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Structural Health Monitoring Based on Sensing Technology

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

Deadline for manuscript submissions: 20 May 2024 | Viewed by 25912

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Special Issue Editors

Dr. Muhammad Arslan Khan

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Guest Editor

Affiliation: School of Civil Engineering, University College Dublin, D04 V1W8 Dublin, Ireland
Interests: structural dynamics; sensor-based structural monitoring; infrastructure damage detection; bridge monitoring; structural evaluation using vibrations; structural monitoring of bridge and facilities

Dr. Abdollah Malekjafarian

E-Mail Website
Guest Editor

School of Civil Engineering, University College Dublin, D04 V1W8 Dublin, Ireland
Interests: structural dynamics and assessments; railway track monitoring; railway bridge monitoring; machine learning for SHM
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Giorgio Monti

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Guest Editor

Department of Structural and Geotechnical Engineering, Sapienza University of Rome, 00185 Roma, Italy
Interests: structure assessment; modelling and analysis of RC structures; bridge health monitoring; seismic protection of structure; sensor-based monitoring; assessment and preservation of cultural heritage
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Eugene J. OBrien

E-Mail Website
Guest Editor

School of Civil Engineering, University College Dublin, D04 V1W8 Dublin, Ireland
Interests: bridge health monitoring and assessments; weigh-in-motion; sensor-based monitoring; structural dynamics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the growing demand for infrastructure and transportation structure facilities, the requirement for structural health monitoring (SHM) of old structures is increasing. For that reason, this issue focuses on structural health monitoring using the latest sensing technology toward developing and disseminating high-quality research. The main target of this issue is to bring together and share research relating innovative SHM methods that use the latest sensing technologies to produce effective and reliable techniques. With the help of latest technologies, there is a need for effective and reliable SHM systems that can supplement or replace traditional manual inspections. The research in the field of sensor-based SHM includes the latest experimental and theoretical studies, findings and computational investigations that may improve existing or novel SHM systems. All studies covering (but not exclusive to) the topics and fields below are welcome in this issue:

Structure health monitoring;
Sensor monitoring;
Structure performance assessment;
Industrial structures monitoring;
Intelligent monitoring systems;
Damage detection;
Weigh-in-motion systems;
Novel SHM techniques;
Drone-based monitoring;
Scour monitoring;
Foundation/support damage detection;
Environmental effect detection;
Artificial intelligence;
Modal estimation;
Frequency-based analysis;
Structural dynamics;
Computational modeling and digital twinning;
Laboratory validation;
Disaster management;
Internet of Things;
Remote sensing;
Road profile detection.

Dr. Muhammad Arslan Khan
Dr. Abdollah Malekjafarian
Prof. Dr. Giorgio Monti
Prof. Dr. Eugene J. OBrien
Guest Editors

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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.

Published Papers (12 papers)

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Research

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20 pages, 10259 KiB

Open AccessArticle

A Feasibility Study on Extension of Measurement Distance in Vision Sensor Using Super-Resolution for Dynamic Response Measurement

by Dooyong Cho and Junho Gong

Sensors 2023, 23(20), 8496; https://doi.org/10.3390/s23208496 - 16 Oct 2023

Viewed by 667

Abstract

The current civil infrastructure conditions can be assessed through the measurement of displacement using conventional contact-type sensors. To address the disadvantages of traditional sensors, vision-based sensor measurement systems have been derived in numerous studies and proven as an alternative to traditional sensors. Despite the benefits of the vision sensor, it is well known that the accuracy of the vision-based displacement measurement is largely dependent on the camera extrinsic or intrinsic parameters. In this study, the feasibility study of a deep learning-based single image super-resolution (SISR) technique in a vision-based sensor system is conducted to alleviate the low spatial resolution of image frames at long measurement distance ranges. Additionally, its robustness is evaluated using shaking table tests. As a result, it is confirmed that the SISR can reconstruct definite images of natural targets resulting in an extension of the measurement distance range. Additionally, it is determined that the SISR mitigates displacement measurement error in the vision sensor-based measurement system. Based on this fundamental study of SISR in the feature point-based measurement system, further analysis such as modal analysis, damage detection, and so forth should be continued in order to explore the functionality of SR images by applying low-resolution displacement measurement footage. Full article

