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Advances in Acoustic Emission Testing and Evaluation of Materials and Structures

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Acoustics and Vibrations".

Deadline for manuscript submissions: closed (20 January 2023) | Viewed by 24666

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Dr. Hwa Kian Chai

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Institute for Infrastructure and Environment, The University of Edinburgh, Edinburgh EH9 3FG, UK
Interests: acoustic emission testing and evaluation; surface wave methods, Integrated elastic wave methodologies for structural assessment; database modelling and analysis for structural risk; low-carbon and smart concrete materials; repair and strengthening
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Special Issue Information

Dear Colleagues,

This Special Issue invites original articles that report and discuss the latest developments in the research and implementation of the acoustic emission technique (AET) in the condition assessment and health monitoring of materials and structures. The AET relies on the monitoring of transient elastic waves caused by the release of strain energy signatures from physical events such as fracture, cracking, and friction movements between interfaces in materials or structures. Analysis of the acquired wave data, depending on the nature of the methodical approach, serves a multitude of purposes ranging from damage detection and localization to fracture characterization and failure mode prediction.

Contemporary instrumentation and data analysis for AET have expanded its flexibility further in many aspects to accommodate even some of the most challenging in situ environments commonly found in the built environment and energy sectors, essentially including civil infrastructures such as bridges and dams, as well as assemblies for energy generation such as offshore platforms and wind as well as marine renewable energy structures.

Hence, contributions in the following topics are sought after for this Special Issue. The list is however not exhaustive and should cover other topics that offer insights in a broader perspective of the development and implementation of AET.

Damage detection and localization;
Case studies on in situ implementation;
Composite material characterization and fracture mechanics correlations;
Structural behavior and failure mode interpretation;
Integrated application with other assessment methods;
Use of machine learning/big data analytics in data analysis;
Numerical modeling and analytical study of AE occurrence.

Dr. Hwa Kian Chai
Guest Editor

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.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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

source localization
fracture characterization
machine learning
integrated assessment
numerical modeling
quantitative damage evaluation

Published Papers (11 papers)

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Research

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16 pages, 30118 KiB

Open AccessArticle

A Unified Mixed Deep Neural Network for Fatigue Damage Detection in Components with Different Stress Concentrations

by Susheel Dharmadhikari, Riddhiman Raut, Asok Ray and Amrita Basak

Appl. Sci. 2023, 13(3), 1542; https://doi.org/10.3390/app13031542 - 25 Jan 2023

Cited by 2 | Viewed by 1304

Abstract

The article presents a mixed deep neural network (DNN) approach for detecting micron-scale fatigue damage in high-strength polycrystalline aluminum alloys. Fatigue testing is conducted using a custom-designed apparatus integrated with a confocal microscope and a moving stage to accurately pinpoint the instance of micron-scale crack emergence. The specimens are monitored throughout the duration of the experiment using a pair of high-frequency ultrasonic transducers. The mixed DNN is trained with ultrasonic time-series data that are obtained from two sets of specimens categorized by different stress concentration factors. To understand the effects of mixing the data from both types of specimens, a parametric analysis is performed by varying the amount of training data from each specimen to develop a series of mixed DNNs. The mixed DNN, when tested on unseen data from both specimens, exhibits an accuracy of over 95%. This article, therefore, demonstrates a successful alternative to customized DNNs for new types, geometries, or stress concentration factors in the materials under consideration. Full article

(This article belongs to the Special Issue Advances in Acoustic Emission Testing and Evaluation of Materials and Structures)

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12 pages, 1547 KiB

Open AccessArticle

Correlation of Acoustic Emissions with Electrical Signals in the Vicinity of Fracture in Cement Mortars Subjected to Uniaxial Compressive Loading

by Andronikos Loukidis, Dimitrios Tzagkarakis, Antonios Kyriazopoulos, Ilias Stavrakas and Dimos Triantis

Appl. Sci. 2023, 13(1), 365; https://doi.org/10.3390/app13010365 - 27 Dec 2022

Cited by 7 | Viewed by 1416

Abstract

Acoustic emissions (AEs) and weak electrical signals, also known as pressure stimulated currents (PSCs), were concurrently recorded in order to investigate their behavior and detect precursory indicators when cement mortar specimens were subjected to mechanical compressive loading, emphasizing the behavior of the AEs and the PSC signal in the vicinity of fracture. The axial compressive loading protocol incorporated a constantly increasing stress, from early stress values up to the vicinity of fracture and a sequential stress stabilization until the time the specimen collapses, due to severe growing internal damages. Concurrent recordings of the electrical and acoustic emissions were performed. The AE recordings were analyzed, by incorporating the recently introduced F- and P-functions, and the well-known b-value. The experimental results highlight strong similarities regarding the variations of the PSC signal, the AE hits occurrence rate (F-function), and the AE hits energy release rate (P-function). The above was also confirmed with another similar experiment in an identical specimen. It is noteworthy that, during the stay of the specimens under a constant load regime near their strength levels, a peak appears in the above quantities, which is directly related to an increased rate of axial deformation. The temporal evolution of the b-values is also presented. Results show that the local minima appearing at values close to b ≈ 1.0 correspond to the local maxima of the PSC signal. It is straightforwardly concluded that when both the PSC signal and the AE data are combined, they provide clear pre-failure indicators. Full article

