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基于卡尔曼滤波的优化GM(1,1)模型在建筑物沉降预测中的应用.docx

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基于卡尔曼滤波的优化GM(1,1)模型在建筑物沉降预测中的应用 Abstract Buildingsettlementisacrucialproblemintheconstructionindustry.Predictingthesettlementofbuildingsisessentialtoensurethesafety,stability,anddurabilityofthebuildings.Manymethodshavebeenproposedtopredictbuildingsettlement,outofwhichtheGreySystemTheory(GST)hasbeenwidelyusedduetoitssimplicityandhighaccuracy.However,theGM(1,1)modelofGSThassomedrawbacks,suchasover-dependencyontheinitialvalueofthedataseries,andsensitivitytonoise.Toovercometheseshortcomings,theGM(1,1)modelisoptimizedusingKalmanfilteringinthepresentstudy. Theproposedmodelisappliedtothesettlementpredictionofbuildingsthroughexperimentaldatacollectedfromaconstructionprojectsite.TheresultsofthestudyshowthattheoptimizationoftheGM(1,1)modelusingKalmanFilteringsignificantlyimprovesitsaccuracy.Thefindingsofthisstudyalsodemonstratethattheproposedmodelcaneffectivelyachievelong-termsettlementpredictionofbuildingsandthemodelcanbeusedasanefficienttoolforpredictingbuildingsettlementintheconstructionindustry. Keywords:BuildingSettlement,GreySystemTheory,GM(1,1)model,KalmanFiltering,Long-TermPrediction. Introduction Settlementpredictionisacriticalissueintheconstructionindustry,asitaffectsthelong-termstability,safety,anddurabilityofbuildingsandstructures.Settlementoccursasaresultoftheloadimposedonthebuilding'sfoundation,whichcausesthesoiltocompressandsettle.Ifthesettlementisnotpredictedaccurately,itcanleadtostructuraldamage,safetyhazards,andevenbuildingcollapse.Therefore,predictingthesettlementofbuildingsiscrucialforensuringthelongevityofbuildingsandstructures. TheGreySystemTheory(GST)isadata-drivenmethodthathasbeenwidelyusedforpredictingthebehaviorofcomplexsystems,includingbuildingsettlement.TheGSTisbasedontheprincipleofreducingtheuncertaintyofanunknownsystembytransformingitintoaknownsystemthroughdataprocessing.However,thestandardGM(1,1)modelofGSThassomelimitations,suchasinitialvaluedependencyandsensitivitytonoise. Toovercomethesedrawbacks,theGM(1,1)modelisoptimizedusingKalmanFiltering.