(This article belongs to the Special Issue Structural Health Monitoring Based on Sensing Technology)

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17 pages, 7204 KiB

Open AccessArticle

Method for Diagnosing Bearing Faults in Electromechanical Equipment Based on Improved Prototypical Networks

by Zilong Wang, Honghai Shen, Wenzhuo Xiong, Xueming Zhang and Jinghua Hou

Sensors 2023, 23(9), 4485; https://doi.org/10.3390/s23094485 - 04 May 2023

Cited by 1 | Viewed by 1606

Abstract

Due to the complexity of electromechanical equipment and the difficulties in obtaining large-scale health monitoring datasets, as well as the long-tailed distribution of data, existing methods ignore certain characteristics of health monitoring data. In order to solve these problems, this paper proposes a method for the fault diagnosis of rolling bearings in electromechanical equipment based on an improved prototypical network—the weight prototypical networks (WPorNet). The main contributions of this paper are as follows: (1) the prototypical networks, which perform well on small-sample classification tasks, were improved by calculating the different levels of influence of support sample distributions in order to achieve the prototypical calculation. The change in sample influence was calculated using the Kullback–Leibler divergence of the sample distribution. The influence change in a specific sample can be measured by assessing how much the distribution changes in the absence of that sample; and (2) The Gramian Angular Field (GAF) algorithm was used to transform one-dimensional time series into two-dimensional vibration images, which greatly improved the application effect of the 2D convolutional neural network (CNN). Through experiments on MAFAULDA and CWRU bearing datasets, it was shown that this network effectively solves the shortcomings of a small number of valid samples and a long-tail distribution in health monitoring data, it enhances the dependency between the samples and the global data, it improves the model’s feature extraction ability, and it enhances the accuracy of model classification. Compared with the prototypical network, the improved network model increased the performance of the 2-way 10-shot, 2-way 20-shot, and 2-way 50-shot classification tasks by 5.23%, 5.74%, and 4.37%, respectively, and it increased the performance of the 4-way 10-shot, 4-way 20-shot, and 4-way 50-shot classification tasks by 12.02%, 10.47%, and 4.66%, respectively. Experimental results show that the improved prototypical network model has higher sample classification accuracy and stronger anti-interference ability compared with traditional small-sample classification models. Full article

(This article belongs to the Special Issue Structural Health Monitoring Based on Sensing Technology)

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14 pages, 2741 KiB

Open AccessArticle

Large Displacement Detection Using Improved Lucas–Kanade Optical Flow

by Saleh Al-Qudah and Mijia Yang

Sensors 2023, 23(6), 3152; https://doi.org/10.3390/s23063152 - 15 Mar 2023

Cited by 2 | Viewed by 1952

Abstract

Displacement is critical when it comes to the evaluation of civil structures. Large displacement can be dangerous. There are many methods that can be used to monitor structural displacements, but every method has its benefits and limitations. Lucas–Kanade (LK) optical flow is recognized as a superior computer vision displacement tracking method, but it only applies to small displacement monitoring. An upgraded LK optical flow method is developed in this study and used to detect large displacement motions. One motion controlled by a multiple purpose testing system (MTS) and a free-falling experiment were designed to verify the developed method. The results provided by the upgraded LK optical flow method showed 97 percent accuracy when compared with the movement of the MTS piston. In order to capture the free-falling large displacement, the pyramid and warp optical flow methods are included in the upgraded LK optical flow method and compared with the results of template matching. The warping algorithm with the second derivative Sobel operator provides accurate displacements with 96% average accuracy. Full article

(This article belongs to the Special Issue Structural Health Monitoring Based on Sensing Technology)

