SeismicCollapseSafetyofReinforcedConcreteBuildings.II:ComparativeAssessmentofNonductileandDuctileMomentFramesAbbieB.Liel,M.ASCE1;CurtB.Haselton,M.ASCE2;andGregoryG.Deierlein,F.ASCE3Abstract:Thisstudyisthesecondoftwocompanionpaperstoexaminetheseismiccollapsesafetyofreinforcedconcreteframebuildings,andexaminesnonductilemomentframesthatarerepresentativeofthosebuiltbeforethemid-1970sinCalifornia.Theprobabilisticassessmentreliesonnonlineardynamicsimulationofstructuralresponsetocalculatethecollapserisk,accountingforuncertaintiesinground-motioncharacteristicsandstructuralmodeling.TheevaluationconsidersasetofarchetypicalnonductileRCframestructuresofvaryingheightthataredesignedaccordingtotheseismicprovisionsofthe1967UniformBuildingCode.TheresultsindicatethatnonductileRCframestructureshaveameanannualfrequencyofcollapserangingfrom5to14×10À3atatypicalhigh-seismicCaliforniasite,whichisapproximately40timeshigherthancorrespondingresultsformoderncode-conformingspecialRCmomentframes.Thesemetricsdemonstratetheeffectivenessofductiledetailingandcapacitydesignrequirements,whichhavebeenintroducedoverthepast30yearstoimprovethesafetyofRCbuildings.DataoncomparativesafetybetweennonductileandductileframesmayalsoinformthedevelopmentofpoliciesforappraisingandmitigatingseismiccollapseriskofexistingRCframebuildings.DOI:10.1061/(ASCE)ST.1943-541X.0000275.©2011AmericanSocietyofCivilEngineers.CEDatabasesubjectheadings:Structuralfailures;Earthquakeengineering;Structuralreliability;Reinforcedconcrete;Concretestructures;Seismiceffects;Frames.Authorkeywords:Collapse;Earthquakeengineering;Structuralreliability;Reinforcedconcretestructures;Buildings;Commercial;Seismiceffects.IntroductionReinforcedconcrete(RC)framestructuresconstructedinCalifor-niabeforethemid-1970slackimportantfeaturesofgoodseismicdesign,suchasstrongcolumnsandductiledetailingofreinforce-ment,makingthempotentiallyvulnerabletoearthquake-inducedcollapse.ThesenonductileRCframestructureshaveincurredsignificantearthquakedamageinthe1971SanFernando,1979ImperialValley,1987WhittierNarrows,and1994NorthridgeearthquakesinCalifornia,andmanyotherearthquakesworldwide.ThesefactorsraiseconcernsthatsomeofCalifornia’sapproxi-mately40,000nonductileRCstructuresmaypresentasignificanthazardtolifeandsafetyinfutureearthquakes.However,dataarelackingtogaugethesignificanceofthisrisk,inrelationtoeitherthebuildingpopulationatlargeortospecificbuildings.Thecollapseriskofanindividualbuildingdependsnotonlyonthebuildingcodeprovisionsemployedinitsoriginaldesign,butalsostructuralconfiguration,constructionquality,buildinglocation,andsite-spe-cificseismichazardinformation.Apartfromthechallengesofac-curatelyevaluatingthecollapseriskisthequestionofrisktoleranceandtheminimumlevelofsafetythatisappropriateforbuildings.Inthisregard,comparativeassessmentofbuildingsdesignedaccord-ingtooldversusmodernbuildingcodesprovidesameansofevalu-atingthelevelofacceptableriskimpliedbycurrentdesignpractice.Buildingcoderequirementsforseismicdesignanddetailingofreinforcedconcretehavechangedsignificantlysincethemid-1970s,inresponsetoobservedearthquakedamageandanin-creasedunderstandingoftheimportanceofductiledetailingofreinforcement.IncontrasttooldernonductileRCframes,moderncode-conformingspecialmomentframesforhigh-seismicregionsemployavarietyofcapacitydesignprovisionsthatpreventordelayunfavorablefailuremodessuchascolumnshearfailure,beam-columnjointfailure,andsoft-storymechanisms.Althoughthereisgeneralagreementthatthesechangestobuildingcoderequire-mentsareappropriate,thereislittledatatoquantifytheassociatedimprovementsinseismicsafety.Performance-basedearthquakeengineeringmethodsareappliedinthisstudytoassessthelikelihoodofearthquake-inducedcollapseinarchetypicalnonductileRCframestructures.Performance-basedearthquakeengineeringprovidesaprobabilisticframeworkforre-latingground-motionintensitytostructuralresponseandbuildingperformancethroughnonlineartime-historysimulation(Deierlein2004).TheevaluationofnonductileRCframestructuresisbasedonasetofarchetypicalstructuresdesignedaccordingtothepro-visionsofthe1967UniformBuildingCode(UBC)(ICBO1967).Thesearchetypestructuresarerepresentativeofregularwell-designedRCframestructuresconstructedinCaliforniabetweenapproximately1950and1975.Collapseispredictedthrough1AssistantProfessor,Dept.ofCivil,EnvironmentalandArchitecturalEngineering,Univ.ofColorado,Boulder,CO80309.E-mail:abbie.liel@2AssistantProfessor,Dept.ofCivilEngineering,CaliforniaStateUniv.,Chico,CA95929(correspondingauthor).E-mail:chaselton@3Professor,Dept.ofCivilandEnvironmentalEngineering,StanfordUniv.,Stanford,CA94305.Note.ThismanuscriptwassubmittedonJuly14,2009;approvedonJune30,2010;publishedonlineonJuly15,2010.DiscussionperiodopenuntilSeptember1,2011;separatediscussionsmustbesubmittedforindividualpapers.ThispaperispartoftheJournalofStructuralEngineer-ing,Vol.137,No.4,April1,2011.©ASCE,ISSN0733-9445/2011/4-492–502/$25.00.492/JOURNALOFSTRUCTURALENGINEERING©ASCE/APRIL2011J. Struct. Eng. 2011.137:492-502.Downloaded from by Sultan Qaboos University on 06/21/14. Copyright ASCE. For personal use only; all rights reserved.
博学笃行 自强不息
1
classification
Classification is a fundamental task in machine learning and
data analysis. It involves categorizing data into predefined
classes or categories based on their features or characteristics.
The goal of classification is to build a model that can
accurately predict the class of new, unseen instances.
In this document, we will explore the concept of classification,
different types of classification algorithms, and their
applications in various domains. We will also discuss the
process of building and evaluating a classification model.
I. Introduction to Classification
A. Definition and Importance of Classification
Classification is the process of assigning predefined labels or
classes to instances based on their relevant features. It plays a
vital role in numerous fields, including finance, healthcare,
EfficientVariantsoftheICPAlgorithm
SzymonRusinkiewicz
MarcLevoy
StanfordUniversity
Abstract
TheICP(IterativeClosestPoint)algorithmiswidelyusedforge-
ometricalignmentofthree-dimensionalmodelswhenaninitial
estimateoftherelativeposeisknown.ManyvariantsofICPhave
beenproposed,affectingallphasesofthealgorithmfromthese-
lectionandmatchingofpointstotheminimizationstrategy.We
enumerateandclassifymanyofthesevariants,andevaluatetheir
effectonthespeedwithwhichthecorrectalignmentisreached.
Inordertoimproveconvergencefornearly-flatmesheswithsmall
features,suchasinscribedsurfaces,weintroduceanewvariant
basedonuniformsamplingofthespaceofnormals.Weconclude
byproposingacombinationofICPvariantsoptimizedforhigh
speed.Wedemonstrateanimplementationthatisabletoalign
tworangeimagesinafewtensofmilliseconds,assumingagood
initialguess.Thiscapabilityhaspotentialapplicationtoreal-time
专业术语英译汉
affine 仿射
band 波段
cartography 制图学
clip 剪切
digitizer 数字化仪
DLG 数字线划图
dpi 每英寸点数
edgematching 边缘匹配
equator 赤道
equiarea 等积
geoid 大地水准面
geospatial 地理空间
GPS 全球定位系统
Habitat 栖息地
Interface 接口
Item 项目
Latitude 纬度
legend 图例
longitude 经度
median 中值
meridian 子午线
metadata 元数据
neatline 图廓线
Object-Based 基于对象的
parcel 宗地
photogrammetry 摄影测量
precipitation 降水量
range 范围
raster 栅格
resample 重采样
resolution 分辨率
RMS 均方根
