摘要
基于颗粒流(PFC2D/3D)模型的细观离散元方法是近些年来兴起的一种新的岩土数值计算方法,在岩土工程非连续介质领域中发挥着重要作用,但由于该类模型宏细观参数关系的复杂性,使其在实际工程应用中受到限制。在前人研究的基础上,以无黏结颗粒材料为例,采用4因素3水平正交试验方法设计了9类试样,并各自在3种侧压下进行双轴试验,以探求细观参数不同组合对介质宏观特性的影响,从而避免了控制变量法固定某些参数的局限,更加科学地分析了细观参量对宏观特性影响的敏感程度,并据此提出该类材料宏细观参数匹配的调整原则。最后利用人工神经网络实现了该类材料宏细观参数之间的互演计算,以供PFC模型在实际岩土工程计算时参考。
Mesoscopic discrete element method on the basis of particle flow model is one of the latest numerical methods in recent years,and plays an important role in the area of discontinuous medium of geotechnical engineering.Nevertheless,its application in actual engineering is limited because of complex interrelationship between macroscopic and mesoscopic parameters.Based on the theory of orthogonal test,we take an unbonded material as the test object and design 9 samples which are tested in biaxial compression under 3 different confining pressures.In this test,4 factors are considered and each varies in 3 levels,which can reflect the change of macroscopic property of medium caused by different mesoscopic parameter groups,and can avoid the flaw of controlling-variables-method which makes some variables constant.According to test results,the sensitivity of mesoscopic parameters are discussed and principles about parameter adjusting are obtained.In the end,mutual deduction of macroscopic and mesoscopic parameters is achieved through neural network algorithm.This research could offer reference for the use of software PFC2D/3D（Particle Flow Code in 2 Dimensions/3 Dimensions） in actual engineering.
出处
《长江科学院院报》
CSCD
北大核心
2012年第5期45-50,共6页
Journal of Changjiang River Scientific Research Institute
基金
国家自然科学基金资助项目(51178358)
湖北省自然科学基金重点资助项目(2010CDA057)
关键词
颗粒流模型
正交试验
宏细观参数关系
神经网络算法
互演计算
particle flow model
orthogonal test
relationship between macroscopic and mesoscopic parameters
neural network algorithm
mutual deduction method
