Knowledge base for gold deposit rock mass quality grading and PLS simplified prediction model
This study addresses the characteristics of rock masses in gold mine engineering,analyzing 7 key factors affecting rock mass stability:rock uniaxial compressive strength,RQD value,joint structural face conditions,joint structural face spacing,groundwater conditions,effect of joint structural face orientation on engineering,and in-situ stress value.These 7 indicators were adjusted to establish the rock mass quality M-RMR safety evaluation system.Using the M-RMR system,the engineering rock mass quality grades were classified for the Jiaojia Gold Mine's directly managed mining area,Sizhuang mining area,and Wang'ershan mining area.Furthermore,a neural network knowledge base model was developed to correlate the underground rock mass quality at Jiaojia Gold Mine with its influencing factors,achieving intelligent grading of the rock mass quality for engineering purposes.To simplify the M-RMR indicator system for easier practical application,variable importance projection(VIP)was used to rank the information carried by the 7 indicators,allowing the removal of unimportant variables one by one.The simplified prediction model was built using partial least squares regression of single dependent variables(PLS1).This simplified model can accurately grade rock mass quality using fewer evaluation indicators,demonstrating practical application value.
gold minerock mass quality gradingrock mass stabilityneural networkknowledge base modelsimplified modelpartial least squares regression
万方数据
维普
