文献类型：期刊文献

中文题名：两种拉伸实验的岩石细观参数反演分析

英文题名：Microscopic parameter inversion of particle flow rock by two tensile tests

作者：潘瑞凯 张开智 王树立 姬松涛

第一作者：潘瑞凯

机构：[1]山东科技大学矿业与安全工程学院;[2]贵州理工学院矿业工程学院

第一机构：山东科技大学矿业与安全工程学院,山东青岛266590

年份：2016

卷号：25

期号：8

起止页码：143-146

中文期刊名：中国矿业

外文期刊名：China Mining Magazine

收录：CSTPCD;;北大核心:【北大核心2014】；

基金：国家自然科学基金项目资助(编号:51564004;51374139);贵州省科技厅;贵州理工学院联合基金项目资助(编号:〔2014〕7370);山东省自然科学基金项目资助(编号:ZR2013EEM018)

语种：中文

中文关键词：直接拉伸;人工神经网络;细观参数反演;巴西实验;颗粒流;离散元

外文关键词：direct tension; artificial neural networks; microscopic parameter inversion; Brazilian test; particle flow code; discrete element method

摘要：将颗粒流程序应用于解决工程问题的前提是对岩石试件的几个力学参数进行准确标定,抗拉强度的标定便是其中之一。通过比较分析巴西实验和直接拉伸实验,结合具有非线性映射能力的BP神经网络,对颗粒流岩石的细观参数进行反演研究。表明:巴西实验条件苛刻,难以保证大量实验成功进行,无法向神经网络提供高质量训练样本,反演精度仅为61%;直接拉伸实验条件宽松,可以保证神经网络训练样本的数量和质量,反演精度可提高到83%;在保证样本数量和质量的前提下,BP神经网络有能力实现宏细观参数的准确映射,是颗粒流岩石参数标定的有效手段。
The accurate parameters calibration of rock specimen is the preconditions for applying the particle flow program to the resolution of actual engineering problems, and tensile strength is one of the parameters. This paper presents a study on the accurate and efficient back analysis of microscopic parameters of particle flow rock, by comparing and analyzing the Brazilian test and direct tensile test and in combination with the neural network which possesses the ability of non-linear mapping. The conclusions are as follows： Brazilian test,whose back analysis accuracy is only 61% ,cannot guarantee the successful operations of the numerous tests, and also cannot provide high-quality samples for the neural network as a result of its demanding requirements;Direct tensile test, which is highly adaptable and has a back analysis accuracy of 81% ,can ensure the quantity and quality of the samples for the neural network; BP neural network has the ability of mapping the macro and micro parameters accurately on condition that the quantity and quality are guaranteed, which indicates that it is an effective method for the parameters calibration of particle flow rock material.