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24 pages, 26890 KiB

Open AccessArticle

Model Updating Concept Using Bridge Weigh-in-Motion Data

by Doron Hekič, Andrej Anžlin, Maja Kreslin, Aleš Žnidarič and Peter Češarek

Sensors 2023, 23(4), 2067; https://doi.org/10.3390/s23042067 - 12 Feb 2023

Cited by 5 | Viewed by 1776

Abstract

Finite element (FE) model updating of bridges is based on the measured modal parameters and less frequently on the measured structural response under a known load. Until recently, the FE model updating did not consider strain measurements from sensors installed for weighing vehicles with bridge weigh-in-motion (B-WIM) systems. A 50-year-old multi-span concrete highway viaduct, renovated between 2017 and 2019, was equipped with continuous monitoring system with over 200 sensors, and a B-WIM system. In the most heavily instrumented span, the maximum measured longitudinal strains induced by the full-speed calibration vehicle passages were compared with the modelled strains. Based on the sensitivity study results, three variables that affected its overall stiffness were updated: Young’s modulus adjustment factor of all structural elements, and two anchorage reduction factors that considered the interaction between the superstructure and non-structural elements. The analysis confirmed the importance of the initial manual FE model updating to correctly reflect the non-structural elements during the automatic nonlinear optimisation. It also demonstrated a successful use of pseudo-static B-WIM loading data during the model updating process and the potential to extend the proposed approach to using random B-WIM-weighed vehicles for FE model updating and long-term monitoring of structural parameters and load-dependent phenomena. Full article

(This article belongs to the Special Issue Structural Health Monitoring Based on Sensing Technology)

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24 pages, 10731 KiB

Open AccessArticle

Identification of Vibration Frequencies of Railway Bridges from Train-Mounted Sensors Using Wavelet Transformation

by Emrah Erduran, Fredrik Marøy Pettersen, Semih Gonen and Albert Lau

Sensors 2023, 23(3), 1191; https://doi.org/10.3390/s23031191 - 20 Jan 2023

Cited by 7 | Viewed by 1824

Abstract

This article presents a novel methodology to extract the bridge frequencies from the vibrations recorded on train-mounted sensors. Continuous wavelet transform is used to distinguish the bridge frequencies from the other peaks that are visible in the Fourier amplitude spectrum of the accelerations recorded on train bogies. The efficacy of the proposed method is demonstrated through numerical case studies. For this, a detailed three-dimensional finite element model that can capture the vibration characteristics of the bridge, track, and train is created, and each component of the model is separately validated. The train model used is a three-dimensional multi-degree-of-freedom system that can simulate the pitching and rolling behavior. The train was then virtually driven over the bridge at different speeds and under varying track irregularities to evaluate the robustness of the proposed method in extracting bridge frequencies from train-mounted sensors under different conditions. The proposed methodology is shown to be capable of identifying bridge modal frequencies even for aggressive track irregularity profiles and relatively high speeds of trains. Full article

(This article belongs to the Special Issue Structural Health Monitoring Based on Sensing Technology)

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19 pages, 5663 KiB

Open AccessArticle

Three-Dimensional Digital Image Correlation Based on Speckle Pattern Projection for Non-Invasive Vibrational Analysis

by Alvaro Souto Janeiro, Antonio Fernández López, Marcos Chimeno Manguan and Pablo Pérez-Merino

Sensors 2022, 22(24), 9766; https://doi.org/10.3390/s22249766 - 13 Dec 2022

Cited by 6 | Viewed by 1939

Abstract

Non-contact vibration measurements are relevant for non-invasively characterizing the mechanical behavior of structures. This paper presents a novel methodology for full-field vibrational analysis at high frequencies using the three-dimensional digital image correlation technique combined with the projection of a speckle pattern. The method includes stereo calibration and image processing routines for accurate three-dimensional data acquisition. Quantitative analysis allows the extraction of several deformation parameters, such as the cross-correlation coefficients, shape and intensity, as well as the out-of-plane displacement fields and mode shapes. The potential of the methodology is demonstrated on an Unmanned Aerial Vehicle wing made of composite material, followed by experimental validation with reference accelerometers. The results obtained with the projected three-dimensional digital image correlation show a percentage of error below 5% compared with the measures of accelerometers, achieving, therefore, high sensitivity to detect the dynamic modes in structures made of composite material. Full article

(This article belongs to the Special Issue Structural Health Monitoring Based on Sensing Technology)

