Reducing uncertainties in estimating long-time prestress losses in concrete structures using a hygro-thermo-chemo-mechanical model for concrete

Abstract

Prestress losses occur in concrete structures due to long-time creep and shrinkage in concrete, and stress relaxation in prestressing strands. The existing formulations for creep and shrinkage modeling in concrete are empirical in nature, which have been developed based on a particular set of measured data. In this study a coupled hygro-thermo-mechanical formulation is used to model the physics associated with creep and shrinkage in concrete structures. Uncertainty tools based on a probabilistic framework are used to model the possible variations in the input variables in the hygro-thermo-mechanical formulation. A distance measure based global response sensitivity analysis is adopted to identify the parameters having maximum impact on the output responses. Uncertainties in the selected set of important variables are reduced using Bayesian inference and short-time measured responses. For validating efficiency of the uncertainty reduction technique adopting Bayesian inference and global response sensitivity, the predictions of creep and shrinkage in concrete are compared with a few experiments from the North-Western University (NU) data base. The uncertainty reduction technique is then used to predict the long-time prestress losses in post-tensioned concrete beams and slabs cast in the laboratory.