吴涛, 黄凯, 刘喜, 刘伯权, 黄华. 钢筋混凝土深受弯构件受剪承载力概率模型研究[J]. 工程力学, 2015, 32(11): 210-217. DOI: 10.6052/j.issn.1000-4750.2014.05.0362
引用本文: 吴涛, 黄凯, 刘喜, 刘伯权, 黄华. 钢筋混凝土深受弯构件受剪承载力概率模型研究[J]. 工程力学, 2015, 32(11): 210-217. DOI: 10.6052/j.issn.1000-4750.2014.05.0362
WU Tao, HUANG Kai, LIU Xi, LIU Bo-quan, HUANG Hua. STUDY ON PROBABILISTIC SHEAR STRENGTH MODEL FOR DEEP FLEXURAL MEMBER[J]. Engineering Mechanics, 2015, 32(11): 210-217. DOI: 10.6052/j.issn.1000-4750.2014.05.0362
Citation: WU Tao, HUANG Kai, LIU Xi, LIU Bo-quan, HUANG Hua. STUDY ON PROBABILISTIC SHEAR STRENGTH MODEL FOR DEEP FLEXURAL MEMBER[J]. Engineering Mechanics, 2015, 32(11): 210-217. DOI: 10.6052/j.issn.1000-4750.2014.05.0362

钢筋混凝土深受弯构件受剪承载力概率模型研究

STUDY ON PROBABILISTIC SHEAR STRENGTH MODEL FOR DEEP FLEXURAL MEMBER

  • 摘要: 贝叶斯理论具有充分利用模型信息和数据信息且考虑先验分布等优点,已被广泛应用于各个领域。通过贝叶斯多元线性参数估计方法,建立基于影响参数的钢筋混凝土深受弯构件贝叶斯概率抗剪模型。基于该模型和271组钢筋混凝土深受弯构件试验结果,完成了模型参数计算及基于贝叶斯理论的受剪承载力计算,同时,利用贝叶斯参数剔除法对抗剪模型进行简化,并与我国混凝土结构设计规范(GB50010-2010)、ACI318-08、CSA和EC2等现有规范计算结果进行了对比分析。研究表明:利用贝叶斯方法建立的基于影响参数的深受弯构件抗剪模型计算结果与试验吻合良好,采用贝叶斯动态更新后结果较规范值更接近试验值,简化后模型能较为合理的进行深受弯构件受剪承载力计算。

     

    Abstract: Bayesian theory is widely used in various fields, for its advantages that not only the model information and data information, but also transcendental information is used adequately. A shear model for reinforced concrete deep flexural members based on influence parameters is developed by using Bayesian multivariate statistical theory and 271 test results collected. The model is simplified through a removal process of Bayesian parameters. The performance of the developed probabilistic model is confirmed by comparison with the shear strengths predicted by the shear models in codes of GB 50010-2010, ACI318-08, CSA and EC2. Good agreements between the shear strengths obtained by the simplified model is achieved, based on Bayesian and test results. And the results updating Bayesian dynamics are closer to the tests than the codes. The simplified model can be used in calculating shear strength for deep flexural members.

     

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