An Acoustic Emission Technique for Crack Modes Classification in Concrete Structures

Tra, Viet; Jeong, In-Kyu; Kim, Jong‐Myon

doi:10.3390/su12176724

Cited by 21 publications

(18 citation statements)

References 20 publications

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“…The main goal of our study is to prove our proposed method in three aspects. First, we would like to compare the proposed method with two recently proposed AE feature based typical ML algorithm-oriented crack detection methods [24], [62]. Second, we applied crack classification tasks with multiple variants of SqueezeNet along with the typical one.…”

Section: Resultsmentioning

confidence: 99%

“…The number of samples for each class: normal, micro-cracks, and macro-cracks were 300 in number. The crack detection method (k-NN+CFAR) proposed in [62] used 1,030 feature vectors representing different conditions of the RC beam. However, to have a balanced comparison we took 900 samples for this method too.…”

Section: A Comparison Of the Proposed Methods With The Typical Ae Feature Based ML Algorithmsmentioning

confidence: 99%

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A Lightweight Deep Learning-Based Approach for Concrete Crack Characterization Using Acoustic Emission Signals

2021

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This paper proposes an acoustic emission (AE) based automated crack characterization method for reinforced concrete (RC) beams using a memory efficient lightweight convolutional neural network named SqueezeNet. The proposed method also includes a signal-to-image technique, which is continuous wavelet transformation (CWT) that decomposes the AE signals over time-frequency scales and extracts the crack/fracture information in both the time and frequency domains. First, AE signals for two types of cracks (minor and severe), along with the normal condition (no crack), are collected from the experimental test bed. Second, the previously mentioned CWT based signal-to-image technique is applied to generate twodimensional time-frequency images that are then converted to gray scale images for faster computation. These images are supplied to the SqueezeNet for classification of the concrete crack types. We extensively modified the fire module of the SqueezeNet (SQN-MF) by introducing depth-wise convolutional kernels and channel shuffling operations. Not only does the proposed method utilize deep learning-based techniques for crack classification of concrete beams for the first time, but also the CWT-based imaging technique has not yet been explored in this field either. Additionally, this method does not follow the typical AE burst feature (features like AE counts, peak-amplitude, rise time, decay time, etc.) based methods, and as a result, we no longer require extensive human intervention and expertise to get deep understanding of the crack types. SQN-MF achieves AlexNet-level accuracy with fifty times fewer parameters and has an implementable memory size for the field programmable gate array boards. Overall, the method achieves 100% accuracy. It is 20.8% higher than the typical feature extraction and traditional machine learning based methods. We observed a 4% accuracy increase for the proposed SQN-MF compared to the typical SqueezeNet with bypass connections. INDEX TERMSConcrete crack characterization, continuous wavelet transformation, convolutional neural network, SqueezeNet. MD ARAFAT HABIB was born in Rangpur, Bangladesh. He received his B.Sc. degree from BRAC University, Bangladesh. After that he joined Chosun University in South Korea to pursue his master's degree in computer engineering. His program was funded in part by Chosun University and National Research Foundation of Korea. During his master's program he has received Chosun University excellent student award based on academic performance and Brain Korea Scholarship. He is currently working at the University of Ulsan, South Korea. His current interests include artificial intelligence, signal processing, fault diagnosis, and condition monitoring of industrial machinery. MD JUNAYED HASAN received a B.Sc. degree in Computer Engineering from

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Section: Resultsmentioning

confidence: 99%

Section: A Comparison Of the Proposed Methods With The Typical Ae Feature Based ML Algorithmsmentioning

confidence: 99%

A Lightweight Deep Learning-Based Approach for Concrete Crack Characterization Using Acoustic Emission Signals

2021

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“…Tra et al [34] detected impulses occurring in the AE signals using the constant-false-alarm-rate algorithm. They considered AE features like counts, duration, amplitude, risetime, energy, etc.…”

