Risk analysis during tunnel construction using Bayesian NetworksPorto Mctro case studyWord格式文档下载.docx
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Risk analysis during tunnel construction using Bayesian NetworksPorto Mctro case studyWord格式文档下载.docx
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RitaL.Sousa,HerbertH.Einstein⇑
Dept.ofCivilandEnvironmentalEngineering,MassachusettsInstituteofTechnology,Cambridge,USA
articleinfo
Articlehistory:
Received16December2010
Receivedinrevisedform15July2011
Accepted17July2011
Availableonline27August2011
Keywords:
RiskTunneling
BayesianNetworks
abstract
Thispaperpresentsamethodologytosystematicallyassessandmanagetherisksassociatedwithtunnelconstruction.Themethodologyconsistsofcombiningageologicpredictionmodelthatallowsonetopre-dictgeologyaheadofthetunnelconstruction,withaconstructionstrategydecisionmodelthatallowsonetochooseamongstdifferentconstructionstrategiestheonethatleadstominimumrisk.Thismodelusedtunnelboringmachineperformancedatatorelatetoandpredictgeology.BothmodelsarebasedonBayesianNetworksbecauseoftheirabilitytocombinedomainknowledgewithdata,encodedependen-ciesamongvariables,andtheirabilitytolearncausalrelationships.Thecombinedgeologicprediction–constructionstrategydecisionmodelwasappliedtoacase,thePortoMetro,inPortugal.Theresultsofthegeologicpredictionmodelwereingoodagreementwiththeobservedgeology,andtheresultsoftheconstructionstrategydecisionsupportmodelwereingoodagreementwiththeconstructionmethodsused.Verysignificantistheabilityofthemodeltopredictchangesingeologyandconsequentlyrequiredchangesinconstructionstrategy.Thisriskassessmentmethodologyprovidesapowerfultoolwithwhichplannersandengineerscansystematicallyassessandmitigatetheinherentrisksassociatedwithtunnel
construction.
2011ElsevierLtd.Allrightsreserved.
1.Introduction
Thereisanintrinsicriskassociatedwithtunnelconstructionbecauseofthelimitedaprioriknowledgeoftheexistingsubsur-faceconditions.Althoughthemajorityoftunnelconstructionpro-jectshavebeencompletedsafelytherehavebeenseveralincidentsinvarioustunnelingprojectsthathaveresultedindelays,costoverruns,andinafewcasesmoresignificantconsequencessuchasinjuryandlossoflife.Itisthereforeimportanttosystematicallyassessandmanagetherisksassociatedwithtunnelconstruction.Adetaileddatabaseofaccidentsthatoccurredduringtunnelcon-structionwascreatedbySousa(2010).Thedatabasecontains
204casesallaroundtheworldwithdifferentconstructionmeth-odsanddifferenttypesofaccidents.Theaccidentcaseswereobtainedfromthetechnicalliterature,newspapersandcorrespon-dencewithexpertsinthetunnelingdomain.
Knowledgerepresentationsystems(orknowledgebasedsys-tems)anddecisionanalysistechniqueswerebothdevelopedtofacilitateandimprovethedecisionmakingprocess.KnowledgerepresentationsystemsusevariouscomputationaltechniquesofAI(artificialintelligence)forrepresentationofhumanknowledge
⇑Correspondingauthor.Address:
70MassachusettsAve.,Room1-342,Cam-bridgeMA02139,USA.Tel.:
+16172533598;
fax:
+16172536044.
E-mailaddress:
einstein@mit.edu(H.H.Einstein).
andinference.Decisionanalysisusesdecisiontheoryprinciplessupplementedbyjudgmentpsychology(Henrion,1991).Bothemergedfromresearchdoneinthe1940sregardingdevelopmentoftechniquesforproblemsolvinganddecisionmaking.JohnvonNeumannandOscarMorgensten,whointroducedgametheoryin
‘‘GamesandEconomicBehavior’’(1944),hadatremendousimpactonresearchindecisiontheory.
Althoughthetwofieldshavecommonroots,sincethentheyhavetakendifferentpaths.Morerecentlytherehasbeenaresur-genceofinterestbymanyAIresearchersintheapplicationofprob-abilitytheory,decisiontheoryandanalysistoseveralproblemsinAI,resultinginthedevelopmentofBayesianNetworksandinflu-encediagrams,anextensionofBayesianNetworksdesignedtoincludedecisionvariablesandutilities.The1960ssawtheemer-genceofdecisionanalysiswiththeuseofsubjectiveexpectedutil-ityandBayesianstatistics.HowardRaiffa,RobertSchlaifer,andJohnPrattatHarvard,andRonaldHowardatStanfordemergedasleadersintheseareas.ForinstanceRaiffaandSchlaifer’sAppliedStatisticalDecisionTheory(1961)providedadetailedmathemati-caltreatmentofdecisionanalysisfocusingprimarilyonBayesianstatisticalmodels.Prattetal.(1964)developedbasicdecisionanal-ysis.whileEskesenetal.(2004)andHartfordandBaecher(2004)providegoodsummariesonthedifferenttechniques(faulttrees,decisiontrees,etc.)thatcanbeusedtoassessandmanageriskintunneling.
