江淮水利科技Issue(1)：27-30,40,5.DOI:10.20011/j.cnki.JHWR.202501004
基于BP神经网络的胶凝砂砾石细观参数标定研究
Research on calibration of cemented sand-gravel microscopic parameters based on BP neural network
李飞 1梅应春2
作者信息
- 1. 安徽省交通规划设计研究总院股份有限公司,安徽 合肥 230088
- 2. 安徽省交通勘察设计院有限公司,安徽 合肥 230011
- 折叠
摘要
Abstract
To address the limitations of traditional laboratory tests in obtaining meso-level parameters of cemented sand and gravel(CSG)materials,this paper proposed a BP neural network-based calibration approach for meso-parameters of CSG.Macroscopic parameters were obtained from uniaxial compression tests on CSG,and parameter sensitivity analysis was used to calculate meso-parameters,selecting those with higher sensitivity for the BP neural network-based CSG meso-parameter cali-bration study.The model was evaluated with a mean absolute error of 0.425,mean squared error of 0.434,root mean squared error of 0.633,mean absolute percentage error of 1.46%,and a coefficient of determination of 0.989.The model runtime was 84 seconds,indicating that it accurately captures the meso-parameters of CSG and exhibits strong generalization ability,there-by providing a reliable basis for the study of CSG meso-parameters.关键词
胶凝砂砾石/BP神经网络/细观参数/宏观参数/参数标定Key words
cementitious sand gravel/BP neural network/mesoscopic parameters/macro parameters/parameter calibration分类
水利科学引用本文复制引用
李飞,梅应春..基于BP神经网络的胶凝砂砾石细观参数标定研究[J].江淮水利科技,2025,(1):27-30,40,5.
