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Structural Health Monitoring: Latest Applications and Data Analysis

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 14481

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Prof. Dr. Grzegorz Psuj

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Center for Electromagnetic Fields Engineering and High-Frequency Techniques, Faculty of Electrical Engineering, West Pomeranian University of Technology in Szczecin, 70-313 Szczecin, Poland
Interests: electromagnetic non-destructive testing and evaluation; magnetic sensors; electromagnetic field measurements; multi-sensor measuring systems; sensors network; multi-source data mining and fusion; data processing
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Dr. Barbara Grochowalska (Szymanik)

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Center for Electromagnetic Fields Engineering and High-Frequency Techniques, Faculty of Electrical Engineering, West Pomeranian University of Technology in Szczecin, 70-313 Szczecin, Poland
Interests: active infrared thermography; microwave heating; numerical modelling; optimisation; neural networks; signal and image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is devoted to Structural Health Monitoring (SHM), more precisely to its most recent applications and obtained data analysis. Monitoring the state of engineering structures is unquestionably a timely topic, but it is also a very broad and interdisciplinary one. Both novel sensor systems and measurement, data collecting, and processing techniques are of relevance in this context. SHM techniques encompass all commonly used technical diagnostics and non-destructive testing methods, including electromagnetic, high-frequency, thermovision, radiography, ultrasound, and others. Each of the aforementioned methods presents unique issues, not just in terms of measurement methodology, but data processing as well. As a result, the processing of signals or images for the needs of qualitative and quantitative assessment of the structures under examination is also particularly intriguing in this context.

We invite scientists working in the broad field of technical diagnostics and nondestructive testing to submit original work on any of the above-mentioned themes. All types of research are welcome, including theoretical and experimental studies, as well as comprehensive reviews and surveys.

Prof. Dr. Grzegorz Psuj
Dr. Barbara Grochowalska (Szymanik)
Guest Editors

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Keywords

structural health monitoring
electromagnetic nondestructive evaluation
technical diagnostics
magnetic methods
thermovision
high-frequency electromagnetic methods
ultrasound methods
radiography
multi-sensor measuring systems
sensors network
multi-source data mining and fusion
data processing
image processing
numerical modelling
optimisation
neural networks
machine vision
machine learning

Related Special Issue

Structural Health Monitoring: Latest Applications and Data Analysis, 2nd Edition in Applied Sciences (3 articles)

Published Papers (11 papers)

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Editorial

Jump to: Research

4 pages, 183 KiB

Open AccessEditorial

Structural Health Monitoring: Latest Applications and Data Analysis

by Grzegorz Psuj and Barbara Szymanik

Appl. Sci. 2023, 13(13), 7617; https://doi.org/10.3390/app13137617 - 28 Jun 2023

Cited by 1 | Viewed by 835

Abstract

Structural health monitoring (SHM) is focused on the systematic monitoring of the state of structures to determine their safety, dependability, and durability [...] Full article

(This article belongs to the Special Issue Structural Health Monitoring: Latest Applications and Data Analysis)

Research

Jump to: Editorial

14 pages, 2452 KiB

Open AccessArticle

Verification of Non-Destructive Assessment of Moisture Content of Historical Brick Walls Using Random Forest Algorithm

by Anna Hoła

Appl. Sci. 2023, 13(10), 6006; https://doi.org/10.3390/app13106006 - 13 May 2023

Cited by 2 | Viewed by 760

Abstract

The paper presents the results of verification of the suitability of the random forest algorithm for the non-invasive assessment of excessively damp and salty historical brick walls. A new method of such quantitative assessment was developed and recently published by the author for the purpose of conducting research in buildings where destructive intervention is not possible due to conservation restrictions. However, before implementing the developed method into construction practice, it requires further validation. The conducted research showed that among all analyzed machine learning algorithms, the random forest algorithm is the most predisposed for the non-invasive evaluation of the U_mc mass moisture content of brick walls. Data sets from archival research and experimental tests conducted in two historical buildings were used to verify the usefulness of this algorithm. This usefulness was confirmed by the obtained satisfactory values of the linear correlation coefficient R, which amounted to 0.801 for the first building and 0.803 for the second one. Moreover, it was also proved by the obtained low values of medians of the absolute errors |Δf| equal to 1.79% and 1.46%, and also by the not too high (for an in situ study) medians of the relative errors |RE| equal to 16.70% and 13.75%. Full article

(This article belongs to the Special Issue Structural Health Monitoring: Latest Applications and Data Analysis)

