Yes / Aiming to detect concrete defects, we propose a new identification method based on stochastic configuration networks. The presented model has been trained by time-domain and frequency-domain features which are extracted from filtering and decomposing ultrasonic detection signals. This method was applied to ultrasonic detection data collected from 5 mm, 7 mm, and 9 mm penetrating holes in C30 class concrete. In particular, wavelet packet transform (WPT) was then used to decompose the detected signals, thus the information in different frequency bands can be obtained. Based on the data from the fundamental frequency nodes of the detection signals, we calculated the means, standard deviations, kurtosis coefficients, skewness coefficients and energy ratios to characterize the detection signals. We also analyzed their typical statistical features to assess the complexity of identifying these signals. Finally, we used the stochastic configuration networks (SCNs) algorithm to embed four-fold cross-validation for constructing the recognition model. Based upon the experimental results, the performance of the presented model has been validated and compared with the genetic algorithm based BP neural network model, where the comparison shows that the SCNs algorithm has superior generalization abilities, better fitting abilities, and higher recognition accuracy for recognizing defect signals. In addition, the test and analysis results show that the proposed method is feasible and effective in detecting concrete hole defects. / This work was supported in part by the Zhejiang Provincial Natural Science Foundation (ZJNSF) project under Grant (No. LY18F030012), the National Natural Science Foundation of China projects (NSFC) under Grant (No. 61403356, 61573311).
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/18325 |
| Date | 13 January 2021 |
| Creators | Zhao, J., Hu, T., Zheng, R., Ba, P., Mei, C., Zhang, Qichun |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Article, Published version |
| Rights | © The Authors 2020. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ |
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