无粘结全预应力砼梁预应力损失的贝叶斯估计

提出了用于无粘结全预应力砼梁中预应力损失检测的贝叶斯概率法。研究了预应力筋在转动约束下自由振动特征,求得频率方程,以等效刚度的形式实现其在有限元中的建模。考虑测试结果的噪声影响和结构参数的随机性,利用自适应的马尔科夫链蒙特卡罗抽样技术(AM—MCMC)对梁的有限元模型进行了模型修正,构造出不相关的结构参数样本序列。在获取其后验分布统计特征的基础上预测了梁预应力损失的分布范围。数值模拟结果表明:AM—MCMC抽样技术确保了样本序列的混合能力。选择适当的抽样间隔可以减小样本序列之间的自相关性。对于不同的预应力水平,预测的预应力损失统计均值和试验值之间误差均小于6%。

BAYESIAN ESTIMATE OF PRESTRESS LOSE FOR UNBONDED FULL PRESTRESSED CONCRETE BEAM

A Bayesian probabilistic methodology to detect prestress lose of unbonded full prestressed concrete beam is presented.Free vibration of prestressing tendon with elastic rotational constraints was studied,transcendental equation of natural frequencies was derived,modeling of prestressing tendons was releasized in form of equivalent flexural rigidity in finite element model of beam.Because of noise existentence in test and randomness of material performance test,Markov chain Monte Carlo sampling technology based on adaptive Metropolis algorithm(AM—MCMC) was employed to update the finite element model in probablistic framework,the uncorrelated chain sequences were constructed for structural parameters,and distribution range of prestress lose under different prestress levels were obtained based on posterior estimates of structural parameters.The numerical results indicate that AM-MCMC sampling techonology assure the mixture capacity of chain sequences,and proper selection of sampling interval will decrease the autocorrelection effect of samplings.For different prestressing force levels,errors between the predicted stastististical means of prestress lose and test ones are less than 6%.