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21 pages, 45014 KiB

Open AccessArticle

Contactless Deformation Monitoring of Bridges with Spatio-Temporal Resolution: Profile Scanning and Microwave Interferometry

by Florian Schill, Chris Michel and Andrei Firus

Sensors 2022, 22(23), 9562; https://doi.org/10.3390/s22239562 - 06 Dec 2022

Cited by 7 | Viewed by 2043

Abstract

Against the background of an aging infrastructure, the condition assessment process of existing bridges is becoming an ever more challenging task for structural engineers. Short-term measurements and structural monitoring are valuable tools that can lead to a more accurate assessment of the remaining service life of structures. In this context, contactless sensors have great potential, as a wide range of applications can already be covered with relatively little effort and without having to interrupt traffic. In particular, profile scanning and microwave interferometry, have become increasingly important in the research field of bridge measurement and monitoring in recent years. In contrast to other contactless displacement sensors, both technologies enable a spatially distributed detection of absolute structural displacements. In addition, their high sampling rate enables the detection of the dynamic structural behaviour. This paper analyses the two sensor types in detail and discusses their advantages and disadvantages for the deformation monitoring of bridges. It focuses on a conceptual comparison between the two technologies and then discusses the main challenges related to their application in real-world structures in operation, highlighting the respective limitations of both sensors. The findings are illustrated with measurement results at a railway bridge in operation. Full article

(This article belongs to the Special Issue Structural Health Monitoring Based on Sensing Technology)

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19 pages, 6445 KiB

Open AccessArticle

Characterization of Volcanic Cloud Components Using Machine Learning Techniques and SEVIRI Infrared Images

by Federica Torrisi, Eleonora Amato, Claudia Corradino, Salvatore Mangiagli and Ciro Del Negro

Sensors 2022, 22(20), 7712; https://doi.org/10.3390/s22207712 - 11 Oct 2022

Cited by 7 | Viewed by 1790

Abstract

Volcanic explosive eruptions inject several different types of particles and gasses into the atmosphere, giving rise to the formation and propagation of volcanic clouds. These can pose a serious threat to the health of people living near an active volcano and cause damage to air traffic. Many efforts have been devoted to monitor and characterize volcanic clouds. Satellite infrared (IR) sensors have been shown to be well suitable for volcanic cloud monitoring tasks. Here, a machine learning (ML) approach was developed in Google Earth Engine (GEE) to detect a volcanic cloud and to classify its main components using satellite infrared images. We implemented a supervised support vector machine (SVM) algorithm to segment a combination of thermal infrared (TIR) bands acquired by the geostationary MSG-SEVIRI (Meteosat Second Generation—Spinning Enhanced Visible and Infrared Imager). This ML algorithm was applied to some of the paroxysmal explosive events that occurred at Mt. Etna between 2020 and 2022. We found that the ML approach using a combination of TIR bands from the geostationary satellite is very efficient, achieving an accuracy of 0.86, being able to properly detect, track and map automatically volcanic ash clouds in near real-time. Full article

(This article belongs to the Special Issue Structural Health Monitoring Based on Sensing Technology)

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18 pages, 6871 KiB

Open AccessArticle

Investigation of Temperature Variations and Extreme Temperature Differences for the Corrugated Web Steel Beams under Solar Radiation

by Shiji Huang, Chenzhi Cai, Yunfeng Zou, Xuhui He and Tieming Zhou

Sensors 2022, 22(12), 4557; https://doi.org/10.3390/s22124557 - 16 Jun 2022

Cited by 3 | Viewed by 1590

Abstract

Due to the coupling impacts of solar radiation, wind, air temperature and other environmental parameters, the temperature field of steel structures is significantly non-uniform during their construction and service stages. Corrugated web steel beams have gained popularity in structural engineering during the last few decades, while their thermal actions are barely investigated. In this paper, both experimental and numerical investigations were conducted to reveal the non-uniform features and time variation of the corrugated web steel beams under various environmental conditions. The heat-transfer simulation model was established and verified using the experimental temperature data. Both the experiment and simulation results demonstrate that the steel beam has a complicated and non-uniform temperature field. Moreover, 2-year continuous numerical simulations of steel beams’ thermal actions regarding eight different cities were carried out to investigate the long-term temperature variations. Finally, based on the long-term simulation results and extreme value analysis (EVA), the representative values of steel beams’ daily temperature difference with a 50-year return period were determined. The extreme temperature difference of the steel beam in Harbin reached up to 46.9 °C, while the extreme temperature difference in Haikou was 28.8 °C. The extreme temperature difference is highly associated with the steel beam’s location and surrounding climate. Ideally, the outcomes will provide some contributions for the structural design regarding the corrugated web steel beam. Full article

(This article belongs to the Special Issue Structural Health Monitoring Based on Sensing Technology)

