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面向隧道掘进机可掘性评价的多算法融合优化模型及其工程应用.docx

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面向隧道掘进机可掘性评价的多算法融合优化模型及其工程应用 Title:AMulti-AlgorithmFusionOptimizationModelforEvaluatingExcavabilityofTunnelBoringMachinesandItsEngineeringApplications Abstract: Theevaluationofexcavabilityiscrucialindeterminingthefeasibilityandefficiencyoftunnelboringmachine(TBM)operations.Thispaperpresentsamulti-algorithmfusionoptimizationmodelfortheassessmentofTBMexcavability.Theproposedmodelcombinesmultiplealgorithmsandintegratesvariousfactorstoprovideacomprehensiveevaluationoftheexcavabilityoftunnels.Furthermore,theapplicationofthismodelinengineeringprojectsisdiscussed,highlightingitspracticalsignificanceandpotentialforoptimizationintunnelconstruction. 1.Introduction Tunnelconstructionplaysavitalroleinmoderninfrastructuredevelopment.Tunnelboringmachineshavebecomethepreferredchoicefortunnelingduetotheirefficiencyandsafety.TheexcavabilityofTBMsisacriticalfactorindeterminingtheirperformanceandassessingtheoverallfeasibilityoftunnelconstructionprojects.Thispaperaimstoproposeamulti-algorithmfusionoptimizationmodeltoevaluatetheexcavabilityofTBMs,consideringvariousinfluentialfactorsforrobustassessment. 2.LiteratureReview PreviousstudieshavefocusedonevaluatingtheexcavabilityofTBMsusingsinglealgorithmsorlimitedfactors.TheseapproacheshavelimitationsincapturingthecomplexityandvariabilityofTBMperformanceindifferentgeologicalconditions.Toovercometheselimitations,thisresearchproposesafusionoptimizationmodelthatcombinesmultiplealgorithms,suchasartificialneuralnetworks,fuzzylogic,andgeneticalgorithms,toprovideamoreaccurateandcomprehensiveevaluationofTBMexcavability. 3.Methodology Theproposedmulti-algorithmfusionoptimizationmodelintegratesvariousfactorsthataffectTBMexcavability,includingrockmassproperties,TBMspecifications,geologicalconditions,andoperationalconstraints.Themodelisconstructedbasedonthreemainsteps:datacollectionandpreprocessing,algorithmselectionandintegration,andmodelvalidation.Thedatacollectionprocessincludescollectingdataongeologicalconditions,tunneldimensions,TBMspecifications,a