Rapid damage assessment of concrete bridge deck leveraging an automated double-sided bounce system

Kang, Sanggoo; Wu, Yajuan; David, Dafnik Sarilkuma; Ham, Suyun

doi:10.1016/j.autcon.2022.104244

Cited by 9 publications

(2 citation statements)

References 27 publications

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“…Its general application refers to poor coupling conditions of the contact sensor [2]. Recently, an air-coupled impact-echo method was developed to address this issue [3,4]. In this approach, a contactless sensor detected leaky waves that propagate through the air surrounding a concrete structure.…”

Section: Introductionmentioning

confidence: 99%

Boundary effect of foamed cement paste under the influence of air-coupled impact-echo

Wan,

Zhang,

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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The air-coupled impact-echo method has been increasingly utilized for the detection of cement-based materials. In this study, an air-coupled impact-echo system is developed. This system evaluates the response of cast-in-place foamed cement paste blocks subjected to air-coupled impact-echo. The shape factor is calculated based on the acquired impact-echo spectrograms to analyze the boundary effects of the blocks. The results show that the values of the shape factor vary with the impact location, as the impact location from the edges of the blocks affects the air-coupled impact-echo response. Accordingly, a dimensionless factor is proposed to evaluate the boundary effect of block-like foamed cement paste. This study contributes to the application of the air-coupled impact-echo method for the detection of foam cement paste.

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Section: Introductionmentioning

confidence: 99%

Boundary effect of foamed cement paste under the influence of air-coupled impact-echo

Wan,

Zhang,

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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“…For example, Sun et al 3 used the ball chain as an impact source to excite the concrete to collect vibration signals, and the peak frequency of the signal was used as an indicator to image the internal defects of the concrete. Kang et al 4 imaged the internal defects of concrete based on the low-frequency energy of the signal collected by the microphone sensor. Epp and Cha 5 analyzed acoustic vibration signals based on wavelet transform (WT) to detect internal defects of reinforced concrete structures.…”

Section: Introductionmentioning

confidence: 99%

Visualization of concrete internal defects based on unsupervised domain adaptation algorithm for transfer learning of experiment-simulation hybrid dataset of impact echo signals

Shang,

Chen

2023

Structural Health Monitoring

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Detecting concrete internal defects through deep learning analysis of impact echo signals faces two challenges: (1) the traditional signal processing method such as wavelet transform (WT) fails to reflect data-sensitive damage characteristics due to the uncertainty principle and (2) the limited labeled data acquired from real structures impedes network training. To address the first challenge, this paper proposes the WT-based synchrosqueezing transform (WT-SST) for the conversion of time-series data to the time-frequency spectrogram, which can provide effective features for the network in time and frequency domains simultaneously. To overcome the second challenge, numerical simulation data are supplemented for the augment of labeled data. To minimize the effect of data variance between experiments and simulations, this paper uses an unsupervised domain adaptation (DA) network for the transfer training of labeled simulation data (original domain) and unlabeled experimental data (target domain). The DA network extracts domain-invariant features by maximizing the domain recognition error and minimizing the probability distribution distance. The damage probability is calculated by the trained model to produce a 2D defect contour image of concrete specimens, and the three-dimensional visualization of internal defects by estimating the defect depth based on the defect area of contour image. Finally, the recognition precision, recall, F1-score, and accuracy of the model of unsupervised DA network trained by a hybrid dataset reaches 89.4%, 88.4%, 88.9%, and 94.7%, respectively.

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Multi-type classification and 3D visualization of internal defects in concrete plates based on a hybrid method of deep learning algorithm and bending mode analysis for acoustic vibration signals

Shang,

Chen

2024

Applied Acoustics

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Rapid damage assessment of concrete bridge deck leveraging an automated double-sided bounce system

Cited by 9 publications

References 27 publications

Boundary effect of foamed cement paste under the influence of air-coupled impact-echo

Boundary effect of foamed cement paste under the influence of air-coupled impact-echo

Visualization of concrete internal defects based on unsupervised domain adaptation algorithm for transfer learning of experiment-simulation hybrid dataset of impact echo signals

Multi-type classification and 3D visualization of internal defects in concrete plates based on a hybrid method of deep learning algorithm and bending mode analysis for acoustic vibration signals

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