岩土力学2023，Vol.44Issue(11)：3318-3326,9.DOI:10.16285/j.rsm.2023.0932
软土修正剑桥模型参数反演及其应用研究
Parameter inversion and application of soft soil modified Cambridge model
摘要
Abstract
The reasonable value of soil constitutive parameters is an important prerequisite for numerical simulation.In order to accurately obtain the parameters of the modified Cambridge model for Huzhou soft soil,a parameter inversion method for the modified Cambridge model based on laboratory experiments and neural networks was developed for two typical soft soils in the region.Firstly,laboratory triaxial consolidation undrained tests and standard consolidation rebound tests were conducted,and based on the test results,the parameter inversion interval of the modified Cambridge model for typical soft soil in Huzhou region was determined.Secondly,based on the principle of orthogonal experimental design,numerical calculations were conducted on the lateral displacement of the retaining structure at different parameter levels during the excavation process of the foundation pit.Based on the numerical calculation results,64 sets of PSO-BP neural network training samples were constructed.Finally,the constructed training set was used to invert the parameters of the modified Cambridge model for soft soils in Huzhou area.The critical state effective stress ratio(M1,M2),compression parameter(λ1,λ2),rebound parameter(κ1,κ2),and void ratio(e1,e2)of the two typical soft soil modified Cambridge model parameters obtained through inversion were M1=1.076、λ1=0.050、κ1=0.021、e1=1.712,M2=1.123、λ2=0.038、κ2=0.012,e2=0.967.The predicted deformation values of the retaining structure calculated through inversion parameters were in good agreement with the measured values,with a relative error of no more than 5％.Based on the inversion parameters,finite element numerical calculation was used to predict the deformation of the foundation pit,and the prediction results verified the accuracy of the inversion method.The influence of the number of neural network training samples and the number of input layer nodes on the inversion results of the Cambridge model parameters for soft soil correction was analyzed.The research results can provide parameter support and technical guidance for similar foundation pit projects in Huzhou area.关键词
软土/修正剑桥模型/室内试验/PSO-BP神经网络/参数反演Key words
machine learning/artificial intelligence/geotechnical engineering/forecast/algorithm分类
建筑与水利引用本文复制引用
虞洪,陈晓斌,易利琴,邱俊,顾正浩,赵辉..软土修正剑桥模型参数反演及其应用研究[J].岩土力学,2023,44(11):3318-3326,9.基金项目
国家自然科学基金资助项目(No.51978674).This work was supported by the National Natural Science Foundation of China(51978674). （No.51978674）