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13 pages, 3328 KiB

Open AccessArticle

Acoustic Properties Comparison of Ti6Al4V Produced by Conventional Method and AM Technology in the Aspect of Ultrasonic Structural Health Monitoring of Adhesive Joints

by Jakub Kowalczyk, Dariusz Ulbrich, Michał Nowak, Kamil Sędłak, Konrad Gruber, Tomasz Kurzynowski and Marian Jósko

Appl. Sci. 2023, 13(1), 371; https://doi.org/10.3390/app13010371 - 28 Dec 2022

Cited by 3 | Viewed by 1086

Abstract

The article presents the results of ultrasonic testing of Ti6Al4V material produced by the conventional method and the laser bed fusion method. Modern manufacturing techniques, such as additive manufacturing, allow the production of parts with complex shapes. It is important to control the condition of such components throughout their lifetime. The purpose of this article was to determine the basic acoustic properties of Ti6Al4V material produced by two different methods—bar drawing and the additive manufacturing method. On this basis, an inspection scheme was developed for adhesive joints, the components of which are made by additive manufacturing technology. The decibel drops in the amplitudes of pulses reflected from the boundary of the adhesive-Ti6Al4V-AM and adhesive-Ti6Al4V joints were determined. The decibel drops for the connection of materials made with additive technology are higher than for the material made in a conventional way. The difference in decibel drop in the amplitudes of the additive manufactured material versus the drawn rod, depending on the ultrasonic head, can be up to 60%. The results of the study provide an important practical guideline for testing adhesive joints of parts made with additive manufacturing technology. Full article

(This article belongs to the Special Issue Structural Health Monitoring: Latest Applications and Data Analysis)

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17 pages, 3051 KiB

Open AccessArticle

Classification of Grain-Oriented Electrical Steel Sheets by Magnetic Barkhausen Noise Using Time-Frequency Analysis and Selected Machine Learning Algorithms

by Michal Maciusowicz and Grzegorz Psuj

Appl. Sci. 2022, 12(23), 12469; https://doi.org/10.3390/app122312469 - 06 Dec 2022

Cited by 1 | Viewed by 1328

Abstract

In this paper, a combination of Magnetic Barkhausen Noise (MBN) and several classical machine learning (ML) methods were used to evaluate both the grade and the magnetic directions of conventional and high grain oriented electrical sheets subjected to selected surface engineering methods. The presented analysis was conducted to compare the performance of two machine learning approaches, classical ML and deep learning (DL), in reference to the same MBN examination problem and based on the same database. Thus, during the experiment, 26 classical ML algorithms were used including decision trees, discriminant analysis, support vector machines, naïve Bayes, nearest neighbor, artificial neural networks and ensemble classifiers. The experiments were carried out considering a different number of recognized magnetic directions and hence the number of determined classes as well. The results of classification accuracy of the applied ML methods were compared with those obtained for the DL model presented in a previous paper. The highest accuracy was obtained for ML models based on artificial neural networks and ensemble bagged trees. However, the accuracy did not reach 89% in the best case—for the smallest number of determined classes. Nevertheless, the achieved results generally indicated an approx. 10 percent advantage of the deep learning model over the classical ones in terms of accuracy in each of the considered cases. Full article

(This article belongs to the Special Issue Structural Health Monitoring: Latest Applications and Data Analysis)

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22 pages, 27489 KiB

Open AccessArticle

A Spatial Model for Repairing of the Dam Safety Monitoring Data Combining the Variable Importance for Projection (VIP) and Cokriging Methods

by Shiwan Li, Yanling Li, Xiang Lu, Zhenyu Wu, Liang Pei and Kexin Liu

Appl. Sci. 2022, 12(23), 12296; https://doi.org/10.3390/app122312296 - 01 Dec 2022

Cited by 2 | Viewed by 1008

Abstract

The safe operation of dams is related to the lifeline of the national economy, the safety of the people, and social stability, and dam safety monitoring plays an essential role in scientifically controlling the safety of dams. Since the effects of environmental variables were not considered in conventional monitoring data repairing methods (such as the single time series model and spatial interpolation model), a spatial model for repairing monitoring data combining the variable importance for projection (VIP) method and cokriging was put forward in this paper. In order to improve the accuracy of the model, the influence of different combinations of covariates on it was discussed, and the VIP_j value greater than 0.8 was proposed as the threshold of covariates. The engineering verification shows that the VIP-cokriging spatial model had the advantages of high precision and strong applicability compared with the inverse distance weighting (IDW) model, the ordinary kriging model, and the universal kriging model, and the overall error can be reduced by more than 60%, which could better realize the expansion of the monitoring effect variable to the whole area of the dam space. The engineering application of the PBG dam showed that the model scientifically correlated the existing monitoring points with the spatial location of the dam, and reasonably repaired the measured values of the stopping and abnormal measured points, effectively ensuring that the spatial regular of the monitoring data could truly reflect the actual safety and operational status of the dam. Full article

