人民长江2024，Vol.55Issue(7)：240-246,7.DOI:10.16232/j.cnki.1001-4179.2024.07.031
基于机器学习的岩石节理面力学性能分析及预测
Analysis on mechanical properties of fractured rock mass and intelligent prediction based on machine learning
摘要
Abstract
Accurate determination of macroscopic mechanical properties of fractured rock mass is critical in geotechnical and tunnel engineering for effective design and construction.The mechanical behavior of these rock masses is directly influenced by the various morphologies of rock joints.In light of this,this study employed the spectrum fractal dimension D and frequency do-main amplitude integral Rq as quantitative parameters to characterize joint morphology.Furthermore,a joint reconstruction method utilizing Fourier transform technology was devised to precisely define the shape characteristics.To validate the proposed approach,direct shear tests were conducted on rocks with different joint morphologies,employing a combination of 3D printing and numerical analysis techniques.The numerical calculation model was subsequently calibrated for accuracy.Building upon these findings,a systematic parameter analysis was performed to evaluate the rock mechanics performance across diverse joint morphologies.The research results indicated that fractal dimension D and frequency domain amplitude integral Rq are effective parameters for quanti-fying and evaluating joint morphology.Finally,based on the genetic algorithm improved BP neural network,a quantitative mapping relationship between fractal dimension D,frequency domain amplitude integral Rq,normal pressure,friction coefficient and the me-chanical properties of fractured rocks was constructed,forming an intelligent prediction method for the mechanical properties of fractured rocks that considers the characteristics of joint morphology.关键词
节理形貌分析/节理量化重构/数值模拟/直剪试验/机器学习Key words
joint morphology analysis/quantitative reconstruction of joints/numerical simulation/direct shear test/machine learning分类
建筑与水利引用本文复制引用
林永贵,王海波,魏立新,徐江平,马辉..基于机器学习的岩石节理面力学性能分析及预测[J].人民长江,2024,55(7):240-246,7.基金项目
国家自然科学基金青年基金项目(52208381) （52208381）
