| 刘佩,袁泉,魏庆朝.基于贝叶斯理论的恢复力模型参数识别方法[J].计算力学学报,2013,30(5):621~626 |
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| 基于贝叶斯理论的恢复力模型参数识别方法 |
| Parameter identification methodology of restoring force model based on Bayesian theorem |
| 投稿时间：2012-06-18 修订日期：2012-09-25 |
| DOI：10.7511/jslx201305005 |
| 中文关键词: 贝叶斯理论 恢复力模型 参数识别 密肋复合墙体 模型误差 |
| 英文关键词:Bayesian theorem restoring force model parameter identification multi-grid composite walls prediction model error |
| 基金项目:国家自然科学基金青年科学基金(51208030);北京交通大学人才基金(C12RC00040)资助项目. |
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| 中文摘要: |
| 提出了基于贝叶斯理论的恢复力模型参数识别方法,该方法考虑了模型误差的影响,结合实测滞回曲线数据,不仅可以得到模型参数的最有可能值,而且可以得到模型参数的定量的不确定性。以密肋复合墙体在低周反复荷载作用下所得滞回曲线为例,提出了可考虑刚度降低、捏拢滑移及极限荷载后强度降低现象的恢复力模型,建立了基于贝叶斯理论的恢复力模型参数识别计算框架,推导得到了模型参数的负对数似然函数,据此可得到模型参数的最有可能值及协方差矩阵。对标准密肋复合墙体预制试件和现浇试件的恢复力模型参数进行了识别,将根据模型参数最有可能值得到的滞回曲线及根据模型参数最有可能值及协方差矩阵得到的骨架曲线,与相应的实测值进行了对比,验证了所提方法的可行性及识别结果的合理性,更新的模型参数概率分布可用于后续的抗震风险评估。 |
| 英文摘要: |
| Restoring force model parameter identification method based on Bayesian theorem is proposed in this paper,which takes account of the model prediction error.Using the tested hysteretic data,not only the most probable value but also the quantitative uncertainty of the model parameters can be obtained.Take the tested hysteresis curves of multi-grid composite walls under low cyclic loadings for instance,a restoring force model for multi-grid composite walls is proposed.Effects of stiffness degradation,strength degradation after ultimate load,and pinching are taken into account.Bayesian computational frame for parameter identification of restoring force model is proposed.Negative log-likelihood function of model parameter vector is derived,and then expressions of most probable value and covariance matrix of the parameter vector can be derived.Restoring force model parameters of precast multi-grid composite wall specimen and cast-in-place multi-grid composite wall specimen are identified.Hysteresis curves obtained through the most probable values,and backbone curves obtained through the most probable values and covariance matrixes are compared with the corresponding test data,which validate the proposed method and the identification results.The updated probabilistic density function of model parameters can be used for seismic risk assessment in future. |
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