Submit to Sensors Review for Sensors Propose a Special Issue

Journal Browser

► Journal Browser

Sensors Applications in Structural Health Monitoring: Extended Papers from the 10th European Workshop on Structural Health Monitoring (EWSHM)

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 "Sensor Networks".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 10945

Share This Special Issue

Special Issue Editors

Dr. Giuseppe Pitarresi

E-Mail Website
Guest Editor

Department of Industrial and Digital Innovation (DIID), University of Palermo, Piazza Marina, 61, Palermo, PA 90133, Italy
Interests: mechanics of materials; experimental mechanics; composite materials
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Piervincenzo Rizzo

E-Mail Website
Guest Editor

Department of Civil & Environmental Engineering, SWANSON School of Engineering, University of Pittsburgh, Benedum Hall, Pittsburgh, PA 15260, USA
Interests: structural health monitoring; nondestructive evaluation; material characterization

Prof. Dr. Alberto Milazzo

E-Mail Website
Guest Editor

Department of Engineering, University of Palermo, Viale delle Scienze, Bldg. 8, 90128 Palermo, Italy
Interests: structural health monitoring; composite and smart structures; fracture mechanics

Special Issue Information

Dear Colleagues,

This volume will contain the versions of papers originally submitted to the 10th European Workshop on Structural Health Monitoring (EWSHM 2022) held in Palermo (Italy) on July 4-7, 2022. Papers related to novel “smart” sensors, sensors for extreme environments, MEMS/NEMS sensors, fiber optics, piezoelectric, magneto-electric sensors, CNT sensors, etc. are welcome. The papers represent an extension of the innovative works presented in Palermo. Eligible authors are those who have submitted a paper or who have presented a paper at that event, which represents a forum where experts from around the world discuss the latest advancements and breakthroughs in the field of structural health monitoring and more broadly in the fields of smart materials, intelligent systems, and nondestructive evaluation. This 2022 event also intended to promote the exchange of ideas and the cross-fertilization among multiple engineering disciplines and beyond. Topics of special interest include but are not limited to biomedical applications, Internet of Things, and Industry 4.0.

Dr. Giuseppe Pitarresi
Prof. Dr. Piervincenzo Rizzo
Prof. Dr. Alberto Milazzo
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 structures
Industry 4.0
nondestructive evaluation
smart materials

Published Papers (6 papers)

Download All Papers

Research

20 pages, 7876 KiB

Open AccessArticle

Railroad Sleeper Condition Monitoring Using Non-Contact in Motion Ultrasonic Ranging and Machine Learning-Based Image Processing

by Diptojit Datta, Ali Zare Hosseinzadeh, Ranting Cui and Francesco Lanza di Scalea

Sensors 2023, 23(6), 3105; https://doi.org/10.3390/s23063105 - 14 Mar 2023

Cited by 3 | Viewed by 1776

Abstract

An ultrasonic sonar-based ranging technique is introduced for measuring full-field railroad crosstie (sleeper) deflections. Tie deflection measurements have numerous applications, such as detecting degrading ballast support conditions and evaluating sleeper or track stiffness. The proposed technique utilizes an array of air-coupled ultrasonic transducers oriented parallel to the tie, capable of “in-motion” contactless inspections. The transducers are used in pulse-echo mode, and the distance between the transducer and the tie surface is computed by tracking the time-of-flight of the reflected waveforms from the tie surface. An adaptive, reference-based cross-correlation operation is used to compute the relative tie deflections. Multiple measurements along the width of the tie allow the measurement of twisting deformations and longitudinal deflections (3D deflections). Computer vision-based image classification techniques are also utilized for demarcating tie boundaries and tracking the spatial location of measurements along the direction of train movement. Results from field tests, conducted at walking speed at a BNSF train yard in San Diego, CA, with a loaded train car are presented. The tie deflection accuracy and repeatability analyses indicate the potential of the technique to extract full-field tie deflections in a non-contact manner. Further developments are needed to enable measurements at higher speeds. Full article

(This article belongs to the Special Issue Sensors Applications in Structural Health Monitoring: Extended Papers from the 10th European Workshop on Structural Health Monitoring (EWSHM))

► Show Figures

Figure 1

31 pages, 5649 KiB

Open AccessArticle

Fusing Expert Knowledge with Monitoring Data for Condition Assessment of Railway Welds

by Cyprien Hoelzl, Giacomo Arcieri, Lucian Ancu, Stanislaw Banaszak, Aurelia Kollros, Vasilis Dertimanis and Eleni Chatzi

