Recent Advances in Structural Health Monitoring, 2nd Volume

Topic Information

Dear Colleagues,

Following the great success of the Topic Proposal "Recent Advances in Structural Health Monitoring", which was closed on March 31, 2023, and in which 72 papers were published, we have decided to launch a second edition, which we hope will be as successful and provide as much insight as the first.

All the theoretical and technological aspects of structural control and health monitoring theory on materials and structures are covered within the concept of Structural Health Monitoring (SHM).

There are currently a number of highly effective, non-destructive evaluation tools available for SHM monitoring. Nondestructive testing (NDT) refers to a group of non-invasive inspection procedures that are used to assess material qualities, components, and complete process units. Damage mechanisms can also be detected, characterized, and measured using these techniques.

The emphasis of this Topical Collection is the crucial field of damage identification and maintenance of modern and historical buildings, as well as for aerospace and mechanical engineering structures and civil infrastructure.

Original contributions using analytical, numerical, and experimental methods are sought in the main areas of monitoring and control of materials and structures.

Topics will include the more classic areas of monitoring, such as data acquisition, signal processing, and sensor technology, by using acoustic emission damage detection or vibration-based identification methods. Furthermore, in the field of mechanics, passive, active, and semi-active schemes and implementations to perform systems control diagnostics are also desired topics.

Other areas of great interest are those of remote data analysis methodologies, such as wireless communications, control of monitoring systems, sensor–logger combinations for mobile applications, and those on multifunctional materials and structures or artificial intelligence tools.

Among others, the methodologies that involve the use of embedded N/MEMS sensors for local damage detection, corrosion sensors, optical fiber sensors, sonic–ultrasonic tests, digital image correlation, tomography techniques, Raman and terahertz spectroscopy, and electromagnetic analysis, which allow for the evaluation of the level of structural damage and its evolution over time, will be incorporated in this Topical Collection.

As a result, the aim of this new initiative is to bring together researchers working in the field of NDT-SHM, both at the material and structure scales. It is our desire to provide novel insights into the application of NDT to a wide variety of materials and structures in the fields of Civil Engineering and Architecture as well as in Mechanical Engineering.

Prof. Dr. Giuseppe Lacidogna
Dr. Alessandro Grazzini
Dr. Gianfranco Piana
Prof. Dr. Sanichiro Yoshida
Dr. Guang-Liang Feng
Prof. Dr. Jie Xu
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.7	4.5	2011	16.9 Days	CHF 2400	Submit
CivilEng civileng	-	2.0	2020	37.7 Days	CHF 1200	Submit
Materials materials	3.4	5.2	2008	13.9 Days	CHF 2600	Submit
Remote Sensing remotesensing	5.0	7.9	2009	23 Days	CHF 2700	Submit
Sensors sensors	3.9	6.8	2001	17 Days	CHF 2600	Submit

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Published Papers (2 papers)

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All Applied Sciences CivilEng Materials Remote Sensing Sensors

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20 pages, 8234 KiB  

Open AccessArticle

Rock Layer Classification and Identification in Ground-Penetrating Radar via Machine Learning

by Hong Xu, Jie Yan, Guangliang Feng, Zhuo Jia and Peiqi Jing

Remote Sens. 2024, 16(8), 1310; https://doi.org/10.3390/rs16081310 - 09 Apr 2024

Viewed by 400

Abstract

Ground-penetrating radar (GPR) faces complex challenges in identifying underground rock formations and lithological structures. The diversity, intricate shapes, and electromagnetic properties of subsurface rock formations make their accurate detection difficult. Additionally, the heterogeneity of subsurface media, signal scattering, and non-linear propagation effects contribute [...] Read more.

Ground-penetrating radar (GPR) faces complex challenges in identifying underground rock formations and lithological structures. The diversity, intricate shapes, and electromagnetic properties of subsurface rock formations make their accurate detection difficult. Additionally, the heterogeneity of subsurface media, signal scattering, and non-linear propagation effects contribute to the complexity of signal interpretation. To address these challenges, this study fully considers the unique advantages of convolutional neural networks (CNNs) in accurately identifying underground rock formations and lithological structures, particularly their powerful feature extraction capabilities. Deep learning models possess the ability to automatically extract complex signal features from radar data, while also demonstrating excellent generalization performance, enabling them to handle data from various geological conditions. Moreover, deep learning can efficiently process large-scale data, thereby improving the accuracy and efficiency of identification. In our research, we utilized deep neural networks to process GPR signals, using radar images as inputs and generating structure-related information associated with rock formations and lithological structures as outputs. Through training and learning, we successfully established an effective mapping relationship between radar images and lithological label signals. The results from synthetic data indicate a rock block identification success rate exceeding 88%, with a satisfactory continuity identification of lithological structures. Transferring the network to measured data, the trained model exhibits excellent performance in predicting data collected from the field, further enhancing the geological interpretation and analysis. Therefore, through the results obtained from synthetic and measured data, we can demonstrate the effectiveness and feasibility of this research method. Full article

(This article belongs to the Topic Recent Advances in Structural Health Monitoring, 2nd Volume)

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19 pages, 5688 KiB  

Open AccessArticle

Parametric Design and Shape Sensing of Geared Back Frame Shell Structure for Floating Cylindrical Reflector Antenna off the Coast

by Mengmei Mei, He Huang, Yugang Li and Zhe Zheng

Appl. Sci. 2023, 13(20), 11602; https://doi.org/10.3390/app132011602 - 23 Oct 2023

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Abstract

At present, numerous reflector antennas have been constructed worldwide on land. However, there are few applications of reflector antennas directly set off the coast. To expand the application region of reflector antennas, a floating cylindrical reflector antenna (FCRA) driven by the moving mass [...] Read more.

At present, numerous reflector antennas have been constructed worldwide on land. However, there are few applications of reflector antennas directly set off the coast. To expand the application region of reflector antennas, a floating cylindrical reflector antenna (FCRA) driven by the moving mass was developed to implement the elevation angle adjustment. Firstly, the structure design is introduced in detail. The design parameters are stated and analyzed to obtain the kinematic relationship while considering the water surface constraint. Then, the effects of each variable on the rotation capacity and structural stability are discussed. Further, the feasibility of the elevation angle adjustment process is demonstrated by using a prototype model test and software simulation. Finally, the deformation analyses and shape sensing of the back frame are carried out on the basis of the inverse finite element method (iFEM). We concluded that this new structure is feasible and expected to sit off the coast. In addition, the iFEM algorithm with sub-region reconstruction was proved to be suitable for the shape sensing of the over-constrained FCRA during the angle adjustment process via several quasi-static sampling moments. Full article

(This article belongs to the Topic Recent Advances in Structural Health Monitoring, 2nd Volume)

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