Prediction of the Surrounding Rock Deformation Grade for a High-Speed Railway Tunnel Based on Rough Set Theory and a Cloud Model

Author(s):  
Daohong Qiu ◽  
Yang Liu ◽  
Yiguo Xue ◽  
Maoxin Su ◽  
Ying Zhao ◽  
...  
2013 ◽  
Vol 353-356 ◽  
pp. 1597-1603
Author(s):  
Shuang Lan Wu ◽  
Shi Hao Yang ◽  
Xue Wen Zhang

Difficulties of tunnel construction mainly appear in the entrance and exit stage, some adverse geological problems may occur. In terms of the tunnel at the Changsha-Kunming section of the Shanghai-Kunming passenger line, firstly, adverse geological phenomena at tunnel exits was described. Secondly, major factors leading to disasters were listed, including geology, hydrology and construction procedure. Combined with in-situ conditions, Finite Element Method (FEM) was used to analyze the instability mechanism of surrounding rock after the upper arch gate was excavated by three-bench seven-step exaction method. At last, through comparison between computed result and measuring data, several basic conclusions was obtained. It can make much sense to similar engineering.


2013 ◽  
Vol 441 ◽  
pp. 717-720
Author(s):  
Zhi Bo Ren ◽  
Chun Miao Yan ◽  
Yu Zhou Wei ◽  
Lei Sun

According to the high speed of data arriving, a large amount of data and concept drifting in the stream model, combining the techniques of rough set theory, neural network and voting rule, we put forward a new data stream classification model, which is a multi-classifier integration based on rough set theory, neural network. Firstly, it reduces all attributes using rough set theory; secondly, it constructs base classifiers on the data chunks after the reduction of attributes using the improved BP neural network; finally, it fuses various base classifiers into an ensemble by voting rule. Through applying the model to classify data stream, the experiment results show that the ensemble method is feasible and effective.


2017 ◽  
Vol 2017 ◽  
pp. 1-11
Author(s):  
Qian Yang ◽  
Zhaoling Wang

Nowadays, railway tunnel construction faces huge developments and opportunities, with a tendency for high speed and long distance. How to effectively apply the information in the construction process has been the focus of current research. According to the Xian-nvyan tunnel in Xicheng high-speed railway, our research was based on the geological forecast, selecting appropriate tunneling model parameters to establish the 3D calculation model. Through the numerical simulation of three tunnel excavation and support methods, we analyzed the displacement of surrounding rock and the plastic failure to select the construction method reasonably. Compared with the actual measured data, we judged the rationality of the selected scheme and model parameters, so as to provide design parameters which conform to the surrounding rock properties for the subsequent construction, thus optimizing the construction program and applying the concept of information-based construction in engineering actually.


2018 ◽  
Vol 16 (5) ◽  
pp. 734-749
Author(s):  
Xueliang Zhang ◽  
Meixia Wang ◽  
Binghua Zhou ◽  
Xintong Wang

Purpose Because of the properties of loess, the occurrence of collapse following deformation of a large settlement is a common problem during the excavation of tunnels on loess ground. Hence, risk management for safer loess tunnel construction is of great significance. The purpose of this paper is to explore the influence of factors on collapse risk of loess tunnels and establish a risk assessment model using rough set theory and extension theory. Design/methodology/approach The surrounding rock level, groundwater conditions, burial depth, excavation method and support close time were selected as the factors and settlement deformation was the verification index for risk assessment. First, using rough set theory, the influence of risk factors on the collapse risk of loess tunnels was calculated by researching engineering data of excavated sections. Then, a collapse risk assessment model was developed based on extension theory. As the final step, the model was applied to practical engineering in the Loess Plateau of China. Findings The weights of surrounding rock level, groundwater conditions, burial depth, excavation method and support close time obtained using rough set theory were respectively 10.811 per cent, 18.919 per cent, 24.324 per cent, 40.541 per cent and 5.406 per cent. The assessment results obtained using the model were in good agreement with field observations. Originality/value This study highlights key points in collapse risk management of loess tunnels, which could be very useful for future construction methods. The model, using easily obtained parameters, helps in predicting the collapse risk level of loess tunnels excavated under different geological conditions and by different construction organizations and provides a reference for future studies.


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