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Recent Advances in Structural Health Monitoring and Damage Detection
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Recent Advances in Structural Health Monitoring and Damage Detection

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

Deadline for manuscript submissions: 31 May 2024 | Viewed by 14575

Special Issue Editors

Dr. Eduardo Barrera

E-Mail Website
Guest Editor
Instrumentation and Applied Acoustics Research Group, Universidad Politécnica de Madrid, C/Nikola Tesla, s/n, 28031 Madrid, Spain
Interests: structural health monitoring; electronic systems for SHM; wireless sensors; ultrasonic guided wave-based structural health monitoring; non-destructive evaluation; signal processing for damage localization; embedded systems
Dr. Guillermo Azuara

E-Mail Website
Guest Editor
Instrumentation and Applied Acoustics Research Group, Universidad Politécnica de Madrid, C/Nikola Tesla, s/n, 28031 Madrid, Spain
Interests: ultrasonic guided wave-based structural health monitoring; non-destructive evaluation; signal processing; embedded systems; hardware acceleration; deep learning

Special Issue Information

Dear Colleagues,

Structural health monitoring (SHM) is the most interesting topic within non-destructive evaluation (NDE), as well as the most challenging technique to be implemented across several fields, especially in aeronautics and aerospace, as well as in wind turbines. In these particular fields, the challenges involve all the possible techniques and technologies which participate in the SHM process: signal generation and acquisition systems, wired and wireless connections, embedded sensors, data cleansing and processing techniques, interpretation of the extracted information, damage localization algorithms, and damage characterization and quantification methods. This Special Issue is intended to present all the aforementioned techniques and technologies, concerning the current state-of-the-art of the field, as well as to introduce emerging methodologies that can achieve SHM to be fully implemented in the industrial context and, particularly, in proper applications of the aeronautical and aerospace world, such as commercial or transport aviation, or wind turbines, in which SHM has not been completely applied yet. Papers on topics including but not limited to the following aspects will be considered for publication:

  • Dedicated instrumentation for SHM;
  • Electronic embedded systems for SHM;
  • Wireless sensors;
  • Piezoelectric transducers-based SHM systems;
  • Ultrasonic guided waves (UGW) -based methods;
  • Fiber Bragg gratings (FBG) systems;
  • Physics-based damage analysis;
  • Data-driven methods;
  • Machine learning and deep learning applied to SHM;
  • Methodologies for damage localization;
  • Damage characterization;
  • Estimation of damage severity;
  • Real-time SHM;
  • SHM applied to composite materials

Dr. Eduardo Barrera
Dr. Guillermo Azuara
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
  • ultrasonic guided waves
  • damage detection
  • damage localization
  • damage quantification
  • machine learning
  • embedded sensors
  • wireless sensors
  • electronic systems for SHM

Published Papers (6 papers)

