Detection of subsurface voids in concrete-filled steel tubular (CFST) structure using percussion approach

Chen, Dongdong; Montano, Victor; Huo, Linsheng; Fan, Shuli; Song, Gangbing

doi:10.1016/j.conbuildmat.2020.119761

Cited by 52 publications

(16 citation statements)

References 53 publications

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“…Liqiong Zheng et al used Melfrequency cepstral coefficients (MFCCs) as the features of percussion-induced acoustics, and support vector machine (SVM)-based machine learning was utilized to classify results [26]. Dongdong Chen et al used power spectrum density (PSD) to process percussive sound, and a decision tree machine (DTM) learning approach was used to classify results [27].…”

Section: Introductionmentioning

confidence: 99%

Percussion-Based Pipeline Ponding Detection Using a Convolutional Neural Network

et al. 2022

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Pipeline transportation is the main method for long-distance gas transportation; however, ponding in the pipeline can affect transportation efficiency and even cause corrosion to the pipeline in some cases. A non-destructive method to detect pipeline ponding using percussion acoustic signals and a convolution neural network (CNN) is proposed in this paper. During the process of detection, a constant energy spring impact hammer is used to apply an impact on the pipeline, and the percussive acoustic signals are collected. A Mel spectrogram is used to extract the acoustic feature of the percussive acoustic signal with different ponding volumes in the pipeline. The Mel spectrogram is transferred to the input layer of the CNN and the convolutional kernel matrix of the CNN realizes the recognition of pipeline ponding volume. The recognition results show that the CNN can identify the amount of pipeline ponding with the percussive acoustic signals, which use the Mel spectrogram as the acoustic feature. Compared with the support vector machine (SVM) model and the decision tree model, the CNN model has better recognition performance. Therefore, the percussion-based pipeline ponding detection using the convolutional neural network method proposed in this paper has high application potential.

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Section: Introductionmentioning

confidence: 99%

Percussion-Based Pipeline Ponding Detection Using a Convolutional Neural Network

et al. 2022

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“…Zhu Ziqiang [44] studied the attenuation characteristics of ultrasonic waves in concrete. Chen Dongdong [45] studied the power spectrum characteristics of ultrasonic wave propagation in concrete. Lin Weizheng [46] studied the thickness of cement concrete with the ultrasonic detector.…”

Section: Introductionmentioning

confidence: 99%

Study on Ultrasonic Nondestructive Testing of Self-Compacting Concrete under Uniaxial Compression Test

Sun

Wang

2022

Materials

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To study the variation law of ultrasonic parameters of self-compacting concrete before and after damage under uniaxial compression test conditions, the C30 self-compacting concrete blocks stored for 7 days and 28 days were subjected to ultrasonic nondestructive testing, and the variation law of the sound time, amplitude, and sound velocity before and after the damage of self-compacting concrete blocks was emphatically analyzed. The concrete acoustic detection software was introduced to judge and analyze the abnormal values of the parameters of each measuring point, and the defect distribution map of each test block was obtained. The results showed that after curing the self-compacting concrete test block for 7 days and 28 days, the average value of sound time before and after the failure of each measuring point of the test block is small, and the average value of sound time before the failure is less than that after; the average amplitude after failure is smaller than that before failure, and the amplitude of some measuring points will be smaller than that before. The average sound velocity after failure is less than that before failure, and the internal defects appear and the structure is not dense. This study provides a theoretical basis for the application of ultrasonic detection technology in the field of self-compacting concrete and also provides a practical basis for the stability monitoring and failure warning of self-compacting concrete.

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“…e experiments indicated that when the thickness of the steel plate is greater than 6 mm or the sizes of the defects are small, the void defects cannot be detected using this method [26,27]. e impact acoustic method is widely studied and applied in the Japanese engineering field [28], but the method cannot distinguish between small gaps and void defects [29][30][31]. In actual engineering applications, the detection accuracy of the impact acoustic method is generally 60%-70%.…”

Section: Introductionmentioning

confidence: 99%

Application of Impact Imaging Method on Nondestructive Detection of Void Defects in Sandwich-Structured Immersed Tunnel

Liu

Zhang

et al. 2022

Mathematical Problems in Engineering

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Sandwich-structured immersed tunnel is an essential component in the field of cross-sea tunnel structural engineering which has a complicated internal structure. The void defects between the contacting surface of the steel and concrete reduce the carrying capacity of the structure. There are presently no effective, rapid, and accurate methods for detecting the casting quality of steel-shell concrete. Based on the propagation law of elastic stress waves in different media and the theory of a near-source wavefield, a prototype experiment on the nondestructive detection of void defects in sandwich-structured immersed tunnel was conducted. The experiment firstly determined the detection criterion for the impact imaging method through the features of the waveform data collected from 2 compartments (12 opening positions) of the full-scale model and then used the detection criterion to perform blind tests on the other 12 compartments of the full-scale model. The blind tests’ results of 72 opening positions showed that the prediction precision is 87.5% and thereby verify the applicability and accuracy of the detection criterion of the impact imaging method.

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Detection of subsurface voids in concrete-filled steel tubular (CFST) structure using percussion approach

Cited by 52 publications

References 53 publications

Percussion-Based Pipeline Ponding Detection Using a Convolutional Neural Network

Percussion-Based Pipeline Ponding Detection Using a Convolutional Neural Network

Study on Ultrasonic Nondestructive Testing of Self-Compacting Concrete under Uniaxial Compression Test

Application of Impact Imaging Method on Nondestructive Detection of Void Defects in Sandwich-Structured Immersed Tunnel

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