KEYWORDS: Denoising, Wavelets, Tunable filters, Deformation, Data modeling, Signal to noise ratio, Correlation coefficients, Digital filtering, Modal decomposition, Reconstruction algorithms
The settlement deformation of subway tunnels during the construction and operation stages is a relatively complex nonlinear process. The original settlement deformation monitoring data will be affected by some noise during the collection process. In order to reduce the impact of noise and errors in the subsequent prediction process, a joint denoising model based on the improved empirical mode decomposition (ICEEMDAN) algorithm, wavelet threshold denoising and NLMS adaptive filtering was constructed. The model first uses ICEEMDAN to decompose the original data. After decomposing the intrinsic modal component IMF, it divides it into high-frequency and low-frequency components. Then the wavelet threshold is used to remove the components with high correlation coefficients in the high-frequency and low-frequency components. Noise and NLMS adaptive filtering are processed, and finally the additive reconstruction is performed to obtain the denoised data. Experimental analysis results show that compared with ICEEMDAN, wavelet threshold denoising and NLMS adaptive filtering algorithms, the joint denoising model has better denoising effect, higher correlation coefficient, signal-to-noise ratio and smaller mean square root error.
The defects of shield tunnel segments, such as dislocation, cracking and falling off, are one of the hot research topics in underground engineering. Based on data driven thoughts, the point cloud of shield tunnel is expanded into a binary image. Due to the image contains the mapping topological relationship between the original point cloud and the pixel, the morphological algorithm was used to identify the splice joints between rings, segmentation of tunneling image expansion is realized ring by ring. Then, the laser point cloud data were segmented at the segment scale, and the defects could be identified and detected in the segment. The experimental results show that the proposed method could effectively identify the segment defects.
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