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Sensor and Sensing Technologies for Structural Health Monitoring and Non-Destructive Testing—2nd Edition

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 3097

Special Issue Editor

Prof. Dr. Victor Giurgiutiu

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of South Carolina, Columbia, SC 29208, USA
Interests: structural health monitoring (SHM); nondestructive evaluation (NDE)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With recent advancements in sensor technology, structural health monitoring (SHM) and nondestructive evaluation (NDE) systems have been developed and implemented in various civil structures, such as bridges, buildings, tunnels, power plants, and dams. Many advanced types of sensors, from wired to wireless, have been developed to continuously monitor structural conditions through real-time data collection.

With the remarkable progress that has been made following SHM and NDE developments, considerable work remains to be carried out, such as refining theoretical analysis and calibration against well-planned experiments, developing novel sensors for industrial and commercial applications, addressing operational and environmental variations to deploy SHM and NDE techniques for in-service structures, developing signal analysis methods to achieve a probability of detection, and developing novel imaging algorithms to quantify the damage detected. Papers on topics include, but are not limited to, one or several of the following aspects, which will be considered for publication:

  • guided wave actuation and detection;
  • novel signal processing methods;
  • imaging algorithms for damage quantification;
  • damage detection in composite structures;
  • fiber optic sensors;
  • the use of an accelerometer for structural health monitoring;
  • vibrating wire sensors;
  • strain gauges in structural health monitoring;
  • acoustic emission sensors;
  • linear variable differential transformers (LVDTs);
  • temperature sensors.

Prof. Dr. Victor Giurgiutiu
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

  • structural health monitoring
  • damage detection
  • guided wave
  • nondestructive evaluation

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Related Special Issue

Published Papers (4 papers)