(This article belongs to the Special Issue Structural Health Monitoring: Latest Applications and Data Analysis)

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19 pages, 7505 KiB

Open AccessArticle

Quality Tests of Hybrid Joint–Clinching and Adhesive—Case Study

by Jakub Kowalczyk, Waldemar Matysiak, Wojciech Sawczuk, Daniel Wieczorek, Kamil Sędłak and Michał Nowak

Appl. Sci. 2022, 12(22), 11782; https://doi.org/10.3390/app122211782 - 19 Nov 2022

Cited by 5 | Viewed by 1432

Abstract

Inseparable joints are widely used in machine and vehicle construction. Hybrid joints include bonding with sheet metal clinching. This combination reduces costs as well as the time of production compared to welded joints. Tests on the samples made of DC01 sheets were carried out. A case study was conducted on four research series. For each series, the shear forces of the joint were measured. The first series consisted of adhesive bonding, and the second and third series consisted of hybrid bonding, during which the sheet metal clinching joint was developed immediately after the completion of adhesive application and after full joint formation. The last test series only includes sheet metal clinching. In the series where bonding was used, the homogeneity of the prepared joints was analysed using the ultrasonic echo technique. The shear strength of the bonded joints was 476 N, whereas the shear strength of sheet metal clinching was 965 N. For the hybrid joint, the average forces were 1085 N (for the specimens in which the lap joint was made after the joint was fully cured) and 1486 N (for the specimens in which the lap joints were made immediately after the adhesive was applied). It was discovered that the clinching of the steel sheets significantly increases the strength of the joint. The stabilisation of the joint causes better crosslinking conditions. This results in an increase in the strength of the hybrid joint. Full article

(This article belongs to the Special Issue Structural Health Monitoring: Latest Applications and Data Analysis)

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14 pages, 4527 KiB

Open AccessArticle

Multi-Point Interaction of Partially Conductive Cracks with Sweep Frequency Eddy Currents in Electromagnetic Non-Destructive Evaluation

by Milan Smetana, Daniela Gombarska, Ladislav Janousek and Filip Vaverka

Appl. Sci. 2022, 12(22), 11451; https://doi.org/10.3390/app122211451 - 11 Nov 2022

Cited by 2 | Viewed by 831

Abstract

An investigation of real corrosion cracks that can be partially conductive in electromagnetic non-destructive evaluation using sweep-frequency eddy-current frequency-response analysis is carried out in this study. A new approach incorporating innovative solutions is proposed. The goal was to increase the probability of detection of real corrosion cracks in contrast to the conventional sweep-frequency method that is based on single-point signal detection. The proposed procedure was tested on real material specimens where differential responses were gained from real corrosion cracks. Seven austenitic steel plate specimens having corrosion cracks were inspected. Eddy-current responses due to the material cracks were sensed, while a multi-point approach was used for this purpose. The presented unique results clearly showed that the detection ability of a fixed probe driven with sweep-frequency excitation signal could be increased when the multi-point detection is used, and the gained signals are further mathematically processed. Full article

(This article belongs to the Special Issue Structural Health Monitoring: Latest Applications and Data Analysis)

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20 pages, 4874 KiB

Open AccessArticle

Cable Force Identification of Two Bending Cable Networks with Arbitrary Boundary Conditions

by Van-Son Nguyen, Chung-Yue Wang, Muhammad Ibnu Syamsi and Hao-Lin Wang

Appl. Sci. 2022, 12(21), 11012; https://doi.org/10.3390/app122111012 - 30 Oct 2022

Cited by 1 | Viewed by 1257

Abstract

The determination of existing cable forces is essential for evaluating the performance of bridges. The vibration technique is preferred among available practical methods because it is simple, fast, and economical. To apply this method, many researchers introduced analytical and empirical formulas considering bending rigidity, cable length, and boundary conditions. Nevertheless, most existing procedures to determine cable forces are for single cables, which are unsuitable for cable networks. To measure the cable forces of the cable networks, engineers should remove any cross-ties before conducting field tests. However, the removal of cross-ties separates the cable network into two independent cables, which does not reflect the actual behavior of the cable network. Hence, to save time and improve accuracy, this paper introduces a method to directly identify the cable tensions of a two-cable network without removing any cross-ties. This technique can precisely estimate the cable forces of the two-bending cable networks with arbitrary boundary conditions with an error of less than 1.0 percent. Full article

