应用新数据同化算法的桥梁极值应力预测

doi:10.13229/j.cnki.jdxbgxb20181017

摘要/Abstract

摘要：

为合理动态预测桥梁极值应力，将极值应力监测数据视为时间序列，提出桥梁极值应力预测的数据同化新方法。利用极值应力监测数据建立动态非线性模型，引入 K均值聚类算法与最大期望算法，并将两者融合嵌入到高斯混合粒子滤波器，得到改进高斯混合粒子滤波算法，结合监测数据实现桥梁极值应力的动态预测，通过在役桥梁监测数据对所提算法的合理性进行验证，并与其他预测算法进行比较，结果发现改进算法更具可行性且预测精度较高。

关键词: 结构工程, 动态非线性模型, 高斯混合粒子滤波器, 桥梁极值应力预测

Abstract:

To reasonably and dynamically predict the extreme stresses of service-bridge structure, the monitored extreme stress data is taken as a time series, a new data assimilation algorithm about bridge extreme stresses prediction is proposed. The dynamic nonlinear model is built with the monitored extreme stress data of bridges, and K-MEANS algorithm and Expectation Maximization (EM) algorithm are introduced and embedded in the Gaussian Mixed Particle Filter (GMPF), then the Improved Gaussian Mixed Particle Filter (IGMPF) prediction approach can be obtained. The structural stresses are dynamically predicted based on the monitored extreme stress data. The monitored stress data of an actual bridge is provided to illustrate the feasibility of the proposed method, which shows that the improved algorithm is more feasible and accurate than the other algorithms.

Key words: structural engineering, dynamic nonlinear model, Gaussian mixed particle filter, dynamic prediction of bridge extreme stresses

中图分类号:

TU391

樊学平,屈广,刘月飞. 应用新数据同化算法的桥梁极值应力预测[J]. 吉林大学学报(工学版), 2020, 50(2): 572-580.

Xue-ping FAN,Guang QU,Yue-fei LIU. Bridge extreme stress prediction based on new data assimilation algorithm[J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(2): 572-580.

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