Submit to Sensors Review for Sensors Propose a Special Issue

Journal Browser

► Journal Browser

Smart Sensors and Machine Learning Technique for Damage Detection and Visualization

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

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

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 8998

Share This Special Issue

Special Issue Editors

Prof. Dr. Yufeng Zhou

E-Mail Website
Guest Editor

School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
Interests: shock wave lithotripsy (SWL); high-intensity focused ultrasound (HIFU); ultrasound-enhanced drug delivery; nondestructive evaluation (NDE); surface acoustic wave (SAW)
Special Issues, Collections and Topics in MDPI journals

Dr. Wentao Wang

E-Mail Website
Guest Editor

Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI 48109-2125, USA
Interests: structural health monitoring; non-destructive testing/evaluation (NDT/E); surface wave; guided wave (GW); wireless sensor networks (WSNs); compressive sensing (CS); osseointegrated prosthesis monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, machine learning has become a field of high interest, and is frontier research direction in numerous fields. Smart sensors will undoubtedly this revolutionary change. The wide use of machine learning and computer vision provide efficient methods for data processing, damage detection, imaging, and condition assessment for the communities of civil engineering, mechanical engineering, biomedical engineering, and aerospace.

The objective of this Special Issue is to discuss the latest advances in research on machine learning and smart sensors for structural health monitoring (SHM), nondestructive evaluation/testing (NDE/NDT) and multi-discipline applications.

Topics of interest include, but are not limited to:

Machine learning-based SHM and NDE/NDT;
Computer vision-based SHM and NDE/NDT;
Damage detection and visualization;
New development of smart sensor and embedded sensing system;
Wearable or mobile sensing system;
Simulation and imaging technology of wave propagation;
Classification and prediction problem.

Prof. Dr. Yufeng Zhou
Dr. Wentao Wang
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

Machine learning
artificial intelligence
structural health monitoring
NDE/T
damage detection
smart sensors
ultrasonics
internet of things

Published Papers (4 papers)

Download All Papers

Research

15 pages, 11075 KiB

Open AccessArticle

A Synthetic Data Generation Technique for Enhancement of Prediction Accuracy of Electric Vehicles Demand

by Subhajit Chatterjee and Yung-Cheol Byun

Sensors 2023, 23(2), 594; https://0-doi-org.brum.beds.ac.uk/10.3390/s23020594 - 04 Jan 2023

Cited by 6 | Viewed by 2383

Abstract

In terms of electric vehicles (EVs), electric kickboards are crucial elements of smart transportation networks for short-distance travel that is risk-free, economical, and environmentally friendly. Forecasting the daily demand can improve the local service provider’s access to information and help them manage their short-term supply more effectively. This study developed the forecasting model using real-time data and weather information from Jeju Island, South Korea. Cluster analysis under the rental pattern of the electric kickboard is a component of the forecasting processes. We cannot achieve noticeable results at first because of the low amount of training data. We require a lot of data to produce a solid prediction result. For the sake of the subsequent experimental procedure, we created synthetic time-series data using a generative adversarial networks (GAN) approach and combined the synthetic data with the original data. The outcomes have shown how the GAN-based synthetic data generation approach has the potential to enhance prediction accuracy. We employ an ensemble model to improve prediction results that cannot be achieved using a single regressor model. It is a weighted combination of several base regression models to one meta-regressor. To anticipate the daily demand in this study, we create an ensemble model by merging three separate base machine learning algorithms, namely CatBoost, Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The effectiveness of the suggested strategies was assessed using some evaluation indicators. The forecasting outcomes demonstrate that mixing synthetic data with original data improves the robustness of daily demand forecasting and outperforms other models by generating more agreeable values for suggested assessment measures. The outcomes further show that applying ensemble techniques can reasonably increase the forecasting model’s accuracy for daily electric kickboard demand. Full article

(This article belongs to the Special Issue Smart Sensors and Machine Learning Technique for Damage Detection and Visualization)

► Show Figures

Figure 1

22 pages, 7516 KiB

Open AccessArticle

Efficacy and Damage Diagnosis of Reinforced Concrete Columns and Joints Strengthened with FRP Ropes Using Piezoelectric Transducers

by Chris G. Karayannis, Emmanouil Golias, Maria C. Naoum and Constantin E. Chalioris

Sensors 2022, 22(21), 8294; https://0-doi-org.brum.beds.ac.uk/10.3390/s22218294 - 29 Oct 2022

Cited by 28 | Viewed by 1660

Abstract

Recent research has indicated that the implantation of a network of piezoelectric transducer patches in element regions of potential damage development, such as the beam–column joint (BCJ) area, substantially increases the efficacy and accuracy of the structural health monitoring (SHM) methods to identify damage level, providing a reliable diagnosis. The use of piezoelectric lead zirconate titanate (PZT) transducers for the examination of the efficiency of an innovative strengthening technique of reinforced concrete (RC) columns and BCJs is presented and commented on. Two real-scale RC BCJ subassemblages were constructed for this investigation. The columns and the joint panel of the second subassemblage were externally strengthened with carbon fiber-reinforced polymer (C-FRP) ropes. To examine the efficiency of this strengthening technique we used the following transducers: (a) PZT sensors on the ropes and the concrete; (b) tSring linear variable displacement transducers (SLVDTs), diagonally installed on the BCJ, to measure the shear deformations of the BCJ panel; (c) Strain gauges on the internal steel bars. From the experimental results, it became apparent that the PZT transducers successfully diagnosed the loading step at which the primary damage occurred in the first BCJ subassemblage and the damage state of the strengthened BCJ during the loading procedure. Further, data acquired from the diagonal SLVDTs and the strain gauges provided insight into the damage state of the two tested specimens at each step of the loading procedure and confirmed the diagnosis provided by the PZT transducers. Furthermore, data acquired by the PZT transducers, SLVDTs and strain gauges proved the effectiveness of the applied strengthening technique with C-FRP ropes externally mounted on the column and the conjunction area of the examined BCJ subassemblages. Full article

(This article belongs to the Special Issue Smart Sensors and Machine Learning Technique for Damage Detection and Visualization)

► Show Figures

Figure 1

13 pages, 18628 KiB

Open AccessArticle

Experimental Study and FEM Simulations for Detection of Rebars in Concrete Slabs by Coplanar Capacitive Sensing Technique

by Farima Abdollahi-Mamoudan, Clemente Ibarra-Castanedo, Tobin Filleter and Xavier P. V. Maldague

Sensors 2022, 22(14), 5400; https://0-doi-org.brum.beds.ac.uk/10.3390/s22145400 - 20 Jul 2022

Cited by 4 | Viewed by 1357

Abstract

In the present study, a relatively novel non-destructive testing (NDT) method called the coplanar capacitive sensing technique was applied in order to detect different sizes of rebars in a reinforced concrete (RC) structure. This technique effectively detects changes in the dielectric properties during scanning in various sections of concrete with and without rebars. Numerical simulations were carried out by three-dimensional (3D) finite element modelling (FEM) in COMSOL Multiphysics software to analyse the impact of the presence of rebars on the electric field generated by the coplanar capacitive probe. In addition, the effect of the presence of a surface defect on the rebar embedded in the concrete slab was demonstrated by the same software for the first time. Experiments were performed on a concrete slab containing rebars, and were compared with FEM results. The results showed that there is a good qualitative agreement between the numerical simulations and experimental results. Full article

(This article belongs to the Special Issue Smart Sensors and Machine Learning Technique for Damage Detection and Visualization)

► Show Figures

Figure 1

21 pages, 4753 KiB

Open AccessArticle

Development and Validation of a Framework for Smart Wireless Strain and Acceleration Sensing

by Omobolaji Lawal, Amirali Najafi, Tu Hoang, Shaik Althaf V. Shajihan, Kirill Mechitov and Billie F. Spencer, Jr.

Sensors 2022, 22(5), 1998; https://0-doi-org.brum.beds.ac.uk/10.3390/s22051998 - 03 Mar 2022

Cited by 7 | Viewed by 2391

Abstract

Civil infrastructure worldwide is subject to factors such as aging and deterioration. Structural health monitoring (SHM) can be used to assess the impact of these processes on structural performance. SHM demands have evolved from routine monitoring to real-time and autonomous assessment. One of the frontiers in achieving effective SHM systems has been the use of wireless smart sensors (WSSs), which are attractive compared to wired sensors, due to their flexibility of use, lower costs, and ease of long-term deployment. Most WSSs use accelerometers to collect global dynamic vibration data. However, obtaining local behaviors in a structure using measurands such as strain may also be desirable. While wireless strain sensors have previously been developed by some researchers, there is still a need for a high sensitivity wireless strain sensor that fully meets the general demands for monitoring large-scale civil infrastructure. In this paper, a framework for synchronized wireless high-fidelity acceleration and strain sensing, which is commonly termed multimetric sensing in the literature, is proposed. The framework is implemented on the Xnode, a next-generation wireless smart sensor platform, and integrates with the strain sensor for strain acquisition. An application of the multimetric sensing framework is illustrated for total displacement estimation. Finally, the potential of the proposed framework integrated with vision-based measurement systems for multi-point displacement estimation with camera-motion compensation is demonstrated. The proposed approach is verified experimentally, showing the potential of the developed framework for various SHM applications. Full article

(This article belongs to the Special Issue Smart Sensors and Machine Learning Technique for Damage Detection and Visualization)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Smart Sensors and Machine Learning Technique for Damage Detection and Visualization

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (4 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI