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Acoustic and Ultrasonic Sensing Technology in Non-Destructive Testing—2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".

Deadline for manuscript submissions: 31 March 2026 | Viewed by 449

Special Issue Editors


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Guest Editor
Centro de Tecnologías Físicas, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: acoustics materials; acoustic lenses; finite elements method; topology, geometric parametrical and translation optimisation; sound propagation in complex media; composite materials; US material characterisation; medical US applications; modelling ultrasonic devices; ultrasonic NDT
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Centro de Tecnologías Físicas, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: acoustics materials; acoustic lenses; finite elements method; topology, geometric parametrical and translation optimisation; sound propagation in complex media; composite materials; US material characterisation; medical US applications; modelling ultrasonic devices; ultrasonic NDT
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Centro de Tecnologías Físicas, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: acoustics materials; acoustic lenses; finite elements method; topology, geometric parametrical and translation optimisation; sound propagation in complex media; composite materials; US material characterisation; medical US applications; modelling ultrasonic devices; ultrasonic NDT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We present the launch of a Special Issue of the MDPI journal Sensors devoted to “Acoustic and Ultrasonic Sensing Technology in Non-Destructive Testing—2nd Edition”.

The applications of ultrasonic sensors are extremely varied, ranging from use in the verification of trees in poor condition, owing to their non-invasive qualities, to the production of microfluidic movement by means of slightly focused acoustic waves. They have found broad appeal across diverse disciplines and applications, ranging from use as sensors for guiding and checking for industrial and non-industrial non-destructive testing to biological, medical, and food industry applications.

This Special Issue aims to highlight recent advances in the modelling and development of ultrasound devices and materials. Topics may include, but are not limited to, the following keywords.

Dr. Sergio Castiñeira-Ibáñez
Dr. Daniel Tarrazó-Serrano
Prof. Dr. Constanza Rubio Michavila
Guest Editors

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. Sensors 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 2600 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

  • ultrasonic devices
  • HIFU transducer(s)
  • non-destructive testing material characterization
  • photonic nanojets
  • ultrasonic imaging and visualization
  • magnetic resonance imaging (MRI) compatible materials
  • medical and biomedical ultrasonic sensors
  • biological ultrasonic sensors
  • food characterization ultrasonic sensors

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Published Papers (1 paper)

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Research

16 pages, 5851 KB  
Article
Bolt Anchorage Defect Identification Based on Ultrasonic Guided Wave and Deep Learning
by Hui Xing, Weiguo Di, Xiaoyun Sun, Mingming Wang and Chaobo Li
Sensors 2025, 25(20), 6431; https://doi.org/10.3390/s25206431 - 17 Oct 2025
Viewed by 270
Abstract
As a critical supporting component in geotechnical engineering structures such as bridges, tunnels, and highways, the anchorage quality of bolts directly impacts their structural safety. The ultrasonic guided wave method is a popular method for the non-destructive testing of anchorage quality. However, noise [...] Read more.
As a critical supporting component in geotechnical engineering structures such as bridges, tunnels, and highways, the anchorage quality of bolts directly impacts their structural safety. The ultrasonic guided wave method is a popular method for the non-destructive testing of anchorage quality. However, noise from complex field environments, modal mixing caused by anchoring interface reflections, and dispersion effects make it challenging to directly extract defect features from guided wave signals in the time or frequency domains. To address these challenges, this study proposes a solution based on the combination of the guided wave time–frequency spectrum and the gated attention residual network (GA-ResNet). The GA-ResNet introduces a gating mechanism to balance spatial attention and channel attention, and it is used for anchoring model type recognition. Experiments were conducted on four types of anchorage models, and the time–frequency spectrum was selected to be the input feature. The results demonstrate that the GA-ResNet can effectively predict the anchorage bolt defect type and prevent potential safety accidents. Full article
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