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Structural Health Monitoring and Non-destructive Testing for Engineering Applications: Advances in Sensor and Technologies

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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: closed (31 March 2022) | Viewed by 74436

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Special Issue Editors

Dr. Leandro Maio

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Guest Editor

Department of Materials, The University of Manchester, Manchester, UK
Interests: nondestructive evaluation; structural health monitoring; ultrasonic de-icing; signal processing; mechanical testing; low velocity impacts; adhesive bonding; stress analysis; fiber-reinforced composites; digital image correlation
Special Issues, Collections and Topics in MDPI journals

Dr. Vittorio Memmolo

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Guest Editor

Department of Industrial Engineering, University of Naples “Federico II”, Via Claudio 21, 80125 Naples, Italy
Interests: structural health monitoring; nondestructive testing; load monitoring; smart structures; ultrasounds and material testing
Special Issues, Collections and Topics in MDPI journals

Dr. Marco Laracca

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Guest Editor

Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, Via G. Di Biasio, 43-03043 Cassino, Italy
Interests: nondestructive testing; eddy current testing; ultrasounds; sensor realization; sensor characterization; power quality measurements; traceability in metrology; vehicle speed measurements
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Non-destructive testing (NDT) and structural health monitoring (SHM) are strategic procedures for the non-invasive assessment of the health state of materials and structures. Several NDT protocols guarantee that each engineering component enters in service after satisfying a series of stringent tests ensuring the maximum safety level for both persons and products. New studies in this field have a strategic and essential role in order to reduce the NDT costs and improve test performance. As a natural evolution of NDT procedures, structural health monitoring (SHM) has been developing fast, to reduce maintenance costs and limit design constraints. Continuous monitoring of both components and their assemblies ensures high levels of safety, reducing the number of inspections as well.

This Special Issue focuses on fostering technical improvements and new technologies developing for characterization and real-time monitoring of mechanical parts and, generally, structures in a variety of engineering fields (automotive, nuclear, petrochemical, archeology, cultural heritage, aerospace, and so on). We would like to invite original research articles as well as review articles that contain theoretical, analytical, and experimental investigations covering all aspects of NDT&E and SHM.

Dr. Leandro Maio
Dr. Vittorio Memmolo
Dr. Marco Laracca
Guest Editors

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Keywords

Sensors and transducers for NDT/SHM
Advanced techniques for NDT/SHM measurements
Smart sensors for NDT/SHM
Methods and systems for designing, optimizing, and characterizing NDT instruments and devices
Data processing and advanced techniques for NDT and SHM measurements
Reliability of NDT/SHM New SHM methodologies and damage assessment criteria
Methods and devices to optimize the performance of existing NDT techniques
Artificial intelligence in the data analysis and damage prediction applied to NDT/SHM

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Published Papers (13 papers)

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Research

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18 pages, 7652 KiB

Open AccessArticle

An Automated Image-Based Multivariant Concrete Defect Recognition Using a Convolutional Neural Network with an Integrated Pooling Module

by Bubryur Kim, Se-Woon Choi, Gang Hu, Dong-Eun Lee and Ronnie O. Serfa Juan

Sensors 2022, 22(9), 3118; https://doi.org/10.3390/s22093118 - 19 Apr 2022

Cited by 13 | Viewed by 3712

Abstract

Buildings and infrastructure in congested metropolitan areas are continuously deteriorating. Various structural flaws such as surface cracks, spalling, delamination, and other defects are found, and keep on progressing. Traditionally, the assessment and inspection is conducted by humans; however, due to human physiology, the assessment limits the accuracy of image evaluation, making it more subjective rather than objective. Thus, in this study, a multivariant defect recognition technique was developed to efficiently assess the various structural health issues of concrete. The image dataset used was comprised of 3650 different types of concrete defects, including surface cracks, delamination, spalling, and non-crack concretes. The proposed scheme of this paper is the development of an automated image-based concrete condition recognition technique to categorize, not only non-defective concrete into defective concrete, but also multivariant defects such as surface cracks, delamination, and spalling. The developed convolution-based model multivariant defect recognition neural network can recognize different types of defects on concretes. The trained model observed a 98.8% defect detection accuracy. In addition, the proposed system can promote the development of various defect detection and recognition methods, which can accelerate the evaluation of the conditions of existing structures. Full article

