Application of synchronous detection for ultrasonic thickness gauging of concrete Articles with a nonuniform Structure

Kartashev, V. G.; Kachanov, V. K.; Соколов, И. В.; Shalimova, E. V.; Kontsov, R. V.

doi:10.1134/s1061830915080045

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…Simultaneously, Munir [ 18 ] applied a CNN to the classification of ultrasonic detection signals of metal welds and proved that the method is effective for defect recognition of ultrasonic signals. Compared with homogeneous materials, the ultrasonic detection signals of concrete contain more mixed and disturbing noise signals [ 19 ], such as reflected waves and transverse waves. In general, detection signals can be denoised before CNN performing classification and recognition computation.…”

Section: Introductionmentioning

confidence: 99%

A Wavelet Packet Transform and Convolutional Neural Network Method Based Ultrasonic Detection Signals Recognition of Concrete

Zhao

Zhang

2022

Sensors

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This paper proposes a new intelligent recognition method for concrete ultrasonic detection based on wavelet packet transform and a convolutional neural network (CNN). To validate the proposed data-based method, a case study is presented where the K-fold cross-validation was adopted to produce the performance analysis and classification experiments. Moreover, three evaluation indicators, precision, recall, and F-score, are calculated for analyzing the classification performance of the trained models. As a result, the obtained four-classifying CNN reaches more than 99% detection accuracy while the lowest recognition accuracy is not less than 92.5% on the testing dataset for the six-classifying CNN model. Compared with the existing stochastic configuration network (SCN) models, the presented method achieves the design objective with better recognition performance. The calculation results of the six-classifying and five-classifying models and related research clearly indicate the remaining challenging tasks for intelligent recognition algorithms in extracting features and classifying mass data from various concrete defects precisely and efficiently.

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Section: Introductionmentioning

confidence: 99%

A Wavelet Packet Transform and Convolutional Neural Network Method Based Ultrasonic Detection Signals Recognition of Concrete

Zhao

Zhang

2022

Sensors

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“…By establishing relation curve (strengthmeasuring curve) between certain physical quantity and strength of test specimen in advance, people can get the data of concrete strength and presume the structural strength. Due to numerous uncertain factors [3], the measured values (rebound value, ultrasonic value, etc) are distributed which brings low precision and large uncertainty and leads to difficulties in engineering application.…”

Section: Introductionmentioning

confidence: 99%

A Study on Testing Concrete Strength by Inversely Regressed Synthesis Ultrasonic-rebound Method

Cai¹,

Cen²,

Li³

et al. 2017

Proceedings of the 2017 6th International Conference on Measurement, Instrumentation and Automation (ICMIA 2017)

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“…These reflected signals form "structure noise" -stochastic nonstationary process. Metal structure condition is determined by characteristics of Structure noise in [3,4].…”

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confidence: 99%

Radiotechnical Signal Processing Techniques for Polymer Materials Structure Analysis

Kachanov¹,

Соколов²,

Lebedev³

et al. 2017

DDF

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Application of synchronous detection for ultrasonic thickness gauging of concrete Articles with a nonuniform Structure

Cited by 3 publications

References 7 publications

A Wavelet Packet Transform and Convolutional Neural Network Method Based Ultrasonic Detection Signals Recognition of Concrete

A Wavelet Packet Transform and Convolutional Neural Network Method Based Ultrasonic Detection Signals Recognition of Concrete

A Study on Testing Concrete Strength by Inversely Regressed Synthesis Ultrasonic-rebound Method

Radiotechnical Signal Processing Techniques for Polymer Materials Structure Analysis

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