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Structural Health Monitoring (SHM) and Nondestructive Evaluation (NDE) for Infrastructure and Manufacturing

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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 (10 February 2024) | Viewed by 27346

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Special Issue Editors

Prof. Dr. Kanji Ono

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Guest Editor

Department of Materials Science and Engineering, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA
Interests: acoustic emission; nondestructive evaluation (NDE) of structural materials; structural health monitoring
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Didem Ozevin

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Guest Editor

Civil and Materials Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
Interests: acoustic emission; ultrasonics; MEMS sensors; damage detection; algorithm development; metamaterials
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue encompasses on structural health monitoring (SHM) and nondestructive evaluation (NDE) for infrastructure and manufacturing. It focuses on innovative approaches on sensing technologies as applications keep expanding for monitoring bridges, roads, tunnels, embankments, overpasses, railroads, as well as manufacturing processes, small and large. Topics can cover broad areas related to new sensor development, modeling of wireless networking, IoT integration, issues related to the sensor reliability on structural deployment, AI uses on discontinuity characterization as well as entirely new approaches that will advance SHM and NDE. The subject can range from widely used elastic wave propagation and acoustic emission phenomena to optical and electromagnetic sensing of strain, acceleration, temperature and other critical parameters. The wide landscape of SHM and NDE requires the increasing interaction of various branches of sensing professionals and it is hoped that this Special Issue inspires more cross-breeding for better solutions. Newer approaches are essential to attain breakthrough achievements. Uses of modeling tools and emerging artificial intelligence (AI) technologies are just the beginning of such attempts. The following topical areas are listed as examples, and other synergic efforts are most welcome:

Guided elastic wave methods for SHM;
AE applications to hard-to-access structures;
Structural evaluation under extreme environments;
Probability of detection in elastic wave methods;
NDE methods merging AI with various sensing strategies;
Internet of Things in SHM/NDE;
Sensor technology and wireless systems;
Sensor-fusion approaches;
Merging NDE with 3D printing technology;
Embedded sensing solutions with 3D printing technology;
Model-driven and data-driven SHM/NDE methods for advanced manufacturing.

Prof. Dr. Kanji Ono
Prof. Dr. Didem Ozevin
Guest Editors

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Published Papers (11 papers)

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Research

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20 pages, 11193 KiB

Open AccessArticle

Detection of a Submillimeter Notch-Type Defect at Multiple Orientations by a Lamb Wave A₀ Mode at 550 kHz for Long-Range Structural Health Monitoring Applications

by Lorenzo Capineri, Lorenzo Taddei and Eugenio Marino Merlo

Sensors 2024, 24(6), 1926; https://doi.org/10.3390/s24061926 - 17 Mar 2024

Viewed by 1116

Abstract

The early detection of small cracks in large metal structures is a crucial requirement for the implementation of a structural health monitoring (SHM) system with a low transducers density. This work tackles the challenging problem of the early detection of submillimeter notch-type defects with a semielliptical shape and a groove at a constant width of 100 µm and 3 mm depth in a 4.1 mm thick aluminum plate. This defect is investigated with an ultrasonic guided wave (UGW) A₀ mode at 550 kHz to investigate the long range in thick metal plates. The mode selection is obtained by interdigital transducers (IDTs) designed to operate with a 5 mm central wavelength. The novel contribution is the validation of the detection by pulse-echo and pitch and catch with UGW transducers to cover a distance up to 70 cm to reduce the transducers density. The analysis of scattering from this submillimeter defect at different orientations is carried out using simulations with a Finite Element Model (FEM). The detection of the defect is obtained by comparing the scattered signals from the defect with baseline signals of the pristine laminate. Finally, the paper shows that the simulated results are in good agreement with the experimental ones, demonstrating the possible implementation in an SHM system based on the efficient propagation of an antisymmetric mode by IDTs. Full article

(This article belongs to the Special Issue Structural Health Monitoring (SHM) and Nondestructive Evaluation (NDE) for Infrastructure and Manufacturing)

