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Optimization of Tunnel Overbreak Prediction Based on Geological Parameter Analyses
(1 Engineering Research Center of Rock-Soil Drilling and Excavation and Protection, Ministry of Education, Wuhan 430074; 2 Faculty of Engineering, China University of Geosciences, Wuhan 430074; 3 School of Civil Engineering, Hunan University, Changsha 410082)
Abstract Considering that overbreak of rock may cause construction cost increases, large deformation or even tunnel collapse, and using the Mingshan tunnel as an example, this paper analyzes overbreak characteristics and establishes a prediction model. Using the blasting parameters as a constant, the effective geological parameters as an input and the actual overbreak volume as an output, three prediction methods are compared: the Fisher Discrimination Analysis Method(FDA), the Conjugate Gradient Method(CG) and the Support Vector Machine Method(SVM). The results show that their correlation coefficients R2 are 0.694, 0.718 and 0.947,with the correlation coefficient of the SVM model being the highest and the CG model coming second. The SVM model has sound prediction precision and adaptability even at the data point where abrupt change occurs. Adopting the SVM model can result in optimal quantitative prediction with high precision, while adopting the CG model can provide rapid and simple prediction with controllable precision.
Abstract：
Considering that overbreak of rock may cause construction cost increases, large deformation or even tunnel collapse, and using the Mingshan tunnel as an example, this paper analyzes overbreak characteristics and establishes a prediction model. Using the blasting parameters as a constant, the effective geological parameters as an input and the actual overbreak volume as an output, three prediction methods are compared: the Fisher Discrimination Analysis Method(FDA), the Conjugate Gradient Method(CG) and the Support Vector Machine Method(SVM). The results show that their correlation coefficients R2 are 0.694, 0.718 and 0.947,with the correlation coefficient of the SVM model being the highest and the CG model coming second. The SVM model has sound prediction precision and adaptability even at the data point where abrupt change occurs. Adopting the SVM model can result in optimal quantitative prediction with high precision, while adopting the CG model can provide rapid and simple prediction with controllable precision.
LU Zhong-Le-1,
2 ,
WU etc
.Optimization of Tunnel Overbreak Prediction Based on Geological Parameter Analyses[J] MODERN TUNNELLING TECHNOLOGY, 2015,V52(3): 189-192
2015, Vol. 52 