(This article belongs to the Special Issue Advances in Acoustic Emission Testing and Evaluation of Materials and Structures)

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13 pages, 4204 KiB

Open AccessArticle

Theoretical and Experimental Studies of Acoustic Reflection of Bubbly Liquid in Multilayer Media

by Yu Wang, Dehua Chen, Xueshen Cao and Xiao He

Appl. Sci. 2022, 12(23), 12264; https://doi.org/10.3390/app122312264 - 30 Nov 2022

Cited by 4 | Viewed by 1441

Abstract

Bubbly liquids are widely present in the natural environment and industrial fields, such as seawater near the ocean bottom, the multiphase flow in petroleum reservoirs, and the blood with bubbles resulting in decompression sickness. Therefore, accurate measurement of the gas content is of great significance for hydroacoustic physics, oil and gas resources exploration, and disease prevention and diagnosis. Trace bubbles in liquids can lead to considerable changes in the acoustic properties of gas–liquid two-phase media. Acoustic measurements can therefore be applied for trace bubble detection. This study derived the reflection coefficient of acoustic waves propagating in a sandwich layering model with liquid, bubbly liquid, and liquid. The influences of gas contents on the reflection coefficient at the layer interface were analyzed based on theoretical calculations. It was revealed that the magnitude of the reflection coefficient and the frequency interval between its valleys have a quantitative correlation with the gas contents. Thus, a novel means to detect the contents of trace bubbles was proposed by evaluating the reflection coefficients. The reflection features of a thin layer with bubbly liquid were then studied through experiments. It was validated by acoustical measurements and theories that the reflection coefficient is considerably sensitive to the change of gas contents as long as the gas content is tiny. With the increasing gas content, the maximum value of the reflection coefficient increases; meanwhile, the frequency intervals between the valleys become smaller. However, when the gas content is extensive enough, e.g., greater than 1%, the effect of the change of gas content on the reflection coefficient becomes inapparent. In that case, it is not easy to measure the gas content by the acoustic reflection signals with satisfying precision. This proposed method has potential applications for the detection of trace gas bubble content in several scenarios, e.g., decompression illness prevention and diagnosis. Full article

(This article belongs to the Special Issue Advances in Acoustic Emission Testing and Evaluation of Materials and Structures)

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20 pages, 6664 KiB

Open AccessArticle

A0 Lamb Mode Tracking to Monitor Crack Evolution in Thin Aluminum Plates Using Acoustic Emission Sensors

by El yamine Dris, Mourad Bentahar, Redouane Drai and Abderrahim El Mahi

Appl. Sci. 2022, 12(23), 12112; https://doi.org/10.3390/app122312112 - 26 Nov 2022

Cited by 3 | Viewed by 1361

Abstract

This paper presents a real time monitoring methodology to identify the location of acoustic emission (AE) sources generated by microcracks created within an aluminum plate when submitted to a tensile load. The real time detection of the AE hits was performed by means of a network of piezoelectric sensors distributed on the surface of the plate. The proposed localization approach is based on the combination of the time-frequency analysis of the detected AE hits with an extended Kalman filter (EKF). The spatial coordinates of the AE sources were determined by solving a set of nonlinear equations, where the extended Kalman filter is based on an iterative calculation. By considering the statistics related to the estimation of the coordinates’ errors, results show that the proposed method is in agreement with the experimental observations related to the propagation of the crack when the aluminum plate is under load. Full article

(This article belongs to the Special Issue Advances in Acoustic Emission Testing and Evaluation of Materials and Structures)

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15 pages, 5980 KiB

Open AccessArticle

Analysis of Acoustic Emission Energy from Reinforced Concrete Sewage Pipeline under Full-Scale Loading Test

by Pengpeng Li, Weidong Zhang, Zhoujing Ye, Yajian Wang, Songli Yang and Linbing Wang