Sensors 2023, 23(5), 2672; https://doi.org/10.3390/s23052672 - 28 Feb 2023

Cited by 3 | Viewed by 2012

Abstract

Monitoring information can facilitate the condition assessment of railway infrastructure, via delivery of data that is informative on condition. A primary instance of such data is found in Axle Box Accelerations (ABAs), which track the dynamic vehicle/track interaction. Such sensors have been installed on specialized monitoring trains, as well as on in-service On-Board Monitoring (OBM) vehicles across Europe, enabling a continuous assessment of railway track condition. However, ABA measurements come with uncertainties that stem from noise corrupt data and the non-linear rail–wheel contact dynamics, as well as variations in environmental and operational conditions. These uncertainties pose a challenge for the condition assessment of rail welds through existing assessment tools. In this work, we use expert feedback as a complementary information source, which allows the narrowing down of these uncertainties, and, ultimately, refines assessment. Over the past year, with the support of the Swiss Federal Railways (SBB), we have assembled a database of expert evaluations on the condition of rail weld samples that have been diagnosed as critical via ABA monitoring. In this work, we fuse features derived from the ABA data with expert feedback, in order to refine defection of faulty (defect) welds. Three models are employed to this end; Binary Classification and Random Forest (RF) models, as well as a Bayesian Logistic Regression (BLR) scheme. The RF and BLR models proved superior to the Binary Classification model, while the BLR model further delivered a probability of prediction, quantifying the confidence we might attribute to the assigned labels. We explain that the classification task necessarily suffers high uncertainty, which is a result of faulty ground truth labels, and explain the value of continuously tracking the weld condition. Full article

(This article belongs to the Special Issue Sensors Applications in Structural Health Monitoring: Extended Papers from the 10th European Workshop on Structural Health Monitoring (EWSHM))

► Show Figures

Figure 1

15 pages, 19745 KiB

Open AccessArticle

Strain Monitoring and Crack Detection in Masonry Walls under In-Plane Shear Loading Using Smart Bricks: First Results from Experimental Tests and Numerical Simulations

by Andrea Meoni, Antonella D’Alessandro, Felice Saviano, Gian Piero Lignola, Fulvio Parisi and Filippo Ubertini

Sensors 2023, 23(4), 2211; https://doi.org/10.3390/s23042211 - 16 Feb 2023

Cited by 2 | Viewed by 1653

Abstract

A diffuse and continuous monitoring of the in-service structural response of buildings can allow for the early identification of the formation of cracks and collapse mechanisms before the occurrence of severe consequences. In the case of existing masonry constructions, the implementation of tailored Structural Health Monitoring (SHM) systems appears quite significant, given their well-known susceptibility to brittle failures. Recently, a new sensing technology based on smart bricks, i.e., piezoresistive brick-like sensors, was proposed in the literature for the SHM of masonry constructions. Smart bricks can be integrated within masonry to monitor strain and detect cracks. At present, the effectiveness of smart bricks has been proven in different structural settings. This paper contributes to the research by investigating the strain-sensitivity of smart bricks of standard dimensions when inserted in masonry walls subjected to in-plane shear loading. Real-scale masonry walls instrumented with smart bricks and displacement sensors were tested under diagonal compression, and numerical simulations were conducted to interpret the experimental results. At peak condition, numerical models provided comparable strain values to those of smart bricks, i.e., approximately equal to 10⁻⁴, with similar trends. Overall, the effectiveness of smart bricks in strain monitoring and crack detection is demonstrated. Full article

(This article belongs to the Special Issue Sensors Applications in Structural Health Monitoring: Extended Papers from the 10th European Workshop on Structural Health Monitoring (EWSHM))

► Show Figures

Figure 1

22 pages, 2515 KiB

Open AccessArticle

A Bayesian Method for Material Identification of Composite Plates via Dispersion Curves

by Marcus Haywood-Alexander, Nikolaos Dervilis, Keith Worden, Robin S. Mills, Purim Ladpli and Timothy J. Rogers

