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Applied Sciences
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Advanced Diagnostics and Nondestructive Testing Technologies for Civil Structures
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Advanced Diagnostics and Nondestructive Testing Technologies for Civil Structures

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: 20 June 2024 | Viewed by 1613

Special Issue Editor

Prof. Dr. Adriana Brancaccio

E-Mail Website
Guest Editor
Department of Engineering, University of Campania, 81031 Aversa, Italy
Interests: linear and non linear inverse scattering; ground penetrating radar; microwave measurements; microwave tomography; singular values decomposition; detection and localization of defects
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to highlight the latest advancements in civil structure-related nondestructive testing (NDT) and diagnostics. It will showcase innovative technologies and applications that facilitate the accurate and efficient monitoring, evaluation, and condition assessment of civil infrastructure systems.

This Special Issue focuses on NDT methods, advanced diagnostics techniques, and their applications in civil structures, with its scope including, but not limited to, the following topics:

  • Nondestructive testing methods for assessing the condition of concrete, steel, and wooden structures;
  • Innovative signal processing techniques for enhancing the reliability and accuracy of NDT data;
  • Integration of NDT data with numerical models for structural health monitoring and prognosis;
  • Case studies demonstrating the practical application of NDT techniques in civil structures;
  • Development of automated and semi-automated NDT systems for efficient data collection and analysis;
  • Use of NDT data for decision-making in maintenance, repair, and replacement of civil infrastructure elements;
  • Challenges and future trends in NDT for civil structures.

Prof. Dr. Adriana Brancaccio
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • non-destructive testing
  • material characterization
  • civil structure and infrastructure
  • experimental techniques
  • mechanics of materials

Published Papers (3 papers)

