桥梁极值应力的贝叶斯动态耦合线性预测

Bayesian Dynamic Coupled Linear Prediction of Bridge Extreme Stresses

摘要:

基于含有趋势性和随机性的监测极值应力时间序列数据，进行了桥梁极值应力的贝叶斯动态耦合线性预测。将历史监测耦合极值应力数据解耦，建立解耦极值应力的动态耦合线性模型（DCLM)，并将其分解为有限个动态线性模型(DLMs)。基于动态监测的解耦极值应力数据，结合贝叶斯方法，进行动态耦合线性模型的概率递推，进而实现桥梁解耦极值应力的动态预测，并将预测的解耦极值应力进行相加，即可实现桥梁极值应力的动态预测。最后，通过在役桥梁的含有随机性和趋势性的监测极值应力数据，利用ARIMA（auto-regressive integrated moving average）模型，验证了所提方法的合理性与有效性。

Abstract:

Based on the monitoring extreme stress time series data with trend and randomness, the Bayesian dynamic coupled linear prediction for bridge extreme stresses is studied in this paper. Firstly, with the decoupled historical extreme stress data, the dynamic coupled linear model(DCLM) and the corresponding finite dynamic linear models(DLMs） are respectively built. Then, based on the dynamic monitoring decoupled extreme stress data, the probability recursive processes of the built dynamic coupled linear model are proposed with Bayesian method, which can be used to dynamically predict bridge decoupled extreme stresses. Further, the sums of the predicted decoupled extreme stresses can be considered as the predicted results of the bridge extreme stresses. Finally, based on the monitoring extreme stress data with trend and randomness from an existing bridge, the feasibility and application of the proposed method is verified through comparing with auto-regressive integrated moving average (ARIMA) model.