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 (M

M)

compression parameter ()

rebound parameter ()

and void ratio () of the two typical soft soil modified Cambridge model parameters obtained through inversion were . 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.