A Bayesian model updating approach applied to mechanical properties of recycled aggregate concrete under uniaxial or triaxial compression

This paper proposes a Bayesian model updating approach applied to mechanical properties of recycled aggregate concrete (RAC) under uniaxial or triaxial compression. In particular, a probabilistic calibration method is proposed for evaluating the accuracy and applicability of available deterministic models for the mechanical performances of RAC based on the Bayesian theory and the Markov Chain Monte Carlo (MCMC) method. With the aid of the Bayesian parameter estimation technique, assessments of important parameters in the updating process are conducted using a variable selection approach. The selected existing deterministic models for the estimation of RAC mechanical performances are updated accordingly. To conduct the model updating, two large databases of the mechanical properties of RAC were obtained from the literature, including 749 compressive strengths, 476 elastic moduli, 145 flexural strengths, and 324 splitting tensile strengths. Finally, the accuracy and applicability of available deterministic models were calibrated and updated improving their prediction performances.