Bayesian updating of KJHH model for prediction of maximum ground settlement in braced excavations using centrifuge data

Abstract

In this paper, a Bayesian approach for updating a semi-empirical model for predicting excavation-induced maximum ground settlement using centrifuge test data is presented. The Bayesian approach involves three steps: (1) prior estimate of the maximum ground settlement and model bias factor, (2) establishment of the likelihood function and posterior distribution of the model bias factor using the settlement measurement in the centrifuge test, and (3) development of posterior distribution of the predicted maximum settlement. This Bayesian approach is demonstrated with a case study of a well-documented braced excavation, and the results show that the accuracy of the maximum settlement prediction can be improved and the model uncertainty can be reduced with Bayesian updating.