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13 pages, 4989 KiB

Open AccessArticle

Void-Engineered Metamaterial Delay Line with Built-In Impedance Matching for Ultrasonic Applications

by Rajendra P. Palanisamy, Luis A. Chavez, Raymond Castro and Alp T. Findikoglu

Sensors 2024, 24(3), 995; https://doi.org/10.3390/s24030995 - 3 Feb 2024

Viewed by 1216

Abstract

Metamaterials exhibit unique ultrasonic properties that are not always achievable with traditional materials. However, the structures and geometries needed to achieve such properties are often complex and difficult to obtain using common fabrication techniques. In the present research work, we report a novel metamaterial acoustic delay line with built-in impedance matching that is fabricated using a common 3D printer. Delay lines are commonly used in ultrasonic inspection when signals need to be separated in time for improved sensitivity. However, if the impedance of the delay line is not perfectly matched with those of both the sensor and the target medium, a strong standing wave develops in the delay line, leading to a lower energy transmission. The presented metamaterial delay line was designed to match the acoustic impedance at both the sensor and target medium interfaces. This was achieved by introducing graded engineered voids with different densities at both ends of the delay line. The measured impedances of the designed metamaterial samples show a good match with the theoretical predictions. The experimental test results with concrete samples show that the acoustic energy transmission is increased by 120% and the standing wave in the delay line is reduced by over a factor of 2 compared to a commercial delay line. Full article

(This article belongs to the Special Issue Structural Health Monitoring (SHM) and Nondestructive Evaluation (NDE) for Infrastructure and Manufacturing)

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30 pages, 10264 KiB

Open AccessArticle

Vibration-Based SHM in the Synthetic Mooring Lines of the Semisubmersible OO-Star Wind Floater under Varying Environmental and Operational Conditions

by Nikolas P. Anastasiadis, Christos S. Sakaris, Rune Schlanbusch and John S. Sakellariou

Sensors 2024, 24(2), 543; https://doi.org/10.3390/s24020543 - 15 Jan 2024

Cited by 2 | Viewed by 1816

Abstract

As the industry transitions toward Floating Offshore Wind Turbines (FOWT) in greater depths, conventional chain mooring lines become impractical, prompting the adoption of synthetic fiber ropes. Despite their advantages, these mooring lines present challenges in inspection due to their exterior jacket, which prevents visual assessment. The current study focuses on vibration-based Structural Health Monitoring (SHM) in FOWT synthetic mooring lines under uncertainty arising from varying Environmental and Operational Conditions (EOCs). Six damage detection methods are assessed, utilizing either multiple models or a single functional model. The methods are based on Vector Autoregressive (VAR) or Transmittance Function Autoregressive with exogenous input (TF-ARX) models. All methods are evaluated through a Monte Carlo study involving 1100 simulations, utilizing acceleration signals generated from a finite element model of the OO-Star Wind Floater Semi 10 MW wind turbine. With signals from only two measuring positions, the methods demonstrate excellent results, detecting the stiffness reduction of a mooring line at levels 10% through 50%. The methods are also tested for healthy cases, with those utilizing TF-ARX models achieving zero false alarms, even for EOCs not encountered in the training data. Full article

(This article belongs to the Special Issue Structural Health Monitoring (SHM) and Nondestructive Evaluation (NDE) for Infrastructure and Manufacturing)

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18 pages, 3724 KiB

Open AccessArticle

Elastic Waves Excitation and Focusing by a Piezoelectric Transducer with Intermediate Layered Elastic Metamaterials with and without Periodic Arrays of Interfacial Voids

by Mikhail V. Golub, Sergey I. Fomenko, Pavel E. Usov and Artem A. Eremin

Sensors 2023, 23(24), 9747; https://doi.org/10.3390/s23249747 - 11 Dec 2023

Cited by 2 | Viewed by 1077

Abstract

Optimization of the structure of piezoelectric transducers such as the proper design of matching layers can increase maximum wave energy transmission to the host structure and transducer sensitivity. A novel configuration of an ultrasonic transducer, where elastic metamaterial insertion is introduced to provide bulk wave mode conversion and to increase wave energy transfer into a substrate, is proposed. Configurations of layered elastic metamaterials with crack-like voids are examined theoretically since they can provide wide band gaps and strong wave localization and trapping. The analysis shows that the proposed metamaterial-based matching layers can sufficiently change wave energy transmission from a piezoelectric active element for various frequency ranges (relatively low frequencies as well as higher ones). The proposed configuration can also be useful for advanced sensing with higher sensitivity in certain frequency ranges or for demultiplexing different kinds of elastic waves. Full article

