Probabilistic error assessment and correction of design code-based shear strength prediction models for reliability analysis of prestressed concrete girders

Aiming at probabilistic error assessment of design code models for shear strength prediction of prestressed concrete (PC) girders, this study compiled an experimental database containing 369 PC girders that failed in shear. Using the experimental database, this paper first assessed seven well-received shear strength models from five concrete structure and bridge design codes, including ACI 318-19, AASHTO LRFD 2017, CSA A23.3:19, CSA S6:19 and fib MC 2010. In view of the fact that systematic error exists in those models, polynomial correction terms were calibrated for each model together with the remaining error quantified based on the compiled experimental database and Bayesian updating. The resulted models can be used for shear strength predictions with better accuracy and, more importantly, with the model uncertainty quantified probabilistically. In the end, a case study of fragility analysis was conducted to show the benefits of the developed probabilistic models.

Automated summary

Aiming to assess the probabilistic error of design code models for shear strength prediction of prestressed concrete (PC) girders, this study compiled an experimental database of 369 failed PC girders. The study evaluated seven well-received shear strength models from five design codes and calibrated polynomial correction terms for each model to reduce systematic error. The resulting models provide more accurate shear strength predictions and probabilistic quantification of model uncertainty. A case study of fragility analysis demonstrated the benefits of the developed probabilistic models.