基于多类数据处理方法联合分析的运营桥梁安全性预警分级

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MA Yaohua

（China National Chemical Construction Investment Group Co.，Ltd.，Beijing 10230O，China）

Abstract：：Inorder toreasonablyrealize thesafetyearlywarningclasificationofoperationalbridges，combinedwith the measureddataof denoising processing，the corresponding earlywarning criteria wereconstructedbased ontheaccumulated deformation sequence，rate sequence andacceleration sequence，torealize the multi-source information fusionof he safety earlywarningclasificationofoperational bridges，andfullyensure theaccuracyoftheclassificationresults.Theresultsshow thatPSO-DVMDmodelcanefctivelyremoverandom noisefrombridge deformationdataand issuitable fordenoising bridge deformationdata.Therearesomediffrencesintheearlywarning levelsofdiferentmonitoring pointsormonitoring itemsunder diferentcriteria，andthesafetyearlywarninglevelsofbridgesaredeterminedcomprehensivelyacordingtotheunfavorable principle.Theearly-waringclassficationofoperationalbridgesprovidesaquantitativeclassficationstandardforthesafety evaluation of operational bridges，which is worthy of further popularization and application.

Keywords： bridges； denoising； security early warning； correlation vector machine； trend judgment

0 引言

目前，桥梁工程数量日益增加，其建成后的运营安全显得格外重要，因此，开展运营桥梁的安全性预警分级研究具有重要意义[1-2]。（剩余8308字）