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20 pages, 7501 KiB

Open AccessArticle

Bridge Crack Inspection Efficiency of an Unmanned Aerial Vehicle System with a Laser Ranging Module

by Szu-Pyng Kao, Feng-Liang Wang, Jhih-Sian Lin, Jichiang Tsai, Yi-De Chu and Pen-Shan Hung

Sensors 2022, 22(12), 4469; https://doi.org/10.3390/s22124469 - 13 Jun 2022

Cited by 2 | Viewed by 2684

Abstract

In this study, an unmanned aerial vehicle (UAV) with a camera and laser ranging module was developed to inspect bridge cracks. Four laser ranging units were installed adjacent to the camera to measure the distance from the camera to the object to calculate the object’s projection plane and overcome the limitation of vertical photography. The image processing method was adopted to extract crack information and calculate crack sizes. The developed UAV was used in outdoor bridge crack inspection tests; for images taken at a distance of 2.5 m, we measured the crack length, and the error between the result and the real length was less than 0.8%. The developed UAV has a dual-lens design, where one lens is used for bridge inspections and the other lens is used for flight control. The camera of the developed UAV can be rotated from the horizontal level to the zenith according to user requirements; thus, this UAV achieves high safety and efficiency in bridge inspections. Full article

(This article belongs to the Special Issue Structural Health Monitoring Based on Sensing Technology)

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Review

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21 pages, 2827 KiB

Open AccessReview

A Systematic Review of Structural Health Monitoring Systems to Strengthen Post-Earthquake Assessment Procedures

by Brian López-Castro, Ana Gabriela Haro-Baez, Diego Arcos-Aviles, Marco Barreno-Riera and Bryan Landázuri-Avilés

Sensors 2022, 22(23), 9206; https://doi.org/10.3390/s22239206 - 26 Nov 2022

Cited by 6 | Viewed by 4014

Abstract

Structural health monitoring (SHM) is vital to ensuring the integrity of people and structures during earthquakes, especially considering the catastrophic consequences that could be registered in countries within the Pacific ring of fire, such as Ecuador. This work reviews the technologies, architectures, data processing techniques, damage identification techniques, and challenges in state-of-the-art results with SHM system applications. These studies use several data processing techniques such as the wavelet transform, the fast Fourier transform, the Kalman filter, and different technologies such as the Internet of Things (IoT) and machine learning. The results of this review highlight the effectiveness of systems aiming to be cost-effective and wireless, where sensors based on microelectromechanical systems (MEMS) are standard. However, despite the advancement of technology, these face challenges such as optimization of energy resources, computational resources, and complying with the characteristic of real-time processing. Full article

(This article belongs to the Special Issue Structural Health Monitoring Based on Sensing Technology)

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Other

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31 pages, 4217 KiB

Open AccessSystematic Review

A Digital Image Correlation Technique for Laboratory Structural Tests and Applications: A Systematic Literature Review

by Mohammed Abbas Mousa, Mustafasanie M. Yussof, Thulfiqar S. Hussein, Lateef N. Assi and SeyedAli Ghahari

Sensors 2023, 23(23), 9362; https://doi.org/10.3390/s23239362 - 23 Nov 2023

Viewed by 1357

Abstract

Digital image correlation (DIC) is an optical technique used to measure surface displacements and strains in materials and structures. This technique has demonstrated significant utility in structural examination and monitoring. This manuscript offers a comprehensive review of the contemporary research and applications that have leveraged the DIC technique in laboratory-based structural tests. The reviewed works encompass a broad spectrum of structural components, such as concrete beams, columns, pillars, masonry walls, infills, composite materials, structural joints, steel beams, slabs, and other structural elements. These investigations have underscored the efficacy of DIC as a metrological instrument for the precise quantification of surface deformation and strain in these structural components. Moreover, the constraints of the DIC technique have been highlighted, especially in scenarios involving extensive or complex test configurations. Notwithstanding these constraints, the effectiveness of the DIC methodology has been validated as a strain measurement instrument, offering numerous benefits such as non-invasive operation, full-field measurement capability, high precision, real-time surveillance, and compatibility with integration into other measurement instruments and methodologies. Full article

(This article belongs to the Special Issue Structural Health Monitoring Based on Sensing Technology)

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