Sensors 2023, 23(1), 185; https://doi.org/10.3390/s23010185 - 24 Dec 2022

Viewed by 1093

Abstract

Ultrasonic guided waves offer a convenient and practical approach to structural health monitoring and non-destructive evaluation. A key property of guided waves is the fully defined relationship between central frequency and propagation characteristics (phase velocity, group velocity and wavenumber)—which is described using dispersion curves. For many guided wave-based strategies, accurate dispersion curve information is invaluable, such as group velocity for localisation. From experimental observations of dispersion curves, a system identification procedure can be used to determine the governing material properties. As well as returning an estimated value, it is useful to determine the distribution of these properties based on measured data. A method of simulating samples from these distributions is to use the iterative Markov-Chain Monte Carlo (MCMC) procedure, which allows for freedom in the shape of the posterior. In this work, a scanning-laser Doppler vibrometer is used to record the propagation of Lamb waves in a unidirectional-glass-fibre composite plate, and dispersion curve data for various propagation angles are extracted. Using these measured dispersion curve data, the MCMC sampling procedure is performed to provide a Bayesian approach to determining the dispersion curve information for an arbitrary plate. The distribution of the material properties at each angle is discussed, including the inferred confidence in the predicted parameters. The percentage errors of the estimated values for the parameters were 10–15 points larger when using the most likely estimates, as opposed to calculating from the posterior distributions, highlighting the advantages of using a probabilistic approach. Full article

(This article belongs to the Special Issue Sensors Applications in Structural Health Monitoring: Extended Papers from the 10th European Workshop on Structural Health Monitoring (EWSHM))

► Show Figures

Figure 1

28 pages, 12071 KiB

Open AccessArticle

Vibration-Based Approach to Measure Rail Stress: Modeling and First Field Test

by Matthew Belding, Alireza Enshaeian and Piervincenzo Rizzo

Sensors 2022, 22(19), 7447; https://doi.org/10.3390/s22197447 - 30 Sep 2022

Cited by 10 | Viewed by 1578

Abstract

This paper describes a non-invasive inspection technique for the estimation of longitudinal stress in continuous welded rails (CWR) to infer the rail neutral temperature (RNT), i.e., the temperature at which the net longitudinal force in the rail is zero. The technique is based on the use of finite element method (FEM), vibration measurements, and machine learning (ML). FEM is used to model the relationship between the boundary conditions and the longitudinal stress of any given CWR to the vibration characteristics (mode shapes and frequencies) of the rail. The results of the numerical analysis are used to train a ML algorithm that is then tested using field data obtained by an array of accelerometers polled on the track of interest. In the study presented in this article, the proposed technique was proven in the field during an experimental campaign conducted in Colorado. A commercial FEM software was used to model the rail track as a short rail segment repeated indefinitely and under varying boundary conditions and stress. Three datasets were prepared and fed to ML models developed using hyperparameter search optimization techniques and k-fold cross validation to infer the stress or the RNT. The frequencies of vibration were extracted from the time waveforms obtained from two accelerometers temporarily attached to the rail. The results of the experiments demonstrated that the success of the technique is dependent on the accuracy of the model and the ability to properly identify the modeshapes. The results also proved that the ML was also able to predict successfully the neutral temperature of the tested rail by using only a limited number of experimental data for the training. Full article

(This article belongs to the Special Issue Sensors Applications in Structural Health Monitoring: Extended Papers from the 10th European Workshop on Structural Health Monitoring (EWSHM))

► Show Figures

Figure 1

18 pages, 6177 KiB

Open AccessArticle

Improved Adaptive Multi-Objective Particle Swarm Optimization of Sensor Layout for Shape Sensing with Inverse Finite Element Method

by Xiaohan Li, Shengtao Niu, Hong Bao and Naigang Hu

Sensors 2022, 22(14), 5203; https://doi.org/10.3390/s22145203 - 12 Jul 2022

Cited by 6 | Viewed by 1388

Abstract

The inverse finite element method (iFEM) is one of the most effective deformation reconstruction techniques for shape sensing, which is widely applied in structural health monitoring. The distribution of strain sensors affects the reconstruction accuracy of the structure in iFEM. This paper proposes a method to optimize the layout of sensors rationally. Firstly, this paper constructs a dual-objective model based on the accuracy and robustness indexes. Then, an improved adaptive multi-objective particle swarm optimization (IAMOPSO) algorithm is developed for this model, which introduces initialization strategy, the adaptive inertia weight strategy, the guided particle selection strategy and the external candidate solution (ECS) set maintenance strategy to multi-objective particle swarm optimization (MOPSO). Afterwards, the performance of IAMOPSO is verified by comparing with MOPSO applied on the existing inverse beam model. Finally, the IAMOPSO is employed to the deformation reconstruction of complex plate-beam model. The numerical and experimental results demonstrate that the IAMOPSO is an excellent tool for sensor layout in iFEM. Full article

(This article belongs to the Special Issue Sensors Applications in Structural Health Monitoring: Extended Papers from the 10th European Workshop on Structural Health Monitoring (EWSHM))

► Show Figures

Figure 1

Journal Menu

Journal Browser

Sensors Applications in Structural Health Monitoring: Extended Papers from the 10th European Workshop on Structural Health Monitoring (EWSHM)

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (6 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI