有色金属科学与工程2025，Vol.16Issue(1)：104-114,11.DOI:10.13264/j.cnki.ysjskx.2025.01.012
某金矿碎石—尾砂协同胶结充填力学特性与神经网络预测分析
Research on mechanical characteristics and neural network prediction analysis of crushed stone tailings collaborative cementing filling in a gold mine
摘要
Abstract
This article focuses on the crushing of gold mine waste into crushed stones and full tailings below 5 mm.Under the conditions of preparing paste filling slurry,uniaxial compressive strength and tensile strength tests were conducted on the filling material under different parameters,and single-factor and multi-factor fitting analysis and correlation testing were conducted.The correlation between various influencing factors and the strength of the filling body was determined.The influence of various factors on the strength of the paste-filling body was explored.A predictive model was established using an improved MATLAB neural network to predict the effects of slurry concentration X1,cement sand ratio X2,sand gravel ratio X3,and curing age X4 on the strength(uniaxial compressive strength Y1,tensile strength Y2)of the filling material.The results show that there is a multivariate linear function relationship between the strength of the filling body and various parameters.The lime-sand ratio is the main factor affecting the strength of the filling body,followed by the curing age and sandstone ratio,and the slurry concentration is the smallest.The strength of the filling body increases with the increase of filling concentration,curing period,and lime sand ratio,and decreases with the increase of sand stone ratio.The established strength function model of the filling body has strong adaptability and high accuracy in predicting the uniaxial compressive strength Y1 and compressive strength Y2 of the gold mine,which provides a basis for the design of the strength demand of later filling mining.关键词
胶结充填/力学特性/神经网络/预测分析Key words
cemented fill/mechanics properties/neural network/prediction analysis分类
矿业与冶金引用本文复制引用
杨纪光,王增加,郭加仁,王鹏涛,刘杰,桑来发,盛宇航,荆晓东..某金矿碎石—尾砂协同胶结充填力学特性与神经网络预测分析[J].有色金属科学与工程,2025,16(1):104-114,11.基金项目
山东省重大科技创新工程项目(2019SDZY0504) （2019SDZY0504）
