When constructing buildings with basements in urban areas, the deformation levels during the excavation process must be carefully evaluated to prevent or minimize damage to adjacent structures. Because of uncertainties in predicting ground deformations during the design phase for a project, a robust monitoring program should be included in significant excavation projects. The adaptive management approach uses field data and updates key parameters affecting the computed deformation via numerical analysis during early stages of a construction project. The new parameters and subsequent numerical analyses form the basis of new simulations to predict responses of later stages. For this process, inverse analysis is carried out to optimize soil parameters based on the field observations. In this thesis, results of the inverse analysis method are used to study the effects of various soil models to predict lateral wall movements of a sheet pile-supported excavation in Chicago.', 'The ground and wall deformations of the Louis A. Simpson and Kimberly K. Querry Biomedical Research Center are summarized and evaluated in light of subsurface conditions, construction activities and support system stiffness. The responses are compared with other nearby excavations and semi-empirical correlations.', 'Inverse analysis is carried out for the excavation process using three soil models in the clay layers, the Hardening Soil Model, the Hardening Soil Model with Small-Strain Stiffness and the Hypoplastic Constitutive Model. PLAXIS 2D is used for the finite element simulation of the excavation. Lateral deformations at the wall are used as the observations in the optimization. Optimized parameters are determined at various stages of the excavation to evaluate the most appropriate soil model for these conditions.

Last modified

• 10/08/2019

Creator

• Sangrae Kim

DOI

• https://doi.org/10.21985/n2-ax8k-s313

Subject

• Civil and Environmental Engineering

Keyword

• soil model
• adaptive management
• inverse analysis
• observed performance

Date created

• 2018-01-01

Resource type

• Dissertation

Rights statement

• In Copyright