Identification of CFST voids based on mutual information and MiniRocket network

In order to improve the efficiency and accuracy of concrete filled steel tube (CFST) void detection, an intelligent recognition method based on fast Fourier transform (FFT), mutual information (MI) and MiniRocket neural network is proposed in this paper. First, the time domain signal of the CFST percussion wave to be measured is converted to the frequency domain signal using FFT. Secondly, MI is used to establish the correlation between the frequency domain signal and the void state, and the top 30 features with the largest correlation are extracted to establish the data set, which avoids complex mathematical operations and redundant information. A MiniRocket deep learning network is established, and by using fewer parameters and smaller feature sizes improving the speed and accuracy of classification. Finally, the noise robustness of the model is investigated and compared with other algorithms, feature extraction methods and recognition methods. The results show that the proposed method achieves 100 % average prediction accuracy in 100 repetitions of the experiment for different void depths and void widths. At high SNR, this method is less affected. In addition, compared with other algorithms, feature extraction methods and recognition methods, this method has better prediction performance. Therefore, the proposed method has great application potential in the actual intelligent void identification of CFST in the future. © 2024 Chinese Vibration Engineering Society. All rights reserved.