(This article belongs to the Special Issue Structural Health Monitoring (SHM) and Nondestructive Evaluation (NDE) for Infrastructure and Manufacturing)

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16 pages, 4113 KiB

Open AccessArticle

A Technique for Centrifugal Pump Fault Detection and Identification Based on a Novel Fault-Specific Mann–Whitney Test

by Zahoor Ahmad, Jae-Young Kim and Jong-Myon Kim

Sensors 2023, 23(22), 9090; https://doi.org/10.3390/s23229090 - 10 Nov 2023

Viewed by 1681

Abstract

This work presents a technique for fault detection and identification in centrifugal pumps (CPs) using a novel fault-specific Mann–Whitney test (FSU Test) and K-nearest neighbor (KNN) classification algorithm. Traditional fault indicators, such as the mean, peak, root mean square, and impulse factor, lack sensitivity in detecting incipient faults. Furthermore, for defect identification, supervised models rely on pre-existing knowledge about pump defects for training purposes. To address these concerns, a new centrifugal pump fault indicator (CPFI) that does not rely on previous knowledge is developed based on a novel fault-specific Mann–Whitney test. The new fault indicator is obtained by decomposing the vibration signature (VS) of the centrifugal pump hierarchically into its respective time-frequency representation using the wavelet packet transform (WPT) in the first step. The node containing the fault-specific frequency band is selected, and the Mann–Whitney test statistic is calculated from it. The combination of hierarchical decomposition of the vibration signal for fault-specific frequency band selection and the Mann–Whitney test form the new fault-specific Mann–Whitney test. The test output statistic yields the centrifugal pump fault indicator, which shows sensitivity toward the health condition of the centrifugal pump. This indicator changes according to the working conditions of the centrifugal pump. To further enhance fault detection, a new effect ratio (ER) is introduced. The KNN algorithm is employed to classify the fault type, resulting in promising improvements in fault classification accuracy, particularly under variable operating conditions. Full article

(This article belongs to the Special Issue Structural Health Monitoring (SHM) and Nondestructive Evaluation (NDE) for Infrastructure and Manufacturing)

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17 pages, 4484 KiB

Open AccessArticle

Multi-Task Learning of Scanning Electron Microscopy and Synthetic Thermal Tomography Images for Detection of Defects in Additively Manufactured Metals

by Sarah Scott, Wei-Ying Chen and Alexander Heifetz

Sensors 2023, 23(20), 8462; https://doi.org/10.3390/s23208462 - 14 Oct 2023

Cited by 5 | Viewed by 1806

Abstract

One of the key challenges in laser powder bed fusion (LPBF) additive manufacturing of metals is the appearance of microscopic pores in 3D-printed metallic structures. Quality control in LPBF can be accomplished with non-destructive imaging of the actual 3D-printed structures. Thermal tomography (TT) is a promising non-contact, non-destructive imaging method, which allows for the visualization of subsurface defects in arbitrary-sized metallic structures. However, because imaging is based on heat diffusion, TT images suffer from blurring, which increases with depth. We have been investigating the enhancement of TT imaging capability using machine learning. In this work, we introduce a novel multi-task learning (MTL) approach, which simultaneously performs the classification of synthetic TT images, and segmentation of experimental scanning electron microscopy (SEM) images. Synthetic TT images are obtained from computer simulations of metallic structures with subsurface elliptical-shaped defects, while experimental SEM images are obtained from imaging of LPBF-printed stainless-steel coupons. MTL network is implemented as a shared U-net encoder between the classification and the segmentation tasks. Results of this study show that the MTL network performs better in both the classification of synthetic TT images and the segmentation of SEM images tasks, as compared to the conventional approach when the individual tasks are performed independently of each other. Full article

(This article belongs to the Special Issue Structural Health Monitoring (SHM) and Nondestructive Evaluation (NDE) for Infrastructure and Manufacturing)