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Research

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18 pages, 6991 KiB  
Article
Trend Decomposition for Temperature Compensation in a Radar-Based Structural Health Monitoring System of Wind Turbine Blades
by Jonas Simon, Jochen Moll and Viktor Krozer
Sensors 2024, 24(3), 800; https://doi.org/10.3390/s24030800 - 25 Jan 2024
Viewed by 778
Abstract
The compensation of temperature is critical in every structural health monitoring (SHM) system for achieving maximum damage detection performance. This paper analyses a novel approach based on seasonal trend decomposition to eliminate the temperature effect in a radar-based SHM system for wind turbine [...] Read more.
The compensation of temperature is critical in every structural health monitoring (SHM) system for achieving maximum damage detection performance. This paper analyses a novel approach based on seasonal trend decomposition to eliminate the temperature effect in a radar-based SHM system for wind turbine blades that operates in the frequency band from 58 to 63.5 GHz. While the original seasonal trend decomposition searches for the trend of a periodic signal in its entirety, the new method uses a moving average to determine trends for each point of a periodic signal. The points of the seasonal signal no longer need to have the same trend. Based on the determined trends, the measurement signal can be corrected by temperature effects, providing accurate damage detection results under changing temperature conditions. The performance of the trend decomposition is demonstrated with experimental data obtained during a full-scale fatigue test of a 31 m long wind turbine blade subjected to ambient temperature variations. For comparison, the well-known optimal baseline selection (OBS) approach is used, which is based on multiple baseline measurements at different temperature conditions. The use of metrics, such as the contrast in damage indicators, enables the performance assessment of both methods. Full article
(This article belongs to the Special Issue Recent Advances in Structural Health Monitoring and Damage Detection)
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25 pages, 15012 KiB  
Article
Efficacy of Vehicle Scanning Methods in Estimating the Mode Shapes of Bridges Seated on Elastic Supports
by Kultigin Demirlioglu, Semih Gonen and Emrah Erduran
Sensors 2023, 23(14), 6335; https://doi.org/10.3390/s23146335 - 12 Jul 2023
Cited by 3 | Viewed by 852
Abstract
This study systematically assesses the efficacy of the vehicle scanning methods (VSM) in accurately estimating the mode shapes of bridges seated on elastic supports. Three state-of-the-art VSM methods are employed to obtain the mode shapes of bridges using the vehicle data during its [...] Read more.
This study systematically assesses the efficacy of the vehicle scanning methods (VSM) in accurately estimating the mode shapes of bridges seated on elastic supports. Three state-of-the-art VSM methods are employed to obtain the mode shapes of bridges using the vehicle data during its travel. Two of the evaluated methods use a signal decomposition technique to extract the modal components of the bridge from the contact point of the response while the third one relies on the segmentation of the measured signals along the bridge deck and applying an operational modal analysis tool to each segmented signal to estimate the mode shapes. Numerical analyses are conducted on one single- and one two-span bridge, considering smooth and rough road profiles, different vehicle speeds, and presence of lead vehicle. The accuracy of the numerical models used in developing and assessing vehicle scanning models is tested, and the results of the study demonstrate the method using a half-car vehicle model and signal decomposition technique shows robustness against increasing vehicle speeds and road roughness while the method applying the segmentation of the measured signals provides relatively accurate mode shape estimates at the bridge edges at low speed, although the three methods have their limitations. It is also observed that simplified bridge and vehicle models can hide potential challenges that arise from the complexity of actual vehicle and bridge systems. Considering that a significant number of bridges worldwide are built on elastic supports, the practical success of vehicle scanning methods depends on their ability to handle elastic boundary conditions with reliability. Therefore, the article provides valuable insights into the capabilities and limitations of the current vehicle scanning methods, paving the way for further advancements and refinements in these techniques. Full article
(This article belongs to the Special Issue Recent Advances in Structural Health Monitoring and Damage Detection)
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22 pages, 6836 KiB  
Article
Machine Learning-Based Rapid Post-Earthquake Damage Detection of RC Resisting-Moment Frame Buildings
by Edisson Alberto Moscoso Alcantara and Taiki Saito
Sensors 2023, 23(10), 4694; https://doi.org/10.3390/s23104694 - 12 May 2023
Viewed by 1584
Abstract
This study proposes a methodology to predict the damage condition of Reinforced Concrete (RC) resisting-moment frame buildings using Machine Learning (ML) methods. Structural members of six hundred RC buildings with varying stories and spans in X and Y directions were designed using the [...] Read more.
This study proposes a methodology to predict the damage condition of Reinforced Concrete (RC) resisting-moment frame buildings using Machine Learning (ML) methods. Structural members of six hundred RC buildings with varying stories and spans in X and Y directions were designed using the virtual work method. Sixty thousand time-history analyses using ten spectrum-matched earthquake records and ten scaling factors were carried out to cover the structures’ elastic and inelastic behavior. The buildings and earthquake records were split randomly into training data and testing data to predict the damage condition of new ones. In order to reduce bias, the random selection of buildings and earthquake records was carried out several times, and the mean and standard deviation of the accuracy were obtained. Moreover, 27 Intensity Measures (IM) based on acceleration, velocity, or displacement from the ground and roof sensor responses were used to capture the building’s behavior features. The ML methods used IMs, the number of stories, and the number of spans in X and Y directions as input data and the maximum inter-story drift ratio as output data. Finally, seven Machine Learning (ML) methods were trained to predict the damage condition of buildings, finding the best set of training buildings, IMs, and ML methods for the highest prediction accuracy. Full article
(This article belongs to the Special Issue Recent Advances in Structural Health Monitoring and Damage Detection)
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32 pages, 4543 KiB  
Article
Impact of Fireworks Industry Safety Measures and Prevention Management System on Human Error Mitigation Using a Machine Learning Approach
by Indumathi Nallathambi, Padmaja Savaram, Sudhakar Sengan, Meshal Alharbi, Samah Alshathri, Mohit Bajaj, Moustafa H. Aly and Walid El-Shafai
Sensors 2023, 23(9), 4365; https://doi.org/10.3390/s23094365 - 28 Apr 2023
Cited by 2 | Viewed by 5405
Abstract
In the fireworks industry (FI), many accidents and explosions frequently happen due to human error (HE). Human factors (HFs) always play a dynamic role in the incidence of accidents in workplace environments. Preventing HE is a main challenge for safety and precautions in [...] Read more.
In the fireworks industry (FI), many accidents and explosions frequently happen due to human error (HE). Human factors (HFs) always play a dynamic role in the incidence of accidents in workplace environments. Preventing HE is a main challenge for safety and precautions in the FI. Clarifying the relationship between HFs can help in identifying the correlation between unsafe behaviors and influential factors in hazardous chemical warehouse accidents. This paper aims to investigate the impact of HFs that contribute to HE, which has caused FI disasters, explosions, and incidents in the past. This paper investigates why and how HEs contribute to the most severe accidents that occur while storing and using hazardous chemicals. The impact of fireworks and match industry disasters has motivated the planning of mitigation in this proposal. This analysis used machine learning (ML) and recommends an expert system (ES). There were many significant correlations between individual behaviors and the chance of HE to occur. This paper proposes an ML-based prediction model for fireworks and match work industries in Sivakasi, Tamil Nadu. For this study analysis, the questionnaire responses are reviewed for accuracy and coded from 500 participants from the fireworks and match industries in Tamil Nadu who were chosen to fill out a questionnaire. The Chief Inspectorate of Factories in Chennai and the Training Centre for Industrial Safety and Health in Sivakasi, Tamil Nadu, India, significantly contributed to the collection of accident datasets for the FI in Tamil Nadu, India. The data are analyzed and presented in the following categories based on this study’s objectives: the effect of physical, psychological, and organizational factors. The output implemented by comparing ML models, support vector machine (SVM), random forest (RF), and Naïve Bayes (NB) accuracy is 86.45%, 91.6%, and 92.1%, respectively. Extreme Gradient Boosting (XGBoost) has the optimal classification accuracy of 94.41% of ML models. This research aims to create a new ES to mitigate HE risks in the fireworks and match work industries. The proposed ES reduces HE risk and improves workplace safety in unsafe, uncertain workplaces. Proper safety management systems (SMS) can prevent deaths and injuries such as fires and explosions. Full article
(This article belongs to the Special Issue Recent Advances in Structural Health Monitoring and Damage Detection)
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12 pages, 3553 KiB  
Article
Effect of Interface Modification on Mechanoluminescence-Inorganic Perovskite Impact Sensors
by Lucas Braga Carani, Vincent Obiozo Eze and Okenwa Okoli
Sensors 2023, 23(1), 236; https://doi.org/10.3390/s23010236 - 26 Dec 2022
Cited by 1 | Viewed by 2024
Abstract
It is becoming increasingly important to develop innovative self-powered, low-cost, and flexible sensors with the potential for structural health monitoring (SHM) applications. The mechanoluminescence (ML)-perovskite sensor is a potential candidate that combines the light-emitting principles of mechanoluminescence with the light-absorbing properties of perovskite [...] Read more.
It is becoming increasingly important to develop innovative self-powered, low-cost, and flexible sensors with the potential for structural health monitoring (SHM) applications. The mechanoluminescence (ML)-perovskite sensor is a potential candidate that combines the light-emitting principles of mechanoluminescence with the light-absorbing properties of perovskite materials. Continuous in-situ SHM with embedded sensors necessitates long-term stability. A highly stable cesium lead bromide photodetector with a carbon-based electrode and a zinc sulfide (ZnS): copper (Cu) ML layer was described in this article. The addition of a magnesium iodide (MgI2) interfacial modifier layer between the electron transport layer (ETL) and the Perovskite interface improved the sensor’s performance. Devices with the modified structure outperformed devices without the addition of MgI2 in terms of response time and impact-sensing applications. Full article
(This article belongs to the Special Issue Recent Advances in Structural Health Monitoring and Damage Detection)
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Review

