, fast identification and intelligent analysis of bridge inspection data have been realized, and the building information model [25] is gradually becoming the technical basis for the integration of construction, management, and maintenance.…”

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confidence: 99%

Special Issue on Recent Challenges and Innovations in Long-Span Bridges

Sun,

Xiao,

Song

2023

Applied Sciences

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Vortex-Induced Vibration Performance Analysis of Long-Span Sea-Crossing Bridges Using Unsupervised Clustering

Chen,

Wu,

Yang

et al. 2024

JMSE

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Vortex-induced vibration is a type of wind-induced vibration occurring frequently in large-span sea-crossing bridges under relatively low wind speeds, posing a threat to the structural fatigue performance and driving comfort. Identifying the instantaneous occurrence moments of vortex-induced vibration is a prerequisite for establishing a data-driven prediction model for vortex-induced vibration, and it is of great significance for the monitoring and early warning of vortex-induced vibration performance in bridges. To automatically detect the occurrence moments of vortex-induced vibration and establish a correlation model between vortex-induced vibration amplitude and environmental factors, this study proposes a fuzzy C-means clustering-based classification method. In order to detect the occurrence moments of vortex-induced vibration more finely, only short-term or even instantaneous structural vibration indicators were selected and transformed for distribution as clustering features. The entire detection process could be carried out unsupervised, reducing the manual cost of obtaining vortex-induced vibration information from massive monitoring data. Finally, actual vortex-induced vibration test data from a certain overseas bridge was utilized to verify the feasibility of this method. Based on the classification results, the correlation between vortex-induced vibration amplitude and environmental variables was determined, providing valuable guidance for predicting vortex-induced vibration amplitudes.

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Special Issue on Novel Approaches for Structural Health Monitoring

Abstract: Crucial mechanical systems and civil structures or infrastructures, such as bridges, railways, buildings, wind turbines, aeroplanes and more are subjected during their lifetime to natural deterioration of their structural integrity [...]

Cited by 3 publications

References 16 publications

A sparse, triangle-shaped sensor array for damage orientation and characterization of composite structures

A sparse, triangle-shaped sensor array for damage orientation and characterization of composite structures

Special Issue on Recent Challenges and Innovations in Long-Span Bridges

Vortex-Induced Vibration Performance Analysis of Long-Span Sea-Crossing Bridges Using Unsupervised Clustering

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