Section: Related Workmentioning

confidence: 99%

“…However, from Figure 15a, we can comprehend that the CAI-based classifier successfully distinguished between micro and macro-cracks with a very low error margin. We also compared our proposed method with a very recently published paper [34] that used the CFAR algorithm with k-NN. For comparison, we used two parameters: the average classification accuracy and F1-score.…”

Section: Classification Using K-nnmentioning

confidence: 99%

“…The proposed method outperforms the other two methods in both of the considered parameters with a good margin. Table 2 presents the performance comparison for the proposed method, the k-NN algorithm with the direct AE feature vectors, and the work presented in [34]. The proposed method outperforms the other two methods in both of the considered parameters with a good margin.…”

Section: Classification Using K-nnmentioning

confidence: 99%

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A Crack Characterization Method for Reinforced Concrete Beams Using an Acoustic Emission Technique

Habib

Kim

2020

Applied Sciences

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This study aims at characterizing crack types for reinforced concrete beams through the use of acoustic emission burst (AEB) features. The study includes developing a solid crack assessment indicator (CAI) accompanied by a crack detection method using the k-nearest neighbor (k-NN) algorithm that can successfully distinguish among the normal condition, micro-cracks, and macro-cracks (fractures) of concrete beam test specimens. Reinforced concrete (RC) beams undergo a three-point bending test, from which acoustic emission (AE) signals are recorded for further processing. From the recorded AE signals, crucial AEB features like the rise time, decay time, peak amplitude, AE energy, AE counts, etc. are extracted. The Boruta-Mahalanobis system (BMS) is utilized to fuse these features to provide us with a comprehensive and reliable CAI. The noise from the CAI is removed using the cumulative sum (CUMSUM) algorithm, and the final CAI plot is used to classify the three different conditions: normal, micro-cracks, and fractures using k-NN. The proposed method not only for the first time uses the entire time history to create a reliable CAI, but it can meticulously distinguish between micro-cracks and fractures, which previous works failed to deal with in a precise manner. Results obtained from the experiments display that the CAI built upon AEB features and BMS can detect cracks occurring in early stages, along with the gradually increasing damage in the beams. It also soundly outperforms the existing method by achieving an accuracy (classification) of 99.61%, which is 17.61% higher than the previously conducted research.

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Multiple Avalanche Processes in Acoustic Emission Spectroscopy: Multibranching of the Energy−Amplitude Scaling

Chen

Gou

Yuan

et al. 2021

Physica Status Solidi (b)

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Several physical processes can conspire to generate avalanches in materials. Such processes include avalanche mechanisms like dislocation movements, friction processes by pinning magnetic domain walls, moving dislocation tangles, hole collapse in porous materials, collisions of ferroelectric and ferroelastic domain boundaries, kinks in interfaces, and many more. Known methods to distinguish between these species which allow the physical identification of multiavalanche processes are reviewed. A new approach where the scaling relationship between the avalanche energies E and amplitudes A is considered is then described. Avalanches with single mechanisms scale experimentally as E = SiAi2. The energy E reflects the duration D of the avalanche and A(t), the temporal amplitude. The scaling prefactor S depends explicitly on the duration of the avalanche and on details of the avalanche profiles. It is reported that S is not a universal constant but assumes different values depending on the avalanche mechanism. If avalanches coincide, they can still show multivalued scaling between E and A with different S‐values for each branch. Examples for this multibranching effect in low‐Ni 316L stainless steel, 316L stainless steel, polycrystalline Ni, TC21 titanium alloy, and a Fe40Mn40Co10Cr10 high‐entropy alloy are shown.

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An Acoustic Emission Technique for Crack Modes Classification in Concrete Structures

Cited by 21 publications

References 20 publications

A Lightweight Deep Learning-Based Approach for Concrete Crack Characterization Using Acoustic Emission Signals

A Lightweight Deep Learning-Based Approach for Concrete Crack Characterization Using Acoustic Emission Signals

A Crack Characterization Method for Reinforced Concrete Beams Using an Acoustic Emission Technique

Multiple Avalanche Processes in Acoustic Emission Spectroscopy: Multibranching of the Energy−Amplitude Scaling

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