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Force analysis and treatments for Lidong tunnel of Renxin expressway crossing karst caves

Zhu Kai, Zhang Kui, Wu Xiang-Dong, Chen Xiang-Ge

Archives of Civil Engineering

2024

Vol. 70, nr 1

693--705

There are several large karst caves at haunch part of the Lidong Tunnel during construction, together with inrush water due to high pressure within these caves. In light of it, this paper takes YK342+113 section as an example and adopts finite difference software FLAC 3D, so as to analyze tunnel deformation when crossing karst caves under six different working conditions, including with or without karst cave, before and after karst treatment, along with support locations. According to analysis results: First, the wall rock mainly had deformation at tunnel vault when evacuating at the third bench, which is a critical monitoring focus for tunnel construction; Second, karst cave treatment contributed to better conduct forces on both sides of wall rock, thus reducing vault settlement, while not affecting horizontal convergence and upturn of vaults; Third, treatment measures were proved to be effective in minimizing wall rock deformation by comparing deformation curves under different conditions; Fourth, after treatment measures, the angular points within the cave’s chamber had stress concentration, which might cause secondary collapse. Field monitoring data revealed that the final settlement of the tunnel vault was relatively consistent with the numerical analysis results, with a distinct change in daily settlement after initial support construction. By integrating numerical analysis and field monitoring, the rationality of the karst treatment plan was fully verified, providing a valuable reference for similar projects.

SDAE cleaning model of wind speed monitoring data in the Mine Monitoring System

Zhao Dan, Shen Zhiyuan, Song Zihao, Xie Lina

Archives of Mining Sciences

2023

Vol. 68, no. 2

251--266

The effective utilisation of monitoring data of the coal mine is the core of realising intelligent mine. The complex and challenging underground environment, coupled with unstable sensors, can result in “dirty” data in monitoring information. A reliable data cleaning method is necessary to figure out how to extract high-quality information from large monitoring data sets while minimising data redundancy. Based on this, a cleaning method for sensor monitoring data based on stacked denoising autoencoders (SDAE) is proposed. The sample data of the ventilation system under normal conditions are trained by the SDAE algorithm and the upper limit of reconstruction errors is obtained by Kernel density estimation (KDE). The Apriori algorithm is used to study the correlation between monitoring data time series. By comparing reconstruction errors and error duration of test data with the upper limit of reconstruction error and tolerance time, cooperating with the correlation rule, the “dirty” data is resolved. The method is tested in the Dongshan coal mine. The experimental results show that the proposed method can not only identify the dirty data but retain the faulty information. The research provides effective basic data for fault diagnosis and disaster warning.

Nowoczesne metody monitoringu i transmisji danych

Rutecki K., Tomczyk A., Majewski J., Miedziński B., Habrych M.

Mechanizacja i Automatyzacja Górnictwa

2011

R. 49, nr 8

3-7

W artykule przedstawiono i przedyskutowano założenia autorskiego systemu akwizycji danych, realne problemy niezawodności pracy tego systemu, sposobu rozwiązań koncentratora oraz konstrukcji poszczególnych modułów czujnikowych dla wybranych wielkości fizycznych. Sformułowano odpowiednie wnioski praktyczne z uzyskanych wyników przeprowadzonych prac badawczych.

The article presents the assumptions of the authors-developed data acquisition system, real problems related to the reliability of this system, solutions of the concentrator and construction of particular sensor modules for the selected physical quantities. Suitable practical conclusions were formulated based on the results of the conducted research.