Research On Subway Tunnel Surroundding Rock Sub-Classification And Estimation System

With the construction of a large number of subway tunnel, the current surrounding rock classification method cannot meet the rock classification needs to some extent under complex conditions such as a lot of groundwater, weak structural surface, high geostress and so on. In the process of actual subway tunnel construction, there is a phenomenon that when the grades of the surrounding rocks are the same, their stabilities are always different. Therefore, the establishment of a new integrated classification method for the subway tunnel surrounding rock in the construction stage is very necessary.In order to reduce the rock collapse, flooding and other geological disasters that often happened in subway tunnel construction due to the problems caused by surrounding rock classification and to get a clearer understanding about the geological conditions of the surrounding rock in the construction area, this article aims at the specific situation of the subway tunnel of Wuhan. Through collecting the geological survey data in the construction site, we adopt the discriminant analysis method to achieve a transition from qualitative to quantitative sub-classification of the surrounding rock based on the basic rock mass classification. Combined C# with the secondary development of ArcEngine to realize the automatically calculation of rock level of the geological drilling, according to the principle of Inverse Distance Weighted(IDW) to estimate the grade and realize the 3D hierarchical display and rendering of the surrounding rock based on the existing drilling data, using KML landmarks to record the details of the surrounding rock that have been estimated of the grade on the Google Earth platform of the system, realizing the real-time publication and sharing of the resources. The main research contents include:(1) Key technology research, including the theory and application of the discriminant analysis method in the transition from qualitative to quantitative sub-classification of the surrounding rock, and the use of discriminant analysis to establish theory criteria of sub-classification of the surrounding rock; the theory and application of the IDW, Cross-validation, KML technology and computer positioning technology based on WIFI and wireless cellular network.(2) According to the specific distribution of the surrounding rock and geological drilling, combined Google SketchUp with ArcGIS to build the three-dimensional model of the tunnel surrounding rock and geological drilling for the display and analysis of the system.(3) Combined C# with the secondary development of Google Earth and ArcEngine to design a software to realize the automatically calculation of rock level, to estimate the grade and realize the 3D hierarchical display and rendering of the surrounding rock, to conduct the error analysis and to publish and share the classification results.(4)We use W section of the construction line as the specific example of system testing, with a variety of methods to estimate the systematic error.The results indicate that the basic quality sub-classification of rock mass method based on the discriminant analysis is more precisely in the classification of tunnel surrounding rock and able to meet the requirement of surrounding rock classification under complex conditions such as groundwater and weak structural surface, the surrounding rock sub-classification and estimation system also has good practicality. The accuracy of the discriminant function calculated from the 500 known samples is 91.8%, the inverse error of system estimation in the classification of the surrounding rock is 8.97%, compared with the excavation revealed information, the difference is 11%, the 3D hierarchical display and rendering of the surrounding rock is closer to the actual situation, which can provide a reference for the subway tunnel construction, it can also be the supplement for the geological database of follow construction engineering in order to maximize the use of existing data resources, reduce engineering expenses, optimize engineering guidance, enhance the quality of engineering investigation and so on.