Appl. Sci. 2022, 12(17), 8624; https://doi.org/10.3390/app12178624 - 28 Aug 2022

Cited by 7 | Viewed by 1674

Abstract

External load is one of the important reasons for structural damage and failure of reinforced concrete sewage pipelines, causing pipe leaks, pipe explosions, and even road collapses. In this paper, three-point loading experiments on full-size reinforced concrete pipes were carried out, and the damage state of the pipes was monitored by acoustic emission technology; the evolution trend of the mechanical properties and acoustic emission monitoring indexes under load was investigated. The experimental results showed that: (1) According to the change of acoustic emission energy and accumulated energy during loading, the mechanical response of the pipeline can be divided into an elastic compression phase, a plastic damage phase, and a residual strength phase; (2) The accumulated acoustic emission energy (∑E) and the maximum value of a single acoustic emission energy (E_max) can effectively characterize the different damage states of the loaded pipe; (3) A “double-peak” was observed in AF/RA data within the loading process. The appearance of the two peaks corresponds to the change of the loading phase of the pipeline and the occurrence of the major damage. Thus, the AF/RA index can effectively characterize the loading state and the damage degree of the pipeline. This study provides a valuable reference for pipeline health monitoring by using AE technology. Full article

(This article belongs to the Special Issue Advances in Acoustic Emission Testing and Evaluation of Materials and Structures)

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11 pages, 2253 KiB

Open AccessArticle

Improving Quality Control Methods to Test Strengthening Technologies: A Multilevel Model of Acoustic Pulse Flow

by Egor Grigorev and Viktor Nosov

Appl. Sci. 2022, 12(9), 4549; https://doi.org/10.3390/app12094549 - 30 Apr 2022

Cited by 9 | Viewed by 1590

Abstract

This article describes an approach that makes it possible to substantiate quality control criteria and methods to improve strengthening technologies. The approach was used to test the quality of products made using these technologies and analyze different strengthening methods applied to structural materials. In the experiment, samples of welded joints subjected to various types of strengthening were used that underwent acoustic emission (AE) testing. The results of quick evaluations produced by the proposed multilevel model of acoustic pulse flow were compared with the results of long-term cyclic tests to make a conclusion about the effectiveness of the approach being discussed. To improve strengthening quality control, a method is proposed that can be applied to complex and large-sized structures in the construction industry. Full article

(This article belongs to the Special Issue Advances in Acoustic Emission Testing and Evaluation of Materials and Structures)

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15 pages, 2546 KiB

Open AccessArticle

Numerical Voids Detection in Bonded Metal/Composite Assemblies Using Acousto-Ultrasonic Method

by Jialiang Guo, Aurélien Doitrand, Cheikh Sarr, Sylvain Chataigner, Laurent Gaillet and Nathalie Godin

Appl. Sci. 2022, 12(9), 4153; https://doi.org/10.3390/app12094153 - 20 Apr 2022

Cited by 2 | Viewed by 1672

Abstract

This research focuses on the application of an acousto-ultrasonics (AU) technique, a combination of ultrasonic characterization and acoustic emission, to nondestructively detect defects such as voids in bonded metal/composite assemblies. Computational methods are established to examine the effects of voids on the collected signal. The position of the receiver sensor with respect to the defect is also investigated. Given a specific structure and type of actuation signal, the sensor location and probability of detection can be enhanced by the model developed in this work. The defect detection is optimal provided the receiver sensor is located around the epicenter of the defect. Moreover, this work highlights the importance of the choice of reception sensor. Full article

(This article belongs to the Special Issue Advances in Acoustic Emission Testing and Evaluation of Materials and Structures)

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18 pages, 6609 KiB

Open AccessArticle

Analysis of Acoustic Emission Activity during Progressive Failure in Heterogeneous Materials: Experimental and Numerical Investigation

by Leandro Ferreira Friedrich, Boris Nahuel Rojo Tanzi, Angélica Bordin Colpo, Mario Sobczyk, Giuseppe Lacidogna, Gianni Niccolini and Ignacio Iturrioz

Appl. Sci. 2022, 12(8), 3918; https://doi.org/10.3390/app12083918 - 13 Apr 2022

Cited by 14 | Viewed by 1705

Abstract

This work focuses on an experimental and numerical investigation into monitoring damage in a cube-shaped concrete specimen under compression. Experimental monitoring uses acoustic emission (AE) signals acquired by two independent measurement apparatuses, and the same damage process is numerically simulated with the lattice discrete element method (LDEM). The results from the experiment and simulation are then compared in terms of their failure load, final configurations, and the evolution of global parameters based on AE signals, such as the b-value coefficient and the natural time approach. It is concluded that the results from the AE analysis present a significant sensitivity to the characteristics of the acquisition systems. However, natural time methods are more robust for determining such differences, indicating the same general tendency for all three data sets. Full article

(This article belongs to the Special Issue Advances in Acoustic Emission Testing and Evaluation of Materials and Structures)

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19 pages, 4188 KiB

Open AccessFeature PaperArticle

Acoustic Emission Analysis of Fracture and Size Effect in Cementitious Mortars

by Nuhamin Eshetu Deresse, Charlotte Van Steen, Mina Sarem, Stijn François and Els Verstrynge