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Research

26 pages, 21449 KiB  
Article
Automated Multi-Type Pavement Distress Segmentation and Quantification Using Transformer Networks for Pavement Condition Index Prediction
by Zaiyan Zhang, Weidong Song, Yangyang Zhuang, Bing Zhang and Jiachen Wu
Appl. Sci. 2024, 14(11), 4709; https://doi.org/10.3390/app14114709 - 30 May 2024
Viewed by 136
Abstract
Pavement distress detection is a crucial task when assessing pavement performance conditions. Here, a novel deep-learning method based on a transformer network, referred to as ISTD-DisNet, is proposed for multi-type pavement distress semantic segmentation. In this methodology, a mix transformer (MiT) based on [...] Read more.
Pavement distress detection is a crucial task when assessing pavement performance conditions. Here, a novel deep-learning method based on a transformer network, referred to as ISTD-DisNet, is proposed for multi-type pavement distress semantic segmentation. In this methodology, a mix transformer (MiT) based on a hierarchical transformer structure is chosen as the backbone to obtain multi-scale feature information on pavement distress, and a mixed attention module (MAM) is introduced at the decoding stage to capture the pavement distress features across different channels and spatial locations. A learnable transposed convolution upsampling module (TCUM) enhances the model’s ability to restore multi-scale distress details. Subsequently, a novel parameter—the distress pixel density ratio (PDR)—is introduced based on the segmentation results. Analyzing the intrinsic correlation between the PDR and the pavement condition index (PCI), a new pavement damage index prediction model is proposed. Finally, the experimental results reveal that the F1 and mIOU of the proposed method are 95.51% and 91.67%, respectively, and the segmentation performance is better than that of the other seven mainstream segmentation models. Further PCI prediction model validation experimental results also indicate that utilizing the PDR enables the quantitative evaluation of the pavement damage conditions for each assessment unit, holding promising engineering application potential. Full article
(This article belongs to the Special Issue Advanced Diagnostics and Nondestructive Testing Technologies for Civil Structures)
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20 pages, 7737 KiB  
Article
Investigation of Carbon Fiber Reinforced Polymer Concrete Reinforcement Ageing Using Microwave Infrared Thermography Method
by Barbara Szymanik, Sam Ang Keo, Franck Brachelet and Didier Defer
Appl. Sci. 2024, 14(10), 4331; https://doi.org/10.3390/app14104331 - 20 May 2024
Viewed by 516
Abstract
This study presents the utilization of the microwave infrared thermography (MIRT) technique to identify and analyze the defects in the carbon-fiber-reinforced polymer (CFRP) composite reinforcement of concrete specimens. At first, a set of numerical models was created, comprising the broadband pyramidal horn antenna [...] Read more.
This study presents the utilization of the microwave infrared thermography (MIRT) technique to identify and analyze the defects in the carbon-fiber-reinforced polymer (CFRP) composite reinforcement of concrete specimens. At first, a set of numerical models was created, comprising the broadband pyramidal horn antenna and the analyzed specimen. The utilization of the system operating at a power of 1000 W in a continuous mode, operating at frequency of 2.45 GHz, was analyzed. The specimen under examination comprised a compact concrete slab that was covered with an adhesive layer and, thereafter, topped with a layer of CFRP. An air gap represented a defect at the interface between the concrete and the CFRP within the adhesive layer. In the modeling stage, the study investigated three separate scenarios—a sample with no defects, a sample with a defect located at the center, and a sample with a numerous additional random defects located at the rim of the CFRP matte—to analyze the effect of the natural reinforcement degradation in this area. The next phase of the study involved conducting experiments to confirm the results obtained from numerical modeling. In the experiments, the concrete sample aged for 10 years with the defect in the center and naturally developed defects at the CFRP rim was used. The study employed numerical modeling to explore the phenomenon of microwave heating in complex structures. The aim was to assess the chosen antenna design and identify the most effective experimental setup. These conclusions were subsequently confirmed through experimentation. The observations made during the heating process were particularly remarkable since they deviated from earlier studies that solely conducted measurements of the sample post-heating phase. The findings demonstrate that MIRT has the capacity to be employed as a technique for detecting flaws in concrete structures reinforced with CFRP. Full article
(This article belongs to the Special Issue Advanced Diagnostics and Nondestructive Testing Technologies for Civil Structures)
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14 pages, 4796 KiB  
Article
A PZT-Based Smart Anchor Washer for Monitoring Prestressing Force Based on the Wavelet Packet Analysis Method
by Long Wang, Liuyu Zhang, Di Mo and Xiaoguang Wu
Appl. Sci. 2024, 14(2), 641; https://doi.org/10.3390/app14020641 - 12 Jan 2024
Viewed by 712
Abstract
Prestressed steel strands in prestressed structures offset or reduce the tensile stress caused by external loads, making them the primary load-bearing components. Great concerns have been raised about prestress monitoring due to the growing use of structural health monitoring (SHM). Piezoceramic (PZT) active [...] Read more.
Prestressed steel strands in prestressed structures offset or reduce the tensile stress caused by external loads, making them the primary load-bearing components. Great concerns have been raised about prestress monitoring due to the growing use of structural health monitoring (SHM). Piezoceramic (PZT) active sensing methods are commonly used in this field. However, there appears to be a problem of “energy saturation” in the utilization of piezoceramic active sensing methods. In this study, a smart anchor washer with semi-cylinders was developed to alleviate the saturation problem. An intelligent monitoring system is formed by combining the upper and lower annular cylinders with two piezoelectric patches. The piezoelectric patch on the upper annular cylinder is used as an actuator to emit signals through the contact interface of the smart anchor washer, which are then received by the piezoelectric patch on the lower annular cylinder. Based on wavelet packet decomposition, we investigate the correlation between the energy of the received signal and the applied tension force. Finally, a prestressing force index is developed for monitoring prestressing force using Shannon entropy. It is found that the index decreases with the increase in tension. The proposed design and index are also sensitive to early monitoring of prestressing force and can be used to monitor the entire prestressing process of steel strands. Full article
(This article belongs to the Special Issue Advanced Diagnostics and Nondestructive Testing Technologies for Civil Structures)
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