Topic Editors

Department of Structural, Geotechnical and Building Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy
Department of Structural, Geotechnical and Building Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy
Dr. Gianfranco Piana
Department of Structural, Geotechnical and Building Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy
Department of Chemistry and Physics, Southeastern Louisiana University, SLU 10878, Hammond, LA 70402, USA
Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China
School of Civil Engineering, Research Center of Large-Span Spatial Structures, Tianjin University, Tianjin 300350, China

Recent Advances in Structural Health Monitoring, 2nd Volume

Abstract submission deadline
30 September 2024
Manuscript submission deadline
31 December 2024
Viewed by
2535

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

  • structural health monitoring
  • damage evaluation
  • cracking evolution
  • acoustic emission
  • sensors
  • structural stability
  • vibrations
  • dynamic control
  • optical fibre
  • digital image correlation
  • sonic–ultrasonic test
  • tomography techniques
  • impact test
  • radar test
  • electromagnetic analysis

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

Preprints.org is a multidiscipline platform providing preprint service that is dedicated to sharing your research from the start and empowering your research journey.

MDPI Topics is cooperating with Preprints.org and has built a direct connection between MDPI journals and Preprints.org. Authors are encouraged to enjoy the benefits by posting a preprint at Preprints.org prior to publication:

  1. Immediately share your ideas ahead of publication and establish your research priority;
  2. Protect your idea from being stolen with this time-stamped preprint article;
  3. Enhance the exposure and impact of your research;
  4. Receive feedback from your peers in advance;
  5. Have it indexed in Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (3 papers)

Order results
Result details
Journals
Select all
Export citation of selected articles as:
19 pages, 25026 KiB  
Article
Effect of Mineral Composition and Particle Size on the Failure Characteristics and Mechanisms of Marble in the China Jinping Underground Laboratory
by Hong Xu, Peiqi Jing, Guangliang Feng, Zhen Zhang, Quan Jiang and Jie Yan
Materials 2024, 17(10), 2290; https://doi.org/10.3390/ma17102290 - 12 May 2024
Viewed by 419
Abstract
In deep underground engineering, the deformation, failure characteristics, and mechanism of surrounding rock under the influence of grain sizes and mineral compositions are not clear. Based on CJPL-II variously colored marbles, the differences in grain size and mineral composition of the marble were [...] Read more.
In deep underground engineering, the deformation, failure characteristics, and mechanism of surrounding rock under the influence of grain sizes and mineral compositions are not clear. Based on CJPL-II variously colored marbles, the differences in grain size and mineral composition of the marble were analyzed by thin-section analysis and XRD tests, and the effect of intermediate principal stress on the mechanical properties of marble was investigated. Both SEM and microfracture analysis were coupled to reveal the failure mechanisms. The results highlight that the crack initiation strength, damage strength, peak strength, and elasticity modulus of Jinping marble exhibit an increasing trend with an increase in intermediate principal stress, while the peak strain initially increases and subsequently decreases. Moreover, this study established negative correlations between marble strength, brittleness characteristics, and fracture angle with grain size, whereas positive correlations were identified with the content of quartz, sodium feldspar, and the magnitude of the intermediate principal stress. The microcrack density in marble was found to increase with larger grain sizes and decrease with elevated quartz and sodium feldspar content, as well as with increasing intermediate principal stress. Notably, as the intermediate principal stress intensifies and grain size diminishes, the transgranular tensile failure of marble becomes more conspicuous. These research findings contribute to the effective implementation of disaster prevention and control strategies. Full article
Show Figures

Figure 1

20 pages, 8234 KiB  
Article
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 - 9 Apr 2024
Viewed by 589
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
Show Figures

Figure 1

19 pages, 5688 KiB  
Article
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
Viewed by 719
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
Show Figures

Figure 1

Back to TopTop