An attribute recognition model for safe thickness assessment between concealed karst cave and tunnel

2019 ◽  
Vol 26 (4) ◽  
pp. 955-969 ◽  
Author(s):  
Xin Huang ◽  
Shu-cai Li ◽  
Zhen-hao Xu ◽  
Ming Guo ◽  
Xue-song Shi ◽  
...  
Keyword(s):  
Karst Cave ◽  
Recognition Model ◽  
Concealed Karst Cave ◽  
Attribute Recognition ◽  
Safe Thickness

Attribute Recognition Model Based on Entropy Weight and Its Application to Evaluation of Groundwater Quality

2010 ◽  
Vol 29-32 ◽  
pp. 2698-2702
Author(s):  
Xian Qi Zhang ◽  
Wen Hong Feng ◽  
Nan Nan Li
Keyword(s):  
Water Quality ◽  
Groundwater Quality ◽  
Information Entropy ◽  
Simple Calculation ◽  
Weight Coefficient ◽  
Entropy Theory ◽  
Entropy Weight ◽  
Recognition Model ◽  
Model Based ◽  
Attribute Recognition

It is necessary to take into account synthetically attribute of every index because of independence and incompatibility resulted from single index evaluating outcomes. Through the information entropy theory and attribute recognition model being combined together, attribute recognition model based on entropy weight is constructed and applied to evaluating groundwater quality by a new method, weight coefficient by the law of entropy value is exercised so that it is more objective. The outcome from concrete application indicates that it is suitable to evaluate water quality with reasonable conclusion and simple calculation.

The Application of Entropy Weight of Attribute Recognition Model in Reservoir Eutrophication Evaluation

2013 ◽  
Vol 477-478 ◽  
pp. 870-873
Author(s):  
Du Wu ◽  
De Shan Tang ◽  
Xing Wang Lu ◽  
Wen Zhong Yu
Keyword(s):  
Weight Distribution ◽  
Guangdong Province ◽  
Element Model ◽  
Recognition Method ◽  
Entropy Weight ◽  
Recognition Model ◽  
Calculation Results ◽  
Attribute Recognition ◽  
Matter Element Model

Subjective factors could affect the weight distribution of each index in evaluation of reservoir eutrophication, so the example used entropy to deal with the weight distribution of each index. Combined attributes recognition method, the writer selected six indicators to build the entropy weight of attribute recognition model about reservoir eutrophication of ten large reservoirs in Guangdong Province. By comparing the calculated results with the results of matter-element model, the calculation results were basically consistent. So entropy weight of attribute recognition model is applicable to the evaluation of the reservoir eutrophication and it can ensure the fairness and reasonableness of weight distribution.

Numerical Simulation of Safe Distance between Concealed Karst Cave and Tunnel Based on Shear Strength Reduction Method Safety Factor Catastrophe

2014 ◽  
Vol 501-504 ◽  
pp. 1753-1756
Author(s):  
Yong Biao Lai ◽  
Mang Shu Wang ◽  
Xin Hua You
Keyword(s):  
Numerical Simulation ◽  
Safety Factor ◽  
Reduction Method ◽  
Strength Reduction ◽  
Karst Cave ◽  
Strength Reduction Method ◽  
Safe Distance ◽  
Shear Strength Reduction Method ◽  
Concealed Karst Cave ◽  
Shear Strength Reduction

The numerical simulation method of safe distance between concealed karst cave and tunnel based on shear strength reduction method safety factor catastrophe is proposed. The safety factor of different distance between concealed karst cave and tunnel can be obtained using strength reduction finite element method numerical calculation with the finite element solution non convergence as the standard rock damage, then compare the safety factor of different distance, the catastrophe of safety factor corresponding to the distance is the safe distance between concealed karst cave and tunnel, and the plastic zone of rock stratum between concealed karst cave and tunnel is reaching run-through state, which is verified by engineering example.

Predicting Model of Safe Distance between Tunnel and Karst Cave

2013 ◽  
Vol 470 ◽  
pp. 862-865
Author(s):  
Yong Biao Lai ◽  
Meng Shu Wang ◽  
Cheng Uang Bai
Keyword(s):  
Poisson Ratio ◽  
Pressure Coefficient ◽  
Influence Factors ◽  
Friction Angle ◽  
Support Vector ◽  
Karst Cave ◽  
Safe Distance ◽  
Predicting Model ◽  
Rock Density ◽  
Concealed Karst Cave

A method of predicting safe distance between tunnel and karst cave based on support vector machine was proposed, 10 parameters (rock density,elasticity modulusE, poisson ratio, friction angle, cohesionC, lateral pressure coefficient , the karst cave spanL,rate between height and span of the Karst caveR, tunnel deepH,cave position) were chosen for safe distance influence factors, then an intelligent predicting model of safe distance between tunnel and karst cave was built, this predicting model can predict 7 kinds position of safe distance between tunnel and concealed karst cave and was feasible and high prediction precision, which was verified by engineering example.

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