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33 pages, 5628 KiB  
Review
Computer Vision-Based Bridge Inspection and Monitoring: A Review
by Kui Luo, Xuan Kong, Jie Zhang, Jiexuan Hu, Jinzhao Li and Hao Tang
Sensors 2023, 23(18), 7863; https://doi.org/10.3390/s23187863 - 13 Sep 2023
Cited by 8 | Viewed by 2822
Abstract
Bridge inspection and monitoring are usually used to evaluate the status and integrity of bridge structures to ensure their safety and reliability. Computer vision (CV)-based methods have the advantages of being low cost, simple to operate, remote, and non-contact, and have been widely [...] Read more.
Bridge inspection and monitoring are usually used to evaluate the status and integrity of bridge structures to ensure their safety and reliability. Computer vision (CV)-based methods have the advantages of being low cost, simple to operate, remote, and non-contact, and have been widely used in bridge inspection and monitoring in recent years. Therefore, this paper reviews three significant aspects of CV-based methods, including surface defect detection, vibration measurement, and vehicle parameter identification. Firstly, the general procedure for CV-based surface defect detection is introduced, and its application for the detection of cracks, concrete spalling, steel corrosion, and multi-defects is reviewed, followed by the robot platforms for surface defect detection. Secondly, the basic principle of CV-based vibration measurement is introduced, followed by the application of displacement measurement, modal identification, and damage identification. Finally, the CV-based vehicle parameter identification methods are introduced and their application for the identification of temporal and spatial parameters, weight parameters, and multi-parameters are summarized. This comprehensive literature review aims to provide guidance for selecting appropriate CV-based methods for bridge inspection and monitoring. Full article
(This article belongs to the Special Issue Recent Advances in Structural Health Monitoring and Damage Detection)
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