A Bayesian model updating approach applied to mechanical properties of recycled aggregate concrete under uniaxial or triaxial compression. (27th September 2021)

• Record Type: Journal Article
• Authors: Xu, J.J.; Chen, W.G.; Demartino, C.; Xie, T.Y.; Yu, Y.; Fang, C.F.; Xu, M.
• Is Part Of: Construction & building materials. Volume 301(2021)
• Publication Date: 2021-09-27
• Subjects: Recycled aggregate concrete -- Probabilistic calibration -- Bayesian model update -- Mechanical performances; Building materials -- Periodicals; 624.18
• DOI: 10.1016/j.conbuildmat.2021.124274
• Languages: English
• ISSNs: 0950-0618

Abstract

Highlights: Recycled aggregate concrete (RAC) under compressive uniaxial or triaxial compression ayesian model updating approach applied to mechanical properties of RAC. Two large databases of the mechanical properties of RAC were obtained from the literature. Probabilistic models to predict the RAC under uniaxial or triaxial compression. Abstract: 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.