(This article belongs to the Special Issue Structural Health Monitoring and Non-destructive Testing for Engineering Applications: Advances in Sensor and Technologies)

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12 pages, 7443 KiB

Open AccessArticle

Mechanism of Magnetic Permeability Perturbation in Magnetizing-Based Eddy Current Nondestructive Testing

by Zhiyang Deng, Zhiheng Yu, Zhongyu Yuan, Xiaochun Song and Yihua Kang

Sensors 2022, 22(7), 2503; https://doi.org/10.3390/s22072503 - 25 Mar 2022

Cited by 6 | Viewed by 3160

Abstract

DC magnetization is generally considered to suppress the usual local magnetic permeability variation and increase the penetration depth for magnetizing-based eddy current testing (MB-ECT) of ferromagnetic materials. In fact, such simple explanations lead to rough nondestructive evaluation and cause new neglected non-uniform magnetic characteristics. Hence, the “perturbation” of the internal magnetic field variation is analyzed using a magnetic dipole model and the mechanism of magnetic permeability perturbation in MB-ECT is revealed. The theoretical analysis and simulations show that a significant permeability perturbation always appears around a defect and presents opposite features with strong and weak magnetization. Furthermore, experimental results indicate that the hidden signal component arising from the local permeability perturbation is critical for both far-side surface and near-side surface defects in the MB-ECT method. Full article

(This article belongs to the Special Issue Structural Health Monitoring and Non-destructive Testing for Engineering Applications: Advances in Sensor and Technologies)

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13 pages, 8787 KiB

Open AccessArticle

Impact Damage Detection Using Chirp Ultrasonic Guided Waves for Development of Health Monitoring System for CFRP Mobility Structures

by Langxing Tan, Osamu Saito, Fengming Yu, Yoji Okabe, Taku Kondoh, Shota Tezuka and Akihiro Chiba

Sensors 2022, 22(3), 789; https://doi.org/10.3390/s22030789 - 20 Jan 2022

Cited by 11 | Viewed by 2854

Abstract

When impact damage occurs in carbon fiber-reinforced plastic (CFRP) structures, it is barely visible but may cause significant degradation in the mechanical properties of the structure. Hence, a structural health monitoring (SHM) system that can be installed in CFRP mobility structures and is sensitive to impact damage is needed. In this study, we attempted to establish an SHM system based on ultrasonic guided waves, which are generated by inputting a broadband chirp signal into a film-like piezoelectric actuator. The relationship between impact damage size and maximum time-of-flight (ToF) delay was investigated for three types of CFRP plates: woven, non-woven, and hybrid laminates. As a result, it was found that the maximum ToF delay increased linearly with an increase in the damage size for all CFRP laminates. Moreover, the amplitude of the A₀ mode was found to be significantly affected by the damage length in the wave propagation direction. Thus, this SHM method using chirp ultrasonic waves can quantitatively evaluate the size and extent of the impact damage in CFRP laminates. Full article

(This article belongs to the Special Issue Structural Health Monitoring and Non-destructive Testing for Engineering Applications: Advances in Sensor and Technologies)