0886-7798/$-seefrontmatter2011ElsevierLtd.Allrightsreserved.doi:
10.1016/j.tust.2011.07.003
Variouscommercialandresearchsoftwareforriskanalysisdur-ingtunnelconstructionhavebeendevelopedovertheyears,themostimportantofwhichistheDAT(DecisionAidsforTunneling),developedatMITincollaborationwithEPFL(EcolePolytechniqueFé
dé
raledeLausanne).TheDATarebasedonaninteractivepro-gramthatusesprobabilisticmodelingoftheconstructionprocesstoanalyzetheeffectsofgeotechnicaluncertaintiesandconstruc-tionuncertaintiesonconstructioncostsandtime.(Dudtetal.,
2000;
Einstein,2002)However,themajorityofexistingriskanaly-sissystems,includingtheDAT,dealonlywiththeeffectsofran-dom(‘‘common’’)geologicalandconstructionuncertaintiesontimeandcostofconstruction.Thereareothersourcesofrisks,notconsideredinthesesystems,whicharerelatedtospecificgeo-technicalscenariosthatcanhavesubstantialconsequencesonthetunnelprocess,eveniftheirprobabilityofoccurrenceislow.
Thispaperattemptstoaddresstheissueofspecificgeotechnicalriskbyfirstdevelopingamethodologythatallowsonetoidentifymajorsourcesofgeotechnicalrisks,eventhosewithlowprobabil-ity,inthecontextofaparticularprojectandthenperformingaquantitativeriskanalysistoidentifythe‘‘optimal’’constructionstrategies,where‘‘optimal’’referstominimumrisk.Forthatpur-poseadecisionsupportsystemframeworkfordeterminingthe
‘‘optimal’’(minimumrisk)constructionmethodforagiventunnel
Fig.1.BayesianNetworkexample.
therelationsbetweenvariables.Inthisexamplethearrowfrom
CtoB2meansthatChasadirectinfluenceonB2.
Specifically,aBayesianNetworkisacompactandgraphicalrep-resentationofajointdistribution,basedonsomesimplifyingassumptionsthatsomevariablesareconditionallyindependentofothers.AsaresultthejointprobabilityofaBayesianNetworkoverthevariablesU={X1,...,Xn},representedbythechainrulecanbesimplifiedfrom:
n
Y
alignmentwasdeveloped.Thedecisionsupportsystemconsistsoftwomodels:
ageologicpredictionmodel,andaconstructionstrat-egydecisionmodel.BothmodelsarebasedontheBayesianNet-
Pð
UÞ
¼
i
to
Xijx1;
...;
xi1Þ
worktechnique,andwhencombinedallowonetodeterminethe
QnPð
X¼
xjparentsð
XÞ
Þ
where‘‘parents(X)’’isthe
‘optimal’tunnelconstructionstrategies.Thedecisionmodelcon-
parentsetof
iiii
Xi.
tainsanupdatingcomponent,byincludinginformationfromthe
excavatedtunnelsections.Thissystemwasimplementedinarealtunnelproject,thePortoMetroinPortugal.
2.BackgroundonBayesianNetworks
BayesianNetworksaregraphicalrepresentationsofknowledgeforreasoningunderuncertainty.Theycanbeusedatanystageofariskanalysis,andmaysubstitutebothfaulttreesandeventtreesinlogicaltreeanalysis.Whilecommoncauseormoregeneraldepen-dencyphenomenaposesignificantcomplicationsinclassicalfaulttreeanalysis,thisisnotthecasewithBayesianNetworks.Theyareinfactdesignedtofacilitatethemodelingofsuchdependen-cies.Becauseofwhathasbeenstated,BayesianNetworksprovideagoodtoolfordecisionanalysis,includingprioranalysis,posterioranalysisandpre-posterioranalysis.Furthermore,theycanbeex-tendedtoinfluencediagrams,includingdecisionandutilitynodesinordertoexplicitlymodeladecisionproblem.
ABayesianNetworkisaconcisegraphicalrepresentationofthe
jointprobabilityofthedomainthatisbeingrepresentedbythe
ItisthispropertythatmakesBayesianNetworksaverypower-fultoolforrepresentingdomainsunderuncertainty,allowingonetostoreandcomputethejointandmarginaldistributionsmoreefficiently.
InordertoobtainresultsfromBayesianNetworksonedoesinference.ThisconsistsofcomputinganswerstoqueriesmadetotheBayesianNetwork.Thetwomostcommontypesofqueriesare:
–Aprioriprobabilitydistributionofavariable
AÞ
X...XPð
X1;
Xk;
AÞ
ð
1Þ
X1Xk
whereAisthequery-variableandX1toXkaretheremainingvariablesofthenetwork.Thistypeofquerycanbeusedduringthedesignphaseofatunnelforexampletoassesstheproba-bilityoffailureunderdesignconditions(geology,hydrology,etc.).
–Posteriordