Structural health assessment of prefabricated concrete elements using Acoustic Emission: Towards an optimized damage sensing tool

Tsangouri, Eleni; Remy, Olivier; Boulpaep, F.; Verbruggen, Svetlana; Livitsanos, Georgios; Aggelis, Dimitrios G.

doi:10.1016/j.conbuildmat.2019.02.035

Cited by 43 publications

(18 citation statements)

References 22 publications

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“…Xu et al [83] applied two AE sensors together with multichannel monitoring and operating in order to study cracking in crumb rubber concrete, showing that AE can allow for a good understanding of micro-cracking and fracture monitoring. Prefabricated structural elements were assessed by [84] and AE features provided information for concrete cracking, bar debonding and bar plastic deformation. Laboratory-based bending tests of grouted connections for offshore wind turbines were carried out by Tziavos et al [85].…”

Section: Concrete Structuresmentioning

confidence: 99%

Recent Advancements in Non-Destructive Testing Techniques for Structural Health Monitoring

et al. 2021

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Structural health monitoring (SHM) is an important aspect of the assessment of various structures and infrastructure, which involves inspection, monitoring, and maintenance to support economics, quality of life and sustainability in civil engineering. Currently, research has been conducted in order to develop non-destructive techniques for SHM to extend the lifespan of monitored structures. This paper will review and summarize the recent advancements in non-destructive testing techniques, namely, sweep frequency approach, ground penetrating radar, infrared technique, fiber optics sensors, camera-based methods, laser scanner techniques, acoustic emission and ultrasonic techniques. Although some of the techniques are widely and successfully utilized in civil engineering, there are still challenges that researchers are addressing. One of the common challenges within the techniques is interpretation, analysis and automation of obtained data, which requires highly skilled and specialized experts. Therefore, researchers are investigating and applying artificial intelligence, namely machine learning algorithms to address the challenges. In addition, researchers have combined multiple techniques in order to improve accuracy and acquire additional parameters to enhance the measurement processes. This study mainly focuses on the scope and recent advancements of the Non-destructive Testing (NDT) application for SHM of concrete, masonry, timber and steel structures.

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Section: Concrete Structuresmentioning

confidence: 99%

Recent Advancements in Non-Destructive Testing Techniques for Structural Health Monitoring

et al. 2021

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“…Also, the crack initiation could also be detected using the same method. Tsangouri et al [15] suggested that it is not possible to characterize complex fractures in concrete beams by just observing the changes in mechanical properties, and therefore, AE was used to study the concrete degradation process. The damage-initiation process was determined based on the distribution of cumulative AE hits under increasing load.…”

Section: Related Workmentioning

confidence: 99%

“…NDT can reveal the internal status of the materials and provide real-time information on the condition of the structure. Of all these methods, recently, AE has been proven to be very effective for composite materials like concrete [10][11][12][13][14][15][16][17]. During the fracture process in concrete beams, materials release energy and generate mechanical waves that propagate through the solid.…”

Section: Introductionmentioning

confidence: 99%

“…The burst type AE signals are acquired from the brittle materials and they are associated with crack propagation characteristically. Consequently, many research works [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ] have adopted AE technique for evaluating degradation in concrete structures. These research articles provided effective approaches for crack/fracture diagnosis in concrete beams, but they failed to address certain key issues that are described as follows: The previous researches exploited numerous features such as rise time, decay time, amplitude, energy, ringing counts, RA and AF, etc.…”

Section: Introductionmentioning

confidence: 99%

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Performance Degradation Assessment of Concrete Beams Based on Acoustic Emission Burst Features and Mahalanobis—Taguchi System