Appl. Sci. 2022, 12(7), 3489; https://doi.org/10.3390/app12073489 - 30 Mar 2022

Cited by 6 | Viewed by 2116

Abstract

The size effect is a phenomenon where the strength and the ductility of a material depend on the size of the structure. Investigating size effects and related crack formation in brittle materials requires advanced monitoring methods. The aim of this research is to experimentally investigate the impact of size effect with the acoustic emission (AE) technique. Brazilian splitting tests with AE monitoring were performed on cement-based mortar cylinders of three sizes. It was found that in addition to the size, the boundary condition affects the final strength. When adopting similar boundary conditions in samples with different sizes, the larger samples had the lowest tensile splitting strength. For the larger samples, initially, there were fewer AE activities. However, there was a surge of high-amplitude AE events near the peak load. This indicates that as size increases, there is a lack of micro-cracking before macro-crack propagation, and the material fails in a more brittle manner. The width of the fracture process zone was quantified with AE and increased with sample size. A further analysis of the AE amplitude distribution demonstrated a change in the distribution in the pre-peak phase for the larger samples and for the smaller samples in the post-peak phase, signifying the brittle to ductile failure transition that occurs as size decreases. Full article

(This article belongs to the Special Issue Advances in Acoustic Emission Testing and Evaluation of Materials and Structures)

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14 pages, 5124 KiB

Open AccessArticle

Features of Acoustic Emission in Tensile Testing of Dissimilar Welded Joints of Pearlitic and Austenitic Steels

by Vera Barat, Artem Marchenkov, Vladimir Bardakov, Marina Karpova, Daria Zhgut and Sergey Elizarov

Appl. Sci. 2021, 11(24), 11892; https://doi.org/10.3390/app112411892 - 14 Dec 2021

Cited by 8 | Viewed by 2085

Abstract

This paper presents a study of acoustic emission (AE) during the deformation of dissimilar welded joints of austenitic steel to pearlitic steel. One of the specific problems in these welded joints is the presence of decarburized and carbide diffusion interlayers, which intensively increase in width during long-term high-temperature operation. The presence of wide interlayers negatively affects the mechanical properties of welded joints. Moreover, welded defects are difficult to diagnose in welded joints containing interlayers: due to the high structural heterogeneity, interlayers create structural noises that can hinder the detection of defects such as cracks, pores, or a lack of penetration. The AE method may become a complex decision for diagnosing dissimilar welded joints due to applicability to the testing of heterogenic materials with a complex microstructure. Specimens cut from dissimilar welded joints of austenitic steel to pearlitic steel were tested by tension to rupture, with parallel AE data registration. According to the research results, the characteristic features of the AE were revealed for specimens containing defects in the form of lack of penetration as well as for specimens with diffusion interlayers. The results obtained show that the AE method can be used to test both typical welding defects and diffusion interlayers in welded joints of steels of different structural classes. Full article

(This article belongs to the Special Issue Advances in Acoustic Emission Testing and Evaluation of Materials and Structures)

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Review

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30 pages, 2055 KiB

Open AccessFeature PaperReview

Machine-Learning-Based Methods for Acoustic Emission Testing: A Review

by Giuseppe Ciaburro and Gino Iannace

Appl. Sci. 2022, 12(20), 10476; https://doi.org/10.3390/app122010476 - 17 Oct 2022

Cited by 27 | Viewed by 6601

Abstract

Acoustic emission is a nondestructive control technique as it does not involve any input of energy into the materials. It is based on the acquisition of ultrasonic signals spontaneously emitted by a material under stress due to irreversible phenomena such as damage, microcracking, degradation, and corrosion. It is a dynamic and passive-receptive technique that analyzes the ultrasonic pulses emitted by a crack when it is generated. This technique allows for an early diagnosis of incipient structural damage by capturing the precursor signals of the fracture. Recently, the scientific community is making extensive use of methodologies based on machine learning: the use of machine learning makes a machine capable of receiving a series of data, modifying the algorithms as they receive information on what they are processing. In this way, the machine can learn without being explicitly programmed, and this implies a huge use of data and an efficient algorithm to adapt. This review described the methodologies for the implementation of the acoustic emission (AE) technique in the evaluation of the conditions and in the monitoring of materials and structures. The latest research products were also analyzed in the development of new methodologies based on machine learning for the detection and localization of damage for the characterization of the fracture and the prediction of the failure mode. The work carried out highlighted the strong use of these methods, which confirms the extreme usefulness of these techniques in identifying structural damage in scenarios heavily contaminated by residual noise. Full article

(This article belongs to the Special Issue Advances in Acoustic Emission Testing and Evaluation of Materials and Structures)

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