(This article belongs to the Special Issue Structural Health Monitoring: Latest Applications and Data Analysis)

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13 pages, 4661 KiB

Open AccessArticle

Detection of Concrete Cover of Reinforcements in Reinforced Concrete Wall by Microwave Thermography with Transmission Approach

by Sam Ang Keo, Franck Brachelet, Didier Defer and Florin Breaban

Appl. Sci. 2022, 12(19), 9865; https://doi.org/10.3390/app12199865 - 30 Sep 2022

Cited by 5 | Viewed by 1350

Abstract

Concrete cover has an important role in reinforced concrete (RC) structures because it protects reinforcement bars from the bad effects of weather, fire, and bad environmental conditions that cause the corrosion of the reinforcements. Although it is an essential parameter to be considered for structural health monitoring (SHM), its detection by infrared thermography, especially in the heating phase, has not been accessed yet. The detailed analysis and discussions of physical phenomena, known as diffraction and interference, affecting the thermograms during the detection of the steel bars by microwave thermography have given an essential key for resolving this issue. The present paper proposes an innovative methodology with microwave thermography for determining the concrete cover thickness of one-layer reinforcements (12 mm in diameter and regularly placed at 10 cm) in an RC wall (1 m × 1 m × 6.5 cm). By using the transmission approach with five angles of microwave antenna direction (0°, 15°, 30°, 45°, and 60°) and the Snell–Descartes law and linear law, the proposed methodology leads us to deduce the approximate value of the concrete cover thickness (37.74 mm), which is close to the real value (38 mm), as well as the spacing of the steel bars and dielectric constant of the concrete. The detection of the concrete cover thickness is another new remarkable achievement of infrared thermography methods. Full article

(This article belongs to the Special Issue Structural Health Monitoring: Latest Applications and Data Analysis)

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15 pages, 4921 KiB

Open AccessArticle

Thin Dielectric Layers Evaluation Using Tunable Split-Ring Resonator Based Metasurface in THz Frequency Range

by Paulina Gora and Przemyslaw Lopato

Appl. Sci. 2022, 12(17), 8526; https://doi.org/10.3390/app12178526 - 26 Aug 2022

Cited by 2 | Viewed by 1269

Abstract

The paper presents the evaluation of thin dielectric layers using a tunable split-ring resonator-based metasurface in the THz frequency range. Tunable unit cells of a metasurface allow its resonant frequency variation using some external excitation. This can be done in various ways. In this work, the behavior of such a metasurface is investigated by monitoring the resonant frequency value when the unit cell geometry is changed. Such behavior is utilized for the quality evaluation of a thin dielectric layer placed in vicinity of a metasurface. A change in dielectric permittivity noticeably affects the resonant frequency of a metasurface. In order to examine the state of the material under test, finite element method simulations were made for a 15 µm thin layer. As a result, the approximation-based relations between resonant frequencies (obtained for various geometries of structural element—in tunability range) and dielectric parameters of the examined material were derived. These relations carry more information than in the case of just one resonant frequency (the case of a non-tunable metasurface) and can be utilized for permittivity evaluation. Full article

(This article belongs to the Special Issue Structural Health Monitoring: Latest Applications and Data Analysis)

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16 pages, 5434 KiB

Open AccessArticle

Auto-Detection of Hidden Corrosion in an Aircraft Structure by Electromagnetic Testing: A Machine-Learning Approach

by Minhhuy Le, Van Su Luong, Dang Khoa Nguyen, Dang-Khanh Le and Jinyi Lee

Appl. Sci. 2022, 12(10), 5175; https://doi.org/10.3390/app12105175 - 20 May 2022

Cited by 11 | Viewed by 1946

Abstract

An aircraft is a multilayer structure that is assembled by rivets. Under extreme working conditions, corrosion appears and quickly propagates at the rivet sites of the layers; thus, it threads the integrity and safety of the aircraft. Corrosion usually occurs at the hidden layer around the rivet, making it difficult to detect. This paper proposes a machine learning approach incorporating an electromagnetic testing system to detect the hidden corrosion at the riveting site effectively. Several machine learning methods will be investigated for the detection of different sizes and locations of corrosion. The training strategy of the machine-learning models on the small numbers of data will also be investigated. The result shows that the proposed approach could effectively detect 89.48% of the hidden corrosion having from 2.8 to 195.4 mm³ with only 20% of training data and could be increased to 99.0% with 60–80% of the training data. Full article

(This article belongs to the Special Issue Structural Health Monitoring: Latest Applications and Data Analysis)

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