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Smart Sensors in Structural Health Monitoring
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Smart Sensors in Structural Health Monitoring

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

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 15369

Special Issue Editors

Prof. Dr. Hoon Sohn

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
Interests: structural health monitoring; nondestructive testing; smart sensing; data analytics; additive manufacturing; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Heungwoo Nam

E-Mail
Guest Editor
Assistant Professor, Department of Computer Engineering, Daegu University, 201 Daegudae-ro, Gyeongsangbuk-do, Daegu 38453, Korea
Interests: structural health monitoring; machine learning; communication networks and protocols; acoustics; autonomous underwater vehicles

Special Issue Information

Dear Colleagues,

The field of structural health monitoring (SHM) has become of great importance to investigate and maintain ocean structures (e.g., off-shore platforms, undersea tunnels, and submerged floating tunnels) as well as land structures (e.g., buildings and bridges). Sensors are crucial elements for providing reliable information on the health condition and performance of the structures as the ‘ears’ and ‘eyes’ of SHM. In particular, through sensors combined with sensor networks in underwater and terrestrial environments, the continuous monitoring of the structures has been performed, ensuring their safety. Moreover, by means of Internet of Things (IoT) technology, sensors and processors applied with the structures can communicate with each other, collaborating with unmanned underwater vehicles (UUVs) and unmanned aerial vehicles (UAVs), which lead to imagine them being ‘smart’. As a result, in order to assist the decision-making process regarding health monitoring, SHM with smart sensors will be increasingly used in real-life settings. SHM involves sensing and data analysis by means of AI and Big Data, and thus, its effectiveness depends on the technological advances in these fields.

The aim of this Special Issue is to bring together innovative developments in areas related to smart sensors in structural health monitoring, including but not limited to the following:

  • Novel sensors and sensing technologies;
  • Intelligent signal processing;
  • Smart operation strategies for sensor networks;
  • Innovative underwater and terrestrial sensor networks;
  • Adaptive monitoring;
  • Communication (security, resilience, low power, and low cost);
  • Data processing (distributed, aggregation, and big data);
  • Deployment (low cost, error prevention, and localization);
  • Remote monitoring in ocean and land structures;  
  • Collaboration with UUVs and UAVs.

Both review articles and original research papers covering smart sensors in structural health monitoring are solicited. In particular, papers with advances towards practical experiences and services are of interest.

Prof. Dr. Hoon Sohn
Dr. Heungwoo Nam
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

  • Structural health monitoring
  • Smart sensors
  • Smart sensing
  • Smart collaboration
  • Remote monitoring
  • Ocean and land structures
  • IoT
  • UUVs
  • UAVs

Published Papers (5 papers)