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Research

19 pages, 6428 KiB  
Article
New Method of Impact Localization on Plate-like Structures Using Deep Learning and Wavelet Transform
by Asaad Migot, Ahmed Saaudi and Victor Giurgiutiu
Sensors 2025, 25(6), 1926; https://doi.org/10.3390/s25061926 - 20 Mar 2025
Viewed by 224
Abstract
This paper presents a new methodology for localizing impact events on plate-like structures using a proposed two-dimensional convolutional neural network (CNN) and received impact signals. A network of four piezoelectric wafer active sensors (PWAS) was installed on the tested plate to acquire impact [...] Read more.
This paper presents a new methodology for localizing impact events on plate-like structures using a proposed two-dimensional convolutional neural network (CNN) and received impact signals. A network of four piezoelectric wafer active sensors (PWAS) was installed on the tested plate to acquire impact signals. These signals consisted of reflection waves that provided valuable information about impact events. In this methodology, each of the received signals was divided into several equal segments. Then, a wavelet transform (WT)-based time-frequency analysis was used for processing each segment signal. The generated WT diagrams of these segments’ signals were cropped and resized using MATLAB code to be used as input image datasets to train, validate, and test the proposed CNN model. Two scenarios were adopted from PAWS transducers. First, two sensors were positioned in two corners of the plate, while, in the second scenario, four sensors were used to monitor and collect the signals. Eight datasets were collected and reshaped from these two scenarios. These datasets presented the signals of two, three, four, and five impacts. The model’s performance was evaluated using four metrics: confusion matrix, accuracy, precision, and F1 score. The proposed model demonstrated exceptional performance by accurately localizing all of the impact points of the first scenario and 99% of the second scenario. The main limitation of the proposed model is how to differentiate the data samples that have similar features. From our point of view, the similarity challenge arose from two factors: the segmentation interval and the impact distance. First, applying the segmenting procedure to the PWAS signals led to an increase in the number of data samples. The procedure segmented each PWAS signal to 30 samples with equal intervals, regardless of the features of the signal. Segmenting and transforming different PWAS signals into image-based data points led to data samples that had similar features. Second, some of the impacts had a close distance to the PWAS sensors, which resulted in similar segmented signals. Therefore, the second scenario was more challenging for the proposed model. Full article
(This article belongs to the Special Issue Sensor and Sensing Technologies for Structural Health Monitoring and Non-Destructive Testing—2nd Edition)
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14 pages, 3881 KiB  
Article
Tension Estimation in Anchor Rods Using Multimodal Ultrasonic Guided Waves
by Thilakson Raveendran and Frédéric Taillade
Sensors 2025, 25(6), 1665; https://doi.org/10.3390/s25061665 - 7 Mar 2025
Viewed by 360
Abstract
The diagnosis of post-stressed anchor rods is essential for maintaining the service and ensuring the safety of Electricité de France (EDF) structures. These rods are critical for the mechanical strength of structures and electromechanical components. Currently, the standard method for estimating the effective [...] Read more.
The diagnosis of post-stressed anchor rods is essential for maintaining the service and ensuring the safety of Electricité de France (EDF) structures. These rods are critical for the mechanical strength of structures and electromechanical components. Currently, the standard method for estimating the effective tension of post-stressed tie rods with a free length involves measuring the residual force using a hydraulic jack. However, this method can be costly, impact the structure’s operation, and pose risks to employees. Until now, there has been no reliable on-field approach to estimating residual tension using a lightweight setup. This research introduces a nondestructive method using multimodal ultrasonic guided waves to evaluate the residual tension of anchor rods with a few centimeters free at one end. The methodology was developed through both laboratory experiments and simulations. This new method allows for the extraction of dispersion curves for the first three modes, bending, torsional, and longitudinal, using time–frequency analysis and enables the estimation of the steel bar’s properties. Future work will focus on applying this methodology in the field. Full article
(This article belongs to the Special Issue Sensor and Sensing Technologies for Structural Health Monitoring and Non-Destructive Testing—2nd Edition)
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12 pages, 7388 KiB  
Article
Piezoresistive, Piezocapacitive and Memcapacitive Silk Fibroin-Based Cement Mortars
by Daniel A. Triana-Camacho, Antonella D’Alessandro, Silvia Bittolo Bon, Rocco Malaspina, Filippo Ubertini and Luca Valentini
Sensors 2024, 24(22), 7357; https://doi.org/10.3390/s24227357 - 18 Nov 2024
Cited by 1 | Viewed by 866
Abstract
Water-stable proteins may offer a new field of applications in smart materials for buildings and infrastructures where hydraulic reactions are involved. In this study, cement mortars modified through water-soluble silk fibroin (SF) are proposed. Water-soluble SF obtained by redissolving SF films in phosphate [...] Read more.
Water-stable proteins may offer a new field of applications in smart materials for buildings and infrastructures where hydraulic reactions are involved. In this study, cement mortars modified through water-soluble silk fibroin (SF) are proposed. Water-soluble SF obtained by redissolving SF films in phosphate buffer solution (PBS) showed the formation of a gel with the β sheet features of silk II. Electrical measurements of SF indicate that calcium ions are primarily involved in the conductivity mechanism. By exploiting the water solubility properties of silk II and Ca2+ ion transport phenomena as well as their trapping effect on water molecules, SF provides piezoresistive and piezocapacitive properties to cement mortars, thus enabling self-sensing of mechanical strain, which is quite attractive in structural health monitoring applications. The SF/cement-based composite introduces a capacitive gauge factor which surpasses the traditional resistive gauge factor reported in the literature by threefold. Cyclic voltammetry measurements demonstrated that the SF/cement mortars possessed memcapacitive behavior for positive potentials near +5 V, which was attributed to an interfacial charge build-up modulated by the SF concentration and the working electrode. Electrical square-biphasic excitation combined with cyclic compressive loads revealed memristive behavior during the unloading stages. These findings, along with the availability and sustainability of SF, pave the way for the design of novel multifunctional materials, particularly for applications in masonry and concrete structures. Full article
(This article belongs to the Special Issue Sensor and Sensing Technologies for Structural Health Monitoring and Non-Destructive Testing—2nd Edition)
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17 pages, 5246 KiB  
Article
A Comparative Study of Geometric Phase Change- and Sideband Peak Count-Based Techniques for Monitoring Damage Growth and Material Nonlinearity
by Guangdong Zhang, Tribikram Kundu, Pierre A. Deymier and Keith Runge
Sensors 2024, 24(20), 6552; https://doi.org/10.3390/s24206552 - 11 Oct 2024
Cited by 2 | Viewed by 1009
Abstract
This work presents numerical modeling-based investigations for detecting and monitoring damage growth and material nonlinearity in plate structures using topological acoustic (TA) and sideband peak count (SPC)-based sensing techniques. The nonlinear ultrasonic SPC-based technique (SPC-index or SPC-I) has shown its effectiveness in monitoring [...] Read more.
This work presents numerical modeling-based investigations for detecting and monitoring damage growth and material nonlinearity in plate structures using topological acoustic (TA) and sideband peak count (SPC)-based sensing techniques. The nonlinear ultrasonic SPC-based technique (SPC-index or SPC-I) has shown its effectiveness in monitoring damage growth affecting various engineering materials. However, the new acoustic parameter, “geometric phase change (GPC)” and GPC-index (or GPC-I), derived from the TA sensing technique adopted for monitoring damage growth or material nonlinearity has not been reported yet. The damage growth modeling is carried out by the peri-ultrasound technique to simulate nonlinear interactions between elastic waves and damages (cracks). For damage growth with a purely linear response and for the nonlinearity arising from only the nonlinear stress–strain relationship of the material, the numerical analysis is conducted by the finite element method (FEM) in the Abaqus/CAE 2021 software. In both numerical modeling scenarios, the SPC- and GPC-based techniques are adopted to capture and compare those responses. The computed results show that, from a purely linear scattering response in FEM modeling, the GPC-I can effectively detect the existence of damage but cannot monitor damage growth since the linear scattering differences are small when crack thickness increases. The SPC-I does not show any change when a nonlinear response is not generated. However, the nonlinear response from the damage growth can be efficiently modeled by the nonlocal peri-ultrasound technique. Both the GPC-I and SPC-I techniques can clearly show the damage evolution process if the frequencies are properly chosen. This investigation also shows that the GPC-I indicator has the capability to distinguish nonlinear materials from linear materials while the SPC-I is found to be more effective in distinguishing between different types of nonlinear materials. This work can reveal the mechanism of GPC-I for capturing linear and nonlinear responses, and thus can provide guidance in structural health monitoring (SHM). Full article
(This article belongs to the Special Issue Sensor and Sensing Technologies for Structural Health Monitoring and Non-Destructive Testing—2nd Edition)
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