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21 pages, 7079 KiB

Open AccessArticle

Experimental Study on Monitoring Damage Progression of Basalt-FRP Reinforced Concrete Slabs Using Acoustic Emission and Machine Learning

by Tonghao Zhang, Mohammad Mahdi, Mohsen Issa, Chenxi Xu and Didem Ozevin

Sensors 2023, 23(20), 8356; https://doi.org/10.3390/s23208356 - 10 Oct 2023

Cited by 12 | Viewed by 1479

Abstract

Basalt fiber-reinforced polymer (BFRP) reinforced concrete is a new alternative to conventional steel-reinforced concrete due to its high tensile strength and corrosion resistance characteristics. However, as BFRP is a brittle material, unexpected failure of concrete structures reinforced with BFRP may occur. In this study, the damage initiation and progression of BFRP-reinforced concrete slabs were monitored using the acoustic emission (AE) method as a structural health monitoring (SHM) solution. Two simply supported slabs were instrumented with an array of AE sensors in addition to a high-resolution camera, strain, and displacement sensors and then loaded until failure. The dominant damage mechanism was concrete cracking due to the over-reinforced design and adequate BFRP bar-concrete bonding. The AE method was evaluated in terms of identifying the damage initiation, progression from tensile to shear cracks, and the evolution of crack width. Unsupervised machine learning was applied to the AE data obtained from the first slab testing to develop the clusters of the damage mechanisms. The cluster results were validated using the k-means supervised learning model applied to the data obtained from the second slab. The accuracy of the K-NN model trained on the first slab was 99.2% in predicting three clusters (tensile crack, shear crack, and noise). Due to the limitation of a single indicator to characterize complex damage properties, a Statistical SHapley Additive exPlanation (SHAP) analysis was conducted to quantify the contribution of each AE feature to crack width. Based on the SHAP analysis, the AE duration had the highest correlation with the crack width. The cumulative duration of the AE sensor near the crack had close to 100% accuracy to track the crack width. It was concluded that the AE sensors positioned at the mid-span of slabs can be used as an effective SHM solution to monitor the initiation of tensile cracks, sudden changes in structural response due to major damage, damage evolution from tensile to shear cracks, and the progression of crack width. Full article

(This article belongs to the Special Issue Structural Health Monitoring (SHM) and Nondestructive Evaluation (NDE) for Infrastructure and Manufacturing)

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27 pages, 8049 KiB

Open AccessArticle

Critical Examination of Distance-Gain-Size (DGS) Diagrams of Ultrasonic NDE with Sound Field Calculations

by Kanji Ono and Hang Su

Sensors 2023, 23(15), 7004; https://doi.org/10.3390/s23157004 - 7 Aug 2023

Viewed by 1373

Abstract

Ultrasonic non-destructive evaluation, which has been used widely, can detect and size critical flaws in structures. Advances in sound field calculations can further improve its effectiveness. Two calculation methods were used to characterize the relevant sound fields of an ultrasonic transducer and the results were applied to construct and evaluate Distance-Gain-Size (DGS) diagrams, which are useful in flaw sizing. Two published DGS diagrams were found to be deficient because the backward diffraction path was overly simplified and the third one included an arbitrary procedure. Newly constructed DGS diagrams exhibited transducer size dependence, revealing another deficiency in the existing DGS diagrams. However, the extent of the present calculations must be expanded to provide a catalog of DGS diagrams to cover a wide range of practical needs. Details of the new construction method are presented, incorporating two-way diffraction procedures. Full article

(This article belongs to the Special Issue Structural Health Monitoring (SHM) and Nondestructive Evaluation (NDE) for Infrastructure and Manufacturing)

