Segmental Box Girder: Deflection Probability and Bayesian Updating

Abstract

Probabilistic prediction of the confidence limits on long‐time deflections and internal forces of prestressed concrete segmental box‐girder bridges is developed. The uncertainty of the predictions based on the existing models for concrete creep (the prior) is very large, but it can be greatly reduced by Bayesian updating on the basis of short‐time measurements of the deflections during construction or of short‐time creep and shrinkage strains of specimens made from the same concrete as the bridge. The updated (posterior) probabilities can be obtained by latin hypercube sampling, which reduces the problem to a series of deterministic creep structural analyses for randomly generated samples of random parameters of the creep and shrinkage prediction model. The method does not require linearization of the problem with regard to the random parameters, and a large number of the random parameters can be taken into account. Application to a typical boxgirder bridge with age differences between its segments and with a change of structural system from statically indeterminate to determinate is illustrated numerically. The results prove that design for extreme, rather than mean, long‐time deflections and internal forces is feasible. Adoption of such a design approach would improve long‐term serviceability of box‐girder bridges.