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Seismic wave reflection method is an advanced geophysical detection method in tunnel geological prediction. It is more sensitive and effective in detecting geological anomalies such as fault fracture zone and karst. In order to verify the prediction efficacy and accuracy of the seismic wave reflection method with different instruments and equipment (tunnel geological prediction [TGP]/tunnel seismic prediction [TSP]) and different vibration modes (hammering, explosives), a comparison test was carried out in Jinping Tunnel. The test results showed that the time-consumption of the hammering source was short, which can greatly reduce the impact on the construction site; different vibration sources methods of seismic wave reflection can predict the unfavorable geological sections accurately.

Nondestructive testing (NDT) is an advanced and commonly used technique in tunnel engineering quality inspection. To achieve good application of nondestructive testing technology, its main application in tunnel engineering quality inspection strategy is analyzed, including the significance of applying nondestructive testing technology in tunnel engineering quality inspection, the main nondestructive testing technology applied in tunnel engineering quality test analysis, and the analysis of nondestructive testing technology of tunnel engineering quality inspection strategy. The analyses in this paper are done in hopes of providing scientific reference for the utilization of NDT technology and the improvement of tunnel engineering quality.

The \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\textit{K}$$\end{document}K-nearest neighbor (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\textit{K}$$\end{document}K-NN) query is an important query in location-based service (LBS), which can query the nearest k points to a given point, and provide some convenient services such as interest recommendations. Hence the privacy protection issue of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\textit{K}$$\end{document}K-NN query has been a popular research area, protecting the information of queries and the queried results, especially in the information era. However, most of existing schemes fail to consider the privacy protection of location points already stored on servers. Or some schemes support no update of location points. In this paper, we present an updatable and privacy-preserving \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\textit{K}$$\end{document}K-NN query scheme to address the above two issues. Concretely, our scheme utilizes the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\textit{K}$$\end{document}KD-tree (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\textit{K}$$\end{document}K-Dimensional tree) to store the location points of data owners in location service provider and encrypts the points with a distributed double-trapdoor public-key cryptosystem. Then, based on the Ciphertext Comparison Protocol and Ciphertext Euclidean Distance Calculation Protocol, our scheme can protect the privacy of location and query contents. Experimental analyses show our proposal supports some new location points for a fixed location service provider. Moreover, the queried results show a high accuracy of more than 95%.