刘圣宾, 凌干展, 余波. 考虑力学机制和不确定性影响的钢筋混凝土柱概率抗剪承载力模型[J]. 工程力学, 2019, 36(11): 183-194. DOI: 10.6052/j.issn.1000-4750.2018.12.0664
引用本文: 刘圣宾, 凌干展, 余波. 考虑力学机制和不确定性影响的钢筋混凝土柱概率抗剪承载力模型[J]. 工程力学, 2019, 36(11): 183-194. DOI: 10.6052/j.issn.1000-4750.2018.12.0664
LIU Sheng-bin, LING Gan-zhan, YU Bo. PROBABILISTIC SHEAR STRENGTH MODEL OF REINFORCED CONCRETE COLUMNS CONSIDERING MECHANICAL MECHANISM AND UNCERTAINTIES[J]. Engineering Mechanics, 2019, 36(11): 183-194. DOI: 10.6052/j.issn.1000-4750.2018.12.0664
Citation: LIU Sheng-bin, LING Gan-zhan, YU Bo. PROBABILISTIC SHEAR STRENGTH MODEL OF REINFORCED CONCRETE COLUMNS CONSIDERING MECHANICAL MECHANISM AND UNCERTAINTIES[J]. Engineering Mechanics, 2019, 36(11): 183-194. DOI: 10.6052/j.issn.1000-4750.2018.12.0664

考虑力学机制和不确定性影响的钢筋混凝土柱概率抗剪承载力模型

PROBABILISTIC SHEAR STRENGTH MODEL OF REINFORCED CONCRETE COLUMNS CONSIDERING MECHANICAL MECHANISM AND UNCERTAINTIES

  • 摘要: 为了克服传统确定性抗剪承载力模型无法合理考虑不确定性因素影响所存在的缺陷,研究建立了一种能够综合考虑力学机制和不确定性影响的钢筋混凝土(RC)柱概率抗剪承载力模型。首先基于桁架-拱模型,综合考虑混凝土、箍筋和拱作用的抗剪承载力贡献以及不确定性的影响,建立了RC柱概率抗剪承载力模型的解析表达式;然后结合贝叶斯理论和马尔科夫链蒙特卡洛(MCMC)法,确定了概率模型参数的后验分布信息,并分析了概率模型参数的先验分布信息以及更新批次对概率模型参数后验分布的稳定性和收敛性的影响;最后利用试验数据验证了该概率模型的有效性。分析表明,随着试验数据的增加,概率模型参数的后验分布可以实现不断更新;概率抗剪承载力模型不仅可以合理描述抗剪承载力的概率分布特性,而且可以校准分析传统确定性抗剪承载力模型的计算精度。

     

    Abstract: In order to overcome the disadvantage that traditional deterministic shear strength models cannot rationally consider the influence of uncertainties, a probabilistic shear strength model for reinforced concrete (RC) columns was proposed by considering the mechanical mechanism and the influence of uncertainties. The analytical expression of the probabilistic shear strength model for RC columns was established firstly, based on the truss-arch model by taking into account the contributions of concrete, transverse reinforcement and arch action as well as the influence of uncertainties. Then the posterior distribution of probabilistic model parameters was determined by combining the Bayesian theory and the Markov Chain Monte Carlo (MCMC) method. Meanwhile, the influences of the prior distribution and the number of updates on the stability and convergence of the posterior distribution of probabilistic model parameters were investigated. Finally, the applicability of probabilistic shear strength model was verified by comparing with experimental data. The analysis results show that the posterior distribution of probabilistic model parameters will be updated gradually with the increase of experimental data. Moreover, the probabilistic shear strength model is not only able to reasonably describe the probabilistic characteristics of shear strength, but also can calibrate the accuracy of traditional deterministic shear strength models.

     

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