化工矿产地质2025，Vol.47Issue(1)：78-82,5.
基于BP神经网络的花岗岩单轴抗压强度预测
Prediction of Uniaxial Compressive Strength of Granite Based on BP Neural Network
杨奇超 1王朋姣2
作者信息
- 1. 中化地质河南局集团有限公司,河南 郑州 450000
- 2. 中牟县人民政府办公室,河南 郑州 451450
- 折叠
摘要
Abstract
In order to study the mechanical behavior of granite,through physical and mechanical tests and section identification,physical and mechanical parameters and mineral composition are obtained.Using mineral composition,density,and longitudinal wave velocity as basic indicators,BP neural network prediction model is constructed to study the relationship between mineral composition,physical characteristics,and mechanical properties of granite.The results show that using the four indicators ofeldspar content,quartz/mica,longitudinal wave velocity,and density,the uniaxial compressive strength of granite can be accurately predicted with the help of the BP nerral network.It is concluded that without conducting mechanical tests,the uniaxial compressive strength of granite can be preliminarily estimated through mineral composition and physical parameters.关键词
花岗岩/单轴抗压强度/BP神经网络/预测Key words
Granite/Uniaxial compressive strength/BP Neural Network/Prediction分类
天文与地球科学引用本文复制引用
杨奇超,王朋姣..基于BP神经网络的花岗岩单轴抗压强度预测[J].化工矿产地质,2025,47(1):78-82,5.
