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《Chinese Journal of Computational Mechanics》 2017-05
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Probabilistic model for critical diagonal crack angle of shear-critical reinforced concrete columns

YU Bo;WU Ran-li;CHEN Bing;Guangxi Key Laboratory of Disaster Prevention and Engineering Safety,Key Laboratory of Disaster Prevention and Structural Safety of China Ministry of Education,School of Civil Engineering and Architecture,Guangxi University;  
Traditional models for critical diagonal crack angle of shear-critical reinforced concrete(RC)column are generally deterministic and exhibit low accuracy and large scatter,due to the fact that they do not consider the uncertainties of various influential factors,such as material parameters,geometric dimension and boundary constraints and so on.In order to overcome the above limitations,aprobabilistic model for critical diagonal crack angle of shear-critical RC column was established in this study.The deterministic model for critical diagonal crack angle of shear-critical RC column was proposed based on the variable angle truss model first.Then,aprobabilistic model for critical diagonal crack angle of shearcritical RC column which takes into account the influence of both epistemic and aleatory uncertainties was developed by combining with the Bayesian theory and the Markov Chain Monte Carlo(MCMC)method.Moreover,analytical expressions of the mean and variance of probabilistic critical diagonal crack angle were also derived.The accuracy and efficiency of the proposed probabilistic model were validated by comparing with the experimental data and existing deterministic models,which indicates that the proposed probabilistic model could describe the probabilistic characteristic of critical diagonal crack angle of the shear-critical RC column reasonably.Most importantly,the proposed probabilistic model provides not only a benchmark to calibrate the confidence level of traditional deterministic models,but also an efficient way to determine the characteristic values of critical diagonal crack angles of shear-critical RC column with different confidence levels.
【Fund】: 国家自然科学基金(51368006 51478125);; 广西重点实验室系统性研究项目(2013ZDX06)资助项目
【CateGory Index】: TU375.3
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