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20 pages, 2430 KiB

Open AccessArticle

Model of Magnetically Shielded Ferrite-Cored Eddy Current Sensor

by Darko Vasić, Ivan Rep, Dorijan Špikić and Matija Kekelj

Sensors 2022, 22(1), 326; https://doi.org/10.3390/s22010326 - 2 Jan 2022

Cited by 10 | Viewed by 3305

Abstract

Computationally fast electromagnetic models of eddy current sensors are required in model-based measurements, machine interpretation approaches or in the sensor design phase. If a sensor geometry allows it, the analytical approach to the modeling has significant advantages in comparison to numerical methods, most notably less demanding implementation and faster computation. In this paper, we studied an eddy current sensor consisting of a transmitter coil with a finitely long I ferrite core, which was screened with a finitely thick magnetic shield. The sensor was placed above a conductive and magnetic half-layer. We used vector magnetic potential formulation of the problem with a truncated region eigenfunction expansion, and obtained expressions for the transmitter coil impedance and magnetic potential in all subdomains. The modeling results are in excellent agreement with the results using the finite element method. The model was also compared with the impedance measurement in the frequency range from 5 kHz to 100 kHz and the agreement is within

3 %

for the resistance change due to the presence of the half-layer and

1 %

for the inductance change. The presented model can be used for measurement of properties of metallic objects, sensor lift-off or nonconductive coating thickness. Full article

(This article belongs to the Special Issue Structural Health Monitoring and Non-destructive Testing for Engineering Applications: Advances in Sensor and Technologies)

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15 pages, 3937 KiB

Open AccessArticle

Nondestructive Determination of Strength of Concrete Incorporating Industrial Wastes as Partial Replacement for Fine Aggregate

by Temple Chimuanya Odimegwu, A. B. M. Amrul Kaish, Ideris Zakaria, Manal Mohsen Abood, Maslina Jamil and Kayode-Ojo Ngozi

Sensors 2021, 21(24), 8256; https://doi.org/10.3390/s21248256 - 10 Dec 2021

Cited by 4 | Viewed by 2974

Abstract

Schmidt rebound hammer test was employed in this study as a nondestructive test. This test method has been universally utilized due to its non-destructiveness for quick and easy assessment of material strength properties and quality of concrete of an existing structure. Industrial waste materials (air-dried alum sludge, treated alum sludge, limestone dust and quarry dust) were employed as replacement material for fine aggregates in this study. A normal strength concrete was designed to achieve 35 MPa at 28 days, with industrial waste materials replacing fine aggregate at different percentages (0%, 5%, 10% and 15%), and then cured for 7, 28 and 180 days. The compressive strength values and rebound numbers for all the mixes obtained were correlated, and a regression equation was established between compressive strength and Schmidt rebound number. The correlation result showed an excellent relationship between rebound number and compressive strength of concrete produced in this study at all curing ages, with correlation coefficients of R² = 0.98, R² = 0.99 and R² = 0.98. The predicted equation showed a strong relationship with the experimental compressive strength. Therefore, it can be used for the prediction of compressive strength of concrete with industrial waste as a replacement for fine aggregate. Full article

(This article belongs to the Special Issue Structural Health Monitoring and Non-destructive Testing for Engineering Applications: Advances in Sensor and Technologies)

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27 pages, 15177 KiB

Open AccessArticle

Impact of Structural Health Monitoring on Aircraft Operating Costs by Multidisciplinary Analysis

by Vincenzo Cusati, Salvatore Corcione and Vittorio Memmolo

Sensors 2021, 21(20), 6938; https://doi.org/10.3390/s21206938 - 19 Oct 2021

Cited by 28 | Viewed by 24087

Abstract

Structural health monitoring is recognized as a viable solution to increase aviation safety and decrease operating costs enabling a novel maintenance approach based on the actual condition of the airframe, mitigating operating costs induced by scheduled inspections. However, the net benefit is hardly demonstrated, and it is still unclear how the implementation of such an autonomic system can affect performance at aircraft level. To close this gap, this paper presents a systematic analysis where the impact of cost and weight of integrating permanently attached sensors—used for diagnostics- affect the main performance of the aircraft. Through a multidisciplinary aircraft analysis framework, the increment of aircraft operating empty weight is compared with the possible benefits in terms of direct operating costs to identify a breakeven point. Furthermore, the analysis allows to establish a design guideline for structural health monitoring systems returning a safer aircraft without any economic penalties. The results show that the operating costs are lower than those of the reference aircraft up to 4% increase in maximum take-off weight. Paper findings suggest to considering a condition monitoring strategy from the conceptual design stage, since it could maximize the impact of such innovative technology. However, it involves in a design of a brand-new aircraft instead of a modification of an existing one. Full article