Habib

Rai

Kim

2020

Sensors

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Acoustic emission (AE) has been used extensively for structural health monitoring based on the stress waves generated due to evolution of cracks in concrete structures. A major concern while using AE features is that each of them responds differently to the fractures in concrete structures. To tackle this problem, Mahalanobis—Taguchi system (MTS) is utilized, which fuses the AE feature space to provide comprehensive and reliable degradation indicator with a feature selection method to determine useful features. Further, majority of the existing investigations gave little attention to naturally occurring cracks, which are actually more difficult to detect. In this study, a novel degradation indicator (DI) based on AE features and MTS is proposed to indicate the performance degradation in reinforced concrete beams. The experimental results confirm that the MTS can successfully distinguish between healthy and faulty conditions. To alleviate the noise from the DI obtained through MTS, a noise-removal strategy based on Chebyshev inequality is suggested. The results show that the proposed DI based on AE features and MTS is capable of detecting early stage cracks as well as development of damage in concrete beams.

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“…To minimize the maintenance costs and to increase the lifetime of these structures, researchers are increasingly interested in improving current nondestructive evaluation (NDE) technologies or building advanced structural health monitoring (SHM) strategies [1,5]. Specifically, SHM schemes are generally applied to the localization and identification of metal plates deficiencies which are commonly fatigue cracks according to AE technology [2][3][4]. Therefore, the purpose of the paper is investigating an optimization and effective method for the localization of AE sources generated in common and machined metallic panels.…”

Section: Introductionmentioning

confidence: 99%

A Novel Acoustic Emission Sources Localization and Identification Method in Metallic Plates Based on Stacked Denoising Autoencoders

Yang

2020

IEEE Access

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Nowadays, deep learning could be an alternative approach to crack characterization. However, to the best of the authors' knowledge, little research exists on a deep learning-based characterization of fatigue-related AE sources occurring in plate-like structures. Consequently, this paper introduces a stacked denoising autoencoders (SDAE)-based framework to localize acoustic emission (AE) sources in common and complex metallic panels. The experimental specimen are respectively a Q235B steel plate and a 316L stainless steel containing a laser cladding layer. Specifically, SDAE is pre-trained and utilized to localize AE sources that are simulated by using the classical pencil lead break (PLB) approach. Meanwhile, the number of layers and hidden nodes of SDAE used for coordinate-based location is optimized according to a Bayesian Information Criteria (BIC) approach. To validate the proposed network and simplify the analysis, experiments are carried out on the surface of plate-like structures, which only one sensor is applied. After identifying AE sources that occur near laser cladding layers, the proposed approach classifies them into four source-to-laser cladding layer distance categories. Particularly, a tenfold cross-validation method is utilized to improve the accuracy of localization in this paper. Moreover, the effectiveness analysis to the number of sensors and comparison with conventional machine learning methods, including support vector machine (SVM) and artificial neural network (ANN), are also evaluated. In order to validate the performance of the proposed approach in terms of coordinate-based source localization. Ultimately, the results demonstrate that 100% accuracy for zonal localization, and the root mean squared (RMS) localization errors of two metallic panels are 38 mm (1.5") and 48 mm (1.9"), respectively. Additionally, in comparison with conventional machine learning approaches (i.e. SVM and ANN) which the RMS errors were 78 mm (2.5") and 67 mm (2.1"), respectively, the coordinates-based localization accuracy is significantly improved using the proposed approach. The results demonstrate the proposed approach is effective in AE-based structural health monitoring of plate-like structures with singlesensor. INDEX TERMS acoustic emission (AE); sources localization and identification; stacked denoising autoencoders (SDAE); metallic steel plates; structural health monitoring

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Structural health assessment of prefabricated concrete elements using Acoustic Emission: Towards an optimized damage sensing tool

Cited by 43 publications

References 22 publications

Recent Advancements in Non-Destructive Testing Techniques for Structural Health Monitoring

Recent Advancements in Non-Destructive Testing Techniques for Structural Health Monitoring

Performance Degradation Assessment of Concrete Beams Based on Acoustic Emission Burst Features and Mahalanobis—Taguchi System

A Novel Acoustic Emission Sources Localization and Identification Method in Metallic Plates Based on Stacked Denoising Autoencoders

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