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Smart Sensing Technology for Structural Health Monitoring

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

Deadline for manuscript submissions: 20 August 2025 | Viewed by 1211

Special Issue Editor

School of Civil and Environmental Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia
Interests: structural health monitoring; structural dynamics; structural engineering; advanced signal processing and sensor technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Large-scale civil infrastructures such as bridges, buildings, dams, wind turbines and pipeline systems face potential damage related to ageing and various operational and environmental loads. Structural health monitoring is critical to prevent catastrophic structural collapse and provide quantitative data for effective and affordable lifecycle structural management and maintenance. Enormous research efforts and resources have been devoted to developing reliable structural health monitoring technologies. The current two main areas are methods for effective data collection and the reliable evaluation of structural conditions from measured data. The field implementation of traditional contact-based wired sensors poses various economic and technical challenges. With the advance of the wireless sensing technology, there has been a paradigm shift toward IoT sensor technology. With the recent development of sensing and robotic technologies, smart sensing technology has attracted interest of a great many researchers and engineers, especially in computer vision techniques, IoT sensing technology, mobile sensory system, and embedded sensing technology. This Special Issue will capture the latest research and development in all areas of smart sensing technology for structural health monitoring and its practical applications. 

Potential topics include, but are not limited to, the following:

  • Computer vision techniques;
  • IoT sensors;
  • Mobile sensory systems;
  • Robotic sensory systems;
  • Embedded sensing technology;
  • Intelligent sensing technology;
  • Optimization of sensor placement;
  • Sensor fault detection;
  • Advanced measurement technology.

Dr. Xinqun Zhu
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. 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

  • smart sensing technology
  • structural health monitoring
  • computer vision
  • IoT sensors
  • sensor fault detection

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Published Papers (1 paper)

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Research

20 pages, 3347 KiB  
Article
Tiny Machine Learning Implementation for Guided Wave-Based Damage Localization
by Jannik Henkmann, Vittorio Memmolo and Jochen Moll
Sensors 2025, 25(2), 578; https://doi.org/10.3390/s25020578 - 20 Jan 2025
Viewed by 812
Abstract
This work leverages ultrasonic guided waves (UGWs) to detect and localize damage in structures using lightweight Artificial Intelligence (AI) models. It investigates the use of machine learning (ML) to train the effects of the damage on UGWs to the model. To reduce the [...] Read more.
This work leverages ultrasonic guided waves (UGWs) to detect and localize damage in structures using lightweight Artificial Intelligence (AI) models. It investigates the use of machine learning (ML) to train the effects of the damage on UGWs to the model. To reduce the number of trainable parameters, a physical signal processing approach is applied to the raw data before passing the data to the model. Starting from current state of the art in algorithms used for damage detection and localization, an AI-based technique is developed and validated on an experimental benchmark dataset before tiny ML implementation on a low-cost development board. A discussion of the need for a balance between the reduction in computational resources and increasing the precision of the models is also reported. It is shown that by extracting simple features of the signal, the models required to predict the damage locations can be significantly reduced in size while still having high accuracies of over 90%. In addition, it is possible to use these predictions to construct a fairly accurate heat map indicating the likely damage locations. Finally, a convenient edge/cloud visualization of the results can be achieved by simplifying the heat map. Full article
(This article belongs to the Special Issue Smart Sensing Technology for Structural Health Monitoring)
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