(This article belongs to the Special Issue Structural Health Monitoring and Non-destructive Testing for Engineering Applications: Advances in Sensor and Technologies)

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10 pages, 5586 KiB

Open AccessCommunication

Feasibility of Digital Image Correlation for Fatigue Cracks Detection under Dynamic Loading

by Vladimir V. Bardakov, Artem Yu. Marchenkov, Anton Yu. Poroykov, Alexander S. Machikhin, Milana O. Sharikova and Natalya V. Meleshko

Sensors 2021, 21(19), 6457; https://doi.org/10.3390/s21196457 - 27 Sep 2021

Cited by 7 | Viewed by 2424

Abstract

We address non-contact detection of defects in the railway rails under their dynamic loading and propose to combine digital image correlation (DIC) and finite element modeling (FEM). We show that accurate model of defect-free rail operating at the same loading conditions as the inspected one provides a reliable reference for experimental data. In this study, we tested the rail samples with artificial and fatigue defects under cyclic loading, calculated displacement and stress distributions at different locations of the cracks via DIC and validated the obtained results by FEM. The proposed DIC-FEM approach demonstrates high sensitivity to fatigue cracks and can be effectively used for remote control of rails as well as for non-destructive testing of various other objects operating under dynamic loads. Full article

(This article belongs to the Special Issue Structural Health Monitoring and Non-destructive Testing for Engineering Applications: Advances in Sensor and Technologies)

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16 pages, 3746 KiB

Open AccessArticle

Analysis of the Impact of Sustained Load and Temperature on the Performance of the Electromechanical Impedance Technique through Multilevel Machine Learning and FBG Sensors

by Ricardo Perera, Lluis Torres, Francisco J. Díaz, Cristina Barris and Marta Baena

Sensors 2021, 21(17), 5755; https://doi.org/10.3390/s21175755 - 26 Aug 2021

Cited by 12 | Viewed by 2403

Abstract

The electro-mechanical impedance (EMI) technique has been applied successfully to detect minor damage in engineering structures including reinforced concrete (RC). However, in the presence of temperature variations, it can cause false alarms in structural health monitoring (SHM) applications. This paper has developed an innovative approach that integrates the EMI methodology with multilevel hierarchical machine learning techniques and the use of fiber Bragg grating (FBG) temperature and strain sensors to evaluate the mechanical performance of RC beams strengthened with near surface mounted (NSM)-fiber reinforced polymer (FRP) under sustained load and varied temperatures. This problem is a real challenge since the bond behavior at the concrete–FRP interface plays a key role in the performance of this type of structure, and additionally, its failure occurs in a brittle and sudden way. The method was validated in a specimen tested over a period of 1.5 years under different conditions of sustained load and temperature. The analysis of the experimental results in an especially complex problem with the proposed approach demonstrated its effectiveness as an SHM method in a combined EMI–FBG framework. Full article

(This article belongs to the Special Issue Structural Health Monitoring and Non-destructive Testing for Engineering Applications: Advances in Sensor and Technologies)

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15 pages, 524 KiB

Open AccessFeature PaperArticle

The Concept of Accuracy Analysis of the Vertical Displacements Gained from the Hydrostatic Levelling Systems’ Measurements

