Identify Rockburst Grades for Jinping II Hydropower Station Using Gaussian Process for Binary Classification
Aiming to the fact that it is still difficult to reasonably identify rockburst grades, the method based on Gaussian Process for Binary Classification model is proposed for identifying rockburst grades. According to few learning samples, the nonlinear mapping relationship between rockburst grades and its influencing factors is established by Gaussian Process for Binary Classification model. The method is applied to identify rockburst grades for the long exploratory tunnel and diversion tunnel of Jinping II hydropower station. The results of real engineering study show that the method is feasible, simple to be implemented and precise, that makes itself very attractive for a wide application in identifying rockburst grades.
rockburst grades identify gaussian process machine learning
Guoshao Su Yan Zhang Guoshao Su Guoqing Chen
School of Civil and Architecture Engineering Guangxi University Nanning,China State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Chengdu University of Tec
国际会议
长春
英文
364-367
2010-08-24（万方平台首次上网日期，不代表论文的发表时间）