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Underground runoff modulus France Parameter Optimization

Author: PanMingMing
Tutor: HuangTao
School: Southwest Jiaotong University
Course: Engineering Environmental Control
Keywords: Inflow Forecast Underground runoff modulus Parameter Optimization Impact width AHP BP neural network
CLC: U453.61
Type: Master's thesis
Year: 2011
Downloads: 36
Quote: 0
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Abstract


For long tunnel water inflow forecasting accuracy has been difficult to improve this problem, this paper summarizes the long tunnel water inflow forecasting traditional methods applicable conditions , advantages and disadvantages , and comparative analysis of six long tunnel water inflow forecasting results measured results . The results showed that underground runoff modulus France compared with other traditional methods with high accuracy , simple and easy to implement. But the underground runoff modulus greater subjective parameter selection method , and sometimes there will be a larger deviation predicted results . Prediction underground runoff modulus long tunnel water inflow selected parameters are: underground runoff modulus and impact width. Underground runoff modulus is usually obtained by field measurement , this paper based on engineering experience, proposed underground runoff modulus determination of optimization methods for the future selection of underground runoff modulus reference. Affect the width of the selection is generally used empirical values. This thesis has been selected to build the tunnel typical cross-section of the underground runoff modulus inversion method the actual impact of the width , and the predictive parameter comparison results show that the inversion results and experimental results are quite different. Therefore, this thesis underground runoff modulus method parameter optimization study to optimize the impact of the choice of the width of the main . Affect the width of the selected factors include : surface conditions , rock properties and geological structure . This thesis analyzes the AHP weights of each factor , the establishment of the grading factors scoring criteria , and the selected section surface conditions , nature and geological formations were evaluated . The evaluation results as a sample , using BP neural network optimized impact width selection model . Application results show that BP neural network optimization can improve the impact of selection effects width width selected accuracy underground runoff modulus to improve predictive accuracy method has certain reference significance.

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CLC: > Transportation > Road transport > Tunnel project > Tunnel buildings and equipment > Waterproofing and drainage,waterproofing and drainage equipment > Groundwater and gushing water control measures and equipment
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