Novel Approach to Integration of Numerical Modeling and Field Observations for Deep Excavations

Abstract

Precedent and observation of performance are an essential part of the design and construction process in geotechnical engineering. For deep urban excavations designers rely on empirical data to estimate potential deformations and impact on surrounding structures. Numerical simulations are also employed to estimate induced ground deformations. Significant resources are dedicated to monitor construction activities and control induced ground deformations. While engineers are able to learn from observations, numerical simulations have been unable to fully benefit from information gained at a given site or prior excavation case histories in the same area. A novel analysis method, self-learning in engineering simulations (SelfSim), is introduced to integrate precedent into numerical simulations. SelfSim is an inverse analysis technique that combines finite element method, biologically inspired material models, and field measurements. SelfSim extracts relevant constitutive soil information from field measurements of excavation response such as lateral wall deformations and surface settlement. The resulting soil model, used in a numerical analysis, provides correct ground deformations and can be used in estimating deformations of similar excavations. The soil model can continuously evolve using additional field information. SelfSim is demonstrated using two excavation case histories in Boston and Chicago.