【摘要】 土体本构参数的合理取值是数值模拟的重要前提。为准确获取湖州软土修正剑桥模型参数，针对该地区两种典型软土，提出了基于室内试验-神经网络的软土修正剑桥模型参数反演方法。首先，开展了室内三轴固结不排水试验及标准固结-回弹试验，基于室内试验结果确定了湖州地区典型软土修正剑桥模型参数反演区间；其次，基于正交试验设计原理，对基坑开挖过程中不同参数水平下维护结构侧移进行数值计算，根据数值计算结果构造出64组PSO-BP神经网络训练样本；最后，利用构造的训练集对湖州地区软土修正剑桥模型参数进行反演。研究结果表明：反演得到的两种典型软土修正剑桥模型参数临界状态有效应力比M₁、M₂，压缩参数λ₁、λ₂，回弹参数κ₁、κ₂及孔隙比e₁、e₂分别为M₁=1.076、λ₁=0.050、κ₁=0.021、e₁=1.712,M₂=1.123、λ₂=0.038、κ₂=0.012,e₂=0.967；通过反演参数计算得到的维护结构变形预测值与实测值吻合度较高，其相对误差不超过5%；基于反演参数通过有限元数值计算对基坑变形预测，预测结果验证了反演方法的准确性。针对软土修正剑桥模型参数反演，分析了神经网络训练样本数、输入层节点数对反演结果的影响。研究成果可以为湖州地区类似的基坑工程提供参数支持和技术指导。更多还原

【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.更多还原