scholarly journals Allowable Deformation Prediction for Surrounding Rock of Underground Caverns Based on Support Vector Machine

2016 ◽  
pp. 361-369 ◽  
Author(s):  
Ma Cong ◽  
◽  
Tan Yuehu ◽  
Li Erbing ◽  
Yang Meng ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Surrounding Rock ◽  
Support Vector ◽  
Deformation Prediction ◽  
Underground Caverns

scholarly journals Tunnel Surrounding Rock Displacement Prediction Using Support Vector Machine

2010 ◽  
Vol 3 (6) ◽  
pp. 843 ◽  
Author(s):  
Jian Sun ◽  
Bao-Zhen Yao ◽  
Cheng-Yong Yang ◽  
Jin-Bao Yao
Keyword(s):  
Support Vector Machine ◽  
Surrounding Rock ◽  
Support Vector ◽  
Displacement Prediction

scholarly journals Tunnel Surrounding Rock Displacement Prediction Using Support Vector Machine

2010 ◽  
Vol 3 (6) ◽  
pp. 843-852 ◽  
Author(s):  
Bao-Zhen Yao ◽  
Cheng-Yong Yang ◽  
Jin-Bao Yao ◽  
Jian Sun
Keyword(s):  
Support Vector Machine ◽  
Surrounding Rock ◽  
Support Vector ◽  
Displacement Prediction

scholarly journals A New Reliability Rock Mass Classification Method Based on Least Squares Support Vector Machine Optimized by Bacterial Foraging Optimization Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
S. Zheng ◽  
A. N. Jiang ◽  
X. R. Yang ◽  
G. C. Luo
Keyword(s):  
Support Vector Machine ◽  
Rock Mass ◽  
Least Squares ◽  
Optimization Algorithm ◽  
Surrounding Rock ◽  
Support Vector ◽  
Tunnel Construction ◽  
Bacterial Foraging Optimization ◽  
Mass Classification ◽  
Bacterial Foraging Optimization Algorithm

Classification of the surrounding rock is the basis of tunnel design and construction. However, conventional classification methods do not allow dynamic tunnel construction adjustments because they are time-consuming and do not consider the randomness of rock mass. This paper presents a new reliability rock mass classification method based on a least squares support vector machine (LSSVM) optimized by a bacterial foraging optimization algorithm (BFOA). The LSSVM is adopted to express the implicit relationship between classification indicators and rock mass grades, which is a response surface function for reliability evaluation. LSSVM parameters were optimized by the BFOA to form a hybrid BFOA-LSSVM algorithm. Using geological prediction and rock strength resilience results as classification indicators, samples were developed to train the LSSVM model using the hybrid algorithm. The Monte Carlo sampling method of reliability classification was implemented and applied to the Suqiao tunnel at the Puyan highway in the Fujian province of China; the influence of parameters on the performance of the algorithm is discussed. The results indicate that the new method is feasible for tunnel engineering; it can improve the classification accuracy of surrounding rock exhibiting randomness, to provide an effective means of classifying surrounding rock in the dynamic design of tunnel construction.

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