Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)

Legacy Department

Civil Engineering


Juang, C. Hsein

Committee Member

Andrus , Ronald D

Committee Member

Bridges , William C


This dissertation is aimed at applying probabilistic approaches to evaluating the basal-heave stability and the excavation-induced wall and ground movements for serviceability assessment of excavation in clays. The focuses herein are the influence of spatial variability of soil parameters and small sample size on the results of the probabilistic analysis, and Bayesian updating of soil parameters using field observations in braced excavations.
Simplified approaches for reliability analysis of basal-heave in a braced excavation in clay considering the effect of spatial variability in random fields are presented. The proposed approaches employ the variance reduction technique (or more precisely, equivalent variance method) to consider the effect of spatial variability so that the analysis for the probability of basal-heave failure can be performed using well-established first-order reliability method (FORM). Case studies show that simplified approaches yield results that are nearly identical to those obtained from the conventional random field modeling (RFM). The proposed approaches are shown to be effective and efficient for the probabilistic analysis of basal-heave in a braced excavation considering spatial variability. The variance reduction technique is then used in the probabilistic serviceability assessment in a case study.
To characterize the effect of uncertainty in sample statistics and its influence on the results of probabilistic analysis, a simple procedure involving bootstrapping is presented. The procedure is applied to assessing the probability of serviceability failure in a braced excavation. The analysis for the probability of failure, referred to herein as probability of exceeding a specified limiting deformation, necessitates an evaluation of the means and standard deviations of critical soil parameters. In geotechnical practice, these means and standard deviations are often estimated from a very limited data set, which can lead to uncertainty in the derived sample statistics. In this study, bootstrapping is used to characterize the uncertainty or variation of sample statistics and its effect on the failure probability. Through the bootstrapping analysis, the probability of exceedance can be presented as a confidence interval instead of a single, fixed probability. The information gained should enable the engineers to make a more rational assessment of the risk of serviceability failure in a braced excavation. The case study demonstrates the potential of bootstrap method in coping with the problem of having to evaluate failure probability with uncertain sample statistics.
Finally, a Bayesian framework using field observations for back analysis and updating of soil parameters in a multi-stage braced excavation is developed. Because of the uncertainties in the initial estimates of soil parameters and in the analysis model and other factors such as construction quality, the updated soil parameters are presented in the form of posterior distributions. In this dissertation, these posterior distributions are derived using Markov chain Monte Carlo (MCMC) sampling method implemented with Metropolis-Hastings algorithm. In the proposed framework, Bayesian updating is first realized with one type of response observation (maximum wall deflection or maximum ground surface settlement), and then this Bayesian framework is extended to allow for simultaneous use of two types of response observations in the updating. The proposed framework is illustrated with a quality excavation case and shown effective regardless of the prior knowledge of soil parameters and type of response observations adopted.
The probabilistic approaches presented in this dissertation, ranging from probability-based design of basal heave, to probabilistic analysis of serviceability failure in a braced excavation considering spatial variability of soil parameters, to bootstrapping for characterizing the uncertainty of sample statistics and its effect, and to MCMC-based Bayesian updating of soil parameters during the construction, illustrate the potential of probability/statistics as a tool for enabling more rational solutions in geotechnical fields. The case studies presented in this dissertation demonstrate the usefulness of these tools.