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Research

20 pages, 1276 KiB  
Article
Determination of Lamb Wave Modes on Lithium-Ion Batteries Using Piezoelectric Transducers
by Markus Koller, Gregor Glanz, Alexander Bergmann and Hartmut Popp
Sensors 2022, 22(13), 4748; https://doi.org/10.3390/s22134748 - 23 Jun 2022
Cited by 7 | Viewed by 3178
Abstract
This work presents a method to determine the type of Lamb mode (antisymmetric or symmetric) that propagates through a lithium-ion pouch cell. To determine the type of mode and the group velocity at a specific frequency, two- and three-transducer setups were created. For [...] Read more.
This work presents a method to determine the type of Lamb mode (antisymmetric or symmetric) that propagates through a lithium-ion pouch cell. To determine the type of mode and the group velocity at a specific frequency, two- and three-transducer setups were created. For these setups, it is important that all transducers have the same polarization direction. Two transducers are affixed to the center of the cell at a distance of several centimeters from each other so that the group velocity can be determined. Using cross-correlation, the group velocity of the emerging mode can be calculated. The measurement setup and the processing method was first validated with experiments on acrylic glass and aluminum plates. The measurements were supported with FEM simulations and a numerically calculated model. The output voltages of the receiving piezo-elements obtained in the FEM simulation are in agreement with the underlying theories. The phase shift, which results from the output voltage of the piezo-elements mounted one above the other on different sides of the plate, shows the type of mode. The results of the experimental determination of the Lamb mode that propagates through a lithium-ion pouch cell were validated with a numerically calculated multi-layer model and therefore validate this novel experimental approach. Full article
(This article belongs to the Special Issue Smart Sensors in Structural Health Monitoring)
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14 pages, 3279 KiB  
Article
A Deep-Learning-Based Health Indicator Constructor Using Kullback–Leibler Divergence for Predicting the Remaining Useful Life of Concrete Structures
by Tuan-Khai Nguyen, Zahoor Ahmad and Jong-Myon Kim
Sensors 2022, 22(10), 3687; https://doi.org/10.3390/s22103687 - 12 May 2022
Cited by 10 | Viewed by 1753
Abstract
This paper proposes a new technique for the construction of a concrete-beam health indicator based on the Kullback–Leibler divergence (KLD) and deep learning. Health indicator (HI) construction is a vital part of remaining useful lifetime (RUL) approaches for monitoring the health of concrete [...] Read more.
This paper proposes a new technique for the construction of a concrete-beam health indicator based on the Kullback–Leibler divergence (KLD) and deep learning. Health indicator (HI) construction is a vital part of remaining useful lifetime (RUL) approaches for monitoring the health of concrete structures. Through the construction of a HI, the deterioration process can be processed and portrayed so that it can be forwarded to a prediction module for RUL prognosis. The degradation progression and failure can be identified by predicting the RUL based on the situation of the current specimen; as a result, maintenance can be planned to reduce safety risks, reduce financial costs, and prolong the specimen’s useful lifetime. The portrayal of deterioration through HI construction from raw acoustic emission (AE) data is performed using a deep neural network (DNN), whose parameters are obtained by pretraining and fine tuning using a stack autoencoder (SAE). Kullback–Leibler divergence, which is calculated between a reference normal-conditioned signal and a current unknown signal, was used to represent the deterioration process of concrete structures, which has not been investigated for the concrete beams so far. The DNN-based constructor then learns to generate HI from raw data with KLD values as the training label. The HI construction result was evaluated with run-to-fail test data of concrete specimens with two measurements: fitness analysis of the construction result and RUL prognosis. The results confirm the reliability of KLD in portraying the deterioration process, showing a large improvement in comparison to other methods. In addition, this method requires no adept knowledge of the nature of the AE or the system fault, which is more favorable than model-based approaches where this level of expertise is compulsory. Furthermore, AE offers in-service monitoring, allowing the RUL prognosis task to be performed without disrupting the specimen’s work. Full article
(This article belongs to the Special Issue Smart Sensors in Structural Health Monitoring)
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21 pages, 33833 KiB  
Article
Bridge Damage Detection Approach Using a Roving Camera Technique
by Darragh Lydon, Myra Lydon, Rolands Kromanis, Chuan-Zhi Dong, Necati Catbas and Su Taylor
Sensors 2021, 21(4), 1246; https://doi.org/10.3390/s21041246 - 10 Feb 2021
Cited by 20 | Viewed by 3598
Abstract