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18 pages, 9368 KiB

Open AccessArticle

Characterization of Damage Progress in the Defective Grouted Sleeve Connection Using Combined Acoustic Emission and Ultrasonics

by Lu Zhang, Zhenmin Fang, Yongze Tang, Hongyu Li and Qizhou Liu

Sensors 2022, 22(21), 8579; https://doi.org/10.3390/s22218579 - 7 Nov 2022

Cited by 4 | Viewed by 2009

Abstract

The grouted sleeve connection is one of the most widely used connections for prefabricated buildings (PBs). Usually, its quality can have a significant impact on the safety of the whole PB, especially for the internal flaws that form during sleeve grouting. It is directly related to the mechanical performance and failure behavior of the grouted sleeve. Therefore, it is essential to understand the damage progression of the defective grouted sleeve connection. However, destructive testing is the mainstream measure to evaluate the grout sleeves, which is not applicable for in situ inspection. Therefore, this paper proposes a combined acoustic emission (AE) and ultrasonic testing (UT) method to characterize the damage progress of a grouted sleeve with different degrees of internal flaws under tensile loading. The UT was conducted before loading to evaluate the internal flaws. Additionally, the AE was used as the processing monitoring technique during the tensile testing. Two damage modes were identified: (i) brittle mode associated with the rebar pullout; (ii) ductile mode associated with the rapture of the rebar. The UT energy ratio was selected as the most sensitive feature to the internal flaws, both numerically and experimentally. The AE signatures of different damage phases and different damage modes were determined and characterized. For the brittle and ductile damage modes, two and three phases appeared in the AE activities, respectively. The proposed combined AE and UT method can provide a reliable and convenient nondestructive evaluation of grouted sleeves with internal flaws. Moreover, it can also characterize the damage progress of the grouted sleeve connections in real-time. Full article

(This article belongs to the Special Issue Structural Health Monitoring (SHM) and Nondestructive Evaluation (NDE) for Infrastructure and Manufacturing)

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Review

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33 pages, 9392 KiB

Open AccessReview

Embedded Sensors with 3D Printing Technology: Review

by Joan Bas, Taposhree Dutta, Ignacio Llamas Garro, Jesús Salvador Velázquez-González, Rakesh Dubey and Satyendra K. Mishra

Sensors 2024, 24(6), 1955; https://doi.org/10.3390/s24061955 - 19 Mar 2024

Cited by 9 | Viewed by 4998

Abstract

Embedded sensors (ESs) are used in smart materials to enable continuous and permanent measurements of their structural integrity, while sensing technology involves developing sensors, sensory systems, or smart materials that monitor a wide range of properties of materials. Incorporating 3D-printed sensors into hosting structures has grown in popularity because of improved assembly processes, reduced system complexity, and lower fabrication costs. 3D-printed sensors can be embedded into structures and attached to surfaces through two methods: attaching to surfaces or embedding in 3D-printed sensors. We discussed various additive manufacturing techniques for fabricating sensors in this review. We also discussed the many strategies for manufacturing sensors using additive manufacturing, as well as how sensors are integrated into the manufacturing process. The review also explained the fundamental mechanisms used in sensors and their applications. The study demonstrated that embedded 3D printing sensors facilitate the development of additive sensor materials for smart goods and the Internet of Things. Full article

(This article belongs to the Special Issue Structural Health Monitoring (SHM) and Nondestructive Evaluation (NDE) for Infrastructure and Manufacturing)

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26 pages, 2655 KiB

Open AccessReview

Prediction Models for Railway Track Geometry Degradation Using Machine Learning Methods: A Review

by Yingying Liao, Lei Han, Haoyu Wang and Hougui Zhang

Sensors 2022, 22(19), 7275; https://doi.org/10.3390/s22197275 - 26 Sep 2022

Cited by 17 | Viewed by 6983

Abstract

Keeping railway tracks in good operational condition is one of the most important tasks for railway owners. As a result, railway companies have to conduct track inspections periodically, which is costly and time-consuming. Due to the rapid development in computer science, many prediction models using machine learning methods have been developed. It is possible to discover the degradation pattern and develop accurate prediction models. The paper reviews the existing prediction methods for railway track degradation, including traditional methods and prediction methods based on machine learning methods, including probabilistic methods, Artificial Neural Network (ANN), Support Vector Machine (SVM), and Grey Model (GM). The advantages, shortage, and applicability of methods are discussed, and recommendations for further research are provided. Full article

(This article belongs to the Special Issue Structural Health Monitoring (SHM) and Nondestructive Evaluation (NDE) for Infrastructure and Manufacturing)

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