by Waldemar Kamiński

Sensors 2021, 21(14), 4842; https://doi.org/10.3390/s21144842 - 15 Jul 2021

Cited by 5 | Viewed by 2163

Abstract

Nowadays, hydrostatic levelling is a widely used method for the vertical displacements’ determinations of objects such as bridges, viaducts, wharfs, tunnels, high buildings, historical buildings, special engineering objects (e.g., synchrotron), sports and entertainment halls. The measurements’ sensors implemented in the hydrostatic levelling systems (HLSs) consist of the reference sensor (RS) and sensors located on the controlled points (CPs). The reference sensor is the one that is placed at the point that (in theoretical assumptions) is not a subject to vertical displacements and the displacements of controlled points are determined according to its height. The hydrostatic levelling rule comes from the Bernoulli’s law. While using the Bernoulli’s principle in hydrostatic levelling, the following components have to be taken into account: atmospheric pressure, force of gravity, density of liquid used in sensors places at CPs. The parameters mentioned above are determined with some mean errors that influence on the accuracy assessment of vertical displacements. In the subject’s literature, there are some works describing the individual accuracy analyses of the components mentioned above. In this paper, the author proposes the concept of comprehensive determination of mean error of vertical displacement (of each CPs), calculated from the mean errors’ values of components dedicated for specific HLS. The formulas of covariances’ matrix were derived and they enable to make the accuracy assessment of the calculations’ results. The author also presented the subject of modelling of vertical displacements’ gained values. The dependences, enabling to conduct the statistic tests of received model’s parameters, were implemented. The conducted tests make it possible to verify the correctness of used theoretical models of the examined object treated as the rigid body. The practical analyses were conducted for two simulated variants of sensors’ connections in HLS. Variant no. I is the sensors’ serial connection. Variant no. II relies on the connection of each CPs with the reference sensor. The calculations’ results show that more detailed value estimations of the vertical displacements can be obtained using variant no. II. Full article

(This article belongs to the Special Issue Structural Health Monitoring and Non-destructive Testing for Engineering Applications: Advances in Sensor and Technologies)

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22 pages, 6190 KiB

Open AccessArticle

Novel Processing Algorithm to Improve Detectability of Disbonds in Adhesive Dissimilar Material Joints

by Damira Smagulova, Liudas Mazeika and Elena Jasiuniene

Sensors 2021, 21(9), 3048; https://doi.org/10.3390/s21093048 - 27 Apr 2021

Cited by 15 | Viewed by 2847

Abstract

Adhesively bonded dissimilar materials have attracted high interest in the aerospace and automotive industries due to their ability to provide superior structural characteristics and reduce the weight for energy savings. This work focuses on the improvement of disbond-type defect detectability using the immersion pulse-echo ultrasonic technique and an advanced post-processing algorithm. Despite the extensive work done for investigation, it is still challenging to locate such defects in dissimilar material joints due to the large differences in the properties of metals and composites as well as the multi-layered structure of the component. The objective of this work is to improve the detectability of defects in adhesively bonded aluminum and carbon fiber-reinforced plastic (CFRP) by the development of an advanced post-processing algorithm. It was determined that an analysis of multiple reflections has a high potential to improve detectability according to results received by inspection simulations and the evaluation of boundary characteristics. The impact of a highly influential parameter such as the sample curvature can be eliminated by the alignment of arrival time of signals reflected from the sample. The processing algorithm for the improvement of disbond detectability was developed based on time alignment followed by selection of the time intervals with a significant amplitude change of the signals reflected from defective and defect-free areas and shows significant improvement of disbond detectability. Full article

(This article belongs to the Special Issue Structural Health Monitoring and Non-destructive Testing for Engineering Applications: Advances in Sensor and Technologies)

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23 pages, 5831 KiB

Open AccessArticle

An Embedded-Sensor Approach for Concrete Resistivity Measurement in On-Site Corrosion Monitoring: Cell Constants Determination