Increasing extreme climate events, intensifying traffic patterns and long-term underinvestment have led to the escalated deterioration of bridges within our road and rail transport networks. Structural Health Monitoring (SHM) systems provide a means of objectively capturing and quantifying deterioration under operational conditions. Computer [...] Read more.
Increasing extreme climate events, intensifying traffic patterns and long-term underinvestment have led to the escalated deterioration of bridges within our road and rail transport networks. Structural Health Monitoring (SHM) systems provide a means of objectively capturing and quantifying deterioration under operational conditions. Computer vision technology has gained considerable attention in the field of SHM due to its ability to obtain displacement data using non-contact methods at long distances. Additionally, it provides a low cost, rapid instrumentation solution with low interference to the normal operation of structures. However, even in the case of a medium span bridge, the need for many cameras to capture the global response can be cost-prohibitive. This research proposes a roving camera technique to capture a complete derivation of the response of a laboratory model bridge under live loading, in order to identify bridge damage. Displacement is identified as a suitable damage indicator, and two methods are used to assess the magnitude of the change in global displacement under changing boundary conditions in the laboratory bridge model. From this study, it is established that either approach could detect damage in the simulation model, providing an SHM solution that negates the requirement for complex sensor installations. Full article
(This article belongs to the Special Issue Smart Sensors in Structural Health Monitoring)
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17 pages, 6725 KiB  
Article
An Experimental and Numerical Study on the Use of Chirped FBG Sensors for Monitoring Fatigue Damage in Hybrid Composite Patch Repairs
by Rodolfo L. Rito, Stephen L. Ogin and Andrew D. Crocombe
Sensors 2021, 21(4), 1168; https://doi.org/10.3390/s21041168 - 7 Feb 2021
Cited by 3 | Viewed by 2479
Abstract
In this paper, chirped fibre Bragg grating (CFBG) sensors used to monitor the structural health of a composite patch used to repair an aluminium panel is presented. To introduce damage, a notch was produced at the centre of an aluminium panel. The repair [...] Read more.
In this paper, chirped fibre Bragg grating (CFBG) sensors used to monitor the structural health of a composite patch used to repair an aluminium panel is presented. To introduce damage, a notch was produced at the centre of an aluminium panel. The repair consisted of bonding a pre-cured composite patch to the host panel using an aerospace-grade film adhesive; the sensor was embedded in the bond-line during fabrication of the repair. The repaired panels were subjected to tension-tension loading in fatigue. Cracks initiated and grew from both ends of the notch in the aluminium panels and the fatigue loading was stopped periodically for short periods of time to record the reflected spectra from the sensor. It was found that perturbations in the reflected spectra began to occur when the crack was within about 2 to 3 mm of the sensor location; after the crack passed the sensor location, the perturbations essentially stabilised. Predicted reflected spectra have been found to be in good agreement with the experiment, confirming that CFBG sensors can detect crack growth in patch-repaired panels. Full article
(This article belongs to the Special Issue Smart Sensors in Structural Health Monitoring)
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17 pages, 7441 KiB  
Article
New Instrumented Trolleys and A Procedure for Automatic 3D Optical Inspection of Railways
by Maria Cristina Valigi, Silvia Logozzo, Enrico Meli and Andrea Rindi
Sensors 2020, 20(10), 2927; https://doi.org/10.3390/s20102927 - 21 May 2020
Cited by 12 | Viewed by 3535
Abstract
This paper focuses on new instrumented trolleys, allowing automated 3D inspection of railway infrastructures, using optical scanning principles and devices for defects and damage evaluation. Inspection of rolling components is crucial for wear evaluation and to schedule maintenance interventions to assure safety. Currently, [...] Read more.
This paper focuses on new instrumented trolleys, allowing automated 3D inspection of railway infrastructures, using optical scanning principles and devices for defects and damage evaluation. Inspection of rolling components is crucial for wear evaluation and to schedule maintenance interventions to assure safety. Currently, inspection trolleys are mainly instrumented with 2D contact or optical sensors. The application of 3D non-contact digitizers proposed in this paper allows for a quick and more complete monitoring of the health conditions of railways, also in combination with a proper procedure for automatic 3D inspection. The results of the experimental tests using 3D portable optical scanners on railways are compared with results obtained by a trolley instrumented with 2D contact sensors. The results demonstrate the effectiveness of the trolleys mounting 3D handheld optical digitizers with proper automated software inspection procedures. Full article
(This article belongs to the Special Issue Smart Sensors in Structural Health Monitoring)
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