Comparison of two inverse analysis techniques for learning deep excavation response
Résumé
Performance observation is a necessary part of the design and construction process in geotechnical engineering. For deep urban excavations, empirical and numerical methods are used to predict potential deformations and their impacts on surrounding structures. Two inverse analysis approaches are described and compared for an excavation project in downtown Chicago. The first approach is a parameter optimization approach based on genetic algorithm (GA). GA is a stochastic global search technique for optimizing an objective function with linear or non-linear constraints. The second approach, self-learning simulations (SelfSim), is an inverse analysis technique that combines finite element method, continuously evolving material models, and field measurements. The optimization based on genetic algorithm approach identifies material properties of an existing soil model, and SelfSim approach extracts the underlying soil behavior unconstrained by a specific assumption on soil constitutive behavior. The two inverse analysis approaches capture well lateral wall deflections and maximum surface settlements. The GA optimization approach tends to overpredict surface settlements at some distance from the excavation as it is constrained by a specific form of the material constitutive model (i.e. hardening soil model); while the surface settlements computed using SelfSim approach match the observed ones due to its ability to learn small strain non-linearity of soil implied in the measured settlements. © 2009 Elsevier Ltd. All rights reserved.
Mots clés
finite element method
genetic algorithm
geotechnical engineering
inverse analysis
numerical model
optimization
soil-structure interaction
strain
Chicago
Illinois
United States
Constitutive behaviors
Deep excavation
Design and construction process
Field measurement
GA optimization
Genetic algorithm approach
Global search techniques
Hardening soil models
Lateral walls
Material models
Material property
Non-linear constraints
Non-Linearity
Objective functions
Parameter optimization
Self-learning
Small strains
Soil behavior
Soil behaviors
Soil model
Surface settlements
Constrained optimization
Genetic algorithms
Geologic models
Materials
Neural networks
Numerical methods
Settlement of structures
Soils
Structural optimization
Surfaces
artificial neural network
comparative study
computer simulation
empirical analysis
excavation