by Jose Enrique Ramón, Isabel Martínez, José Manuel Gandía-Romero and Juan Soto

Sensors 2021, 21(7), 2481; https://doi.org/10.3390/s21072481 - 2 Apr 2021

Cited by 10 | Viewed by 3232

Abstract

The concrete electrical resistivity is a prominent parameter in structural health monitoring, since, along with corrosion potential, it provides relevant qualitative diagnosis of the reinforcement corrosion. This study proposes a simple expression to reliable determine resistivity from the concrete electrical resistance (R_E) provided by the corrosion sensor of the Integrated Network of Sensors for Smart Corrosion Monitoring (INESSCOM) we have developed. The novelty here is that distinct from common resistivity sensors, the cell constants obtained by the proposed expression are intended to be valid for any sensor implementation scenario. This was ensured by studying most significant geometrical features of the sensor in a wide set of calibration solutions. This embedded-sensor approach is intended to be applicable for R_E measurements obtained both using potential step voltammetry (PSV, used in the INESSCOM sensor for corrosion rate measurement) and alternating current methods. In this regard, we present a simple protocol to reliably determine R_E, and therefore resistivity, from PSV measurements. It consists in adding a very short potentiostatic pulse to the original technique. In this way, we are able to easy monitor resistivity along with corrosion rate through a single sensor, an advantage which is not usual in structural health monitoring. Full article

(This article belongs to the Special Issue Structural Health Monitoring and Non-destructive Testing for Engineering Applications: Advances in Sensor and Technologies)

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Review

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24 pages, 1775 KiB

Open AccessReview

Progress in Sensors for Monitoring Reinforcement Corrosion in Reinforced Concrete Structures—A Review

by Dmitry Shevtsov, Nhat Linh Cao, Van Chi Nguyen, Quoc Quang Nong, Hong Quan Le, Duc Anh Nguyen, Ilya Zartsyn and Oleg Kozaderov

Sensors 2022, 22(9), 3421; https://doi.org/10.3390/s22093421 - 29 Apr 2022

Cited by 19 | Viewed by 4683

Abstract

Non-destructive monitoring methods and continuous monitoring systems based on them are crucial elements of modern systems for the management and maintenance of assets which include reinforced concrete structures. The purpose of our study was to summarise the data on the most common sensors and systems for the non-destructive monitoring of reinforced concrete structures developed over the past 20 years. We considered systems based on electrochemical (potentiometry, methods related to polarisation) and physical (electromagnetic and ultrasonic waves, piezoelectric effect, thermography) examination methods. Special focus is devoted to the existing sensors and the results obtained using these sensors, as well as the advantages and disadvantages of their setups or other equipment used. The review considers earlier approaches and available commercial products, as well as relatively new sensors which are currently being tested. Full article

(This article belongs to the Special Issue Structural Health Monitoring and Non-destructive Testing for Engineering Applications: Advances in Sensor and Technologies)

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18 pages, 14817 KiB

Open AccessReview

Challenges in Bridge Health Monitoring: A Review

by Piervincenzo Rizzo and Alireza Enshaeian

Sensors 2021, 21(13), 4336; https://doi.org/10.3390/s21134336 - 24 Jun 2021

Cited by 105 | Viewed by 13786

Abstract

Bridge health monitoring is increasingly relevant for the maintenance of existing structures or new structures with innovative concepts that require validation of design predictions. In the United States there are more than 600,000 highway bridges. Nearly half of them (46.4%) are rated as fair while about 1 out of 13 (7.6%) is rated in poor condition. As such, the United States is one of those countries in which bridge health monitoring systems are installed in order to complement conventional periodic nondestructive inspections. This paper reviews the challenges associated with bridge health monitoring related to the detection of specific bridge characteristics that may be indicators of anomalous behavior. The methods used to detect loss of stiffness, time-dependent and temperature-dependent deformations, fatigue, corrosion, and scour are discussed. Owing to the extent of the existing scientific literature, this review focuses on systems installed in U.S. bridges over the last 20 years. These are all major factors that contribute to long-term degradation of bridges. Issues related to wireless sensor drifts are discussed as well. The scope of the paper is to help newcomers, practitioners, and researchers at navigating the many methodologies that have been proposed and developed in order to identify damage using data collected from sensors installed in real structures. Full article

(This article belongs to the Special Issue Structural Health Monitoring and Non-destructive Testing for Engineering Applications: Advances in Sensor and Technologies)

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