贝叶斯更新广义概率密度演化方程的桥梁健康监测数据动态预测

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关键词：健康监测；动态预测；贝叶斯递推；概率密度演化；粒子滤波 中图分类号： U441+.2 ;TU392.5 文献标志码：A DOI：10.16385/j.cnki.issn.1004-4523.202401046

Bayesian updating of generalized probability density evolution equations for dynamic prediction of bridge health monitoring data

ZHOU Heng'， FAN Xueping1²，LIU Yuefei1.2 （1.School of Civil Engineering and Mechanics，Lanzhou University，Lanzhou 73oooo，China; 2.KeyLaboratoryofMechanics onDisasterand Environment in Western Chinaofthe MinistryofEducation， LanzhouUniversity，Lanzhou 73oooo，China）

Abstract：Toadresstechallngeofachievingaccuraterealtimepredictionforthemassvedatacollectedthroughbridgehealth monitorig，thisstudyintroducesanovelfilteringmetodtoestablishadynamicpredictionmodelfortemonitoringdatafaciliatigthe dynamicpredictioproces.LeveragigtebenefitsofBayesianreusionfouncertaityaalysis，thestudyderivsgeneralidprbability densityevolutionequationsforboththesystemstatesandobsevedvariables.Theanalyticalsolutionisobtainedbasedontheestablished dynamiclinearodel，whichisutildtostiateteprioidistributionoftsstestate.Subseqentlyemploingthetofartice filtering，thystatetetebtofttateabgthsealatioofacdicto TheaplicabiltndasibilityofteproposdmtodelstratedwithontdstrsdstrandatafotidgsCompnih alternative data prediction methods underscores the superior validity and prediction accuracy of the proposed model.

Keywords： health monitoring;dynamic prediction; Bayesian recursion; probability density evolution;particle filtering

现如今，越来越多的传感器网络对桥梁系统进行实时监测，通过监测结构服役阶段的结构响应和环境变量，运用有效的信号处理方法能够从监测数据中提取反映结构健康状态的特征量，进而对结构的服役性能进行合理评估。（剩余13022字）