Adaptive Reliability Analysis of Excavation Problems

Abstract

Excavation activities like open cutting and tunneling work may cause ground movements. Many of these activities are performed in urban areas where many structures and facilities already exist. These activities are close enough to affect adjacent structures. It is therefore important to understand how the ground movements due to excavations influence nearby structures.

The goal of the proposed research is to investigate and develop analytical methods for addressing uncertainty during observation-based, adaptive design of deep excavation and tunneling projects. Computational procedures based on a Bayesian probabilistic framework are developed for comparative analysis between observed and predicted soil and structure response during construction phases. This analysis couples the adaptive design capabilities of the observational method with updated reliability indices, to be used in risk-based design decisions.

A probabilistic framework is developed to predict three-dimensional deformation profiles due to supported excavations using a semi-empirical approach. The key advantage of this approach for practicing engineers is that an already common semi-empirical chart can be used together with a few additional simple calculations to better evaluate three-dimensional displacement profiles. A reliability analysis framework is also developed to assess the fragility of excavation-induced infrastructure system damage for multiple serviceability limit states.

Finally, a reliability analysis of a shallow circular tunnel driven by a pressurized shield in a frictional and cohesive soil is developed to consider the inherent uncertainty in the input parameters and the proposed model. The ultimate limit state for the face stability is considered in the analysis. The probability of failure that exceeding a specified applied pressure at the tunnel face is estimated. Sensitivity and importance measures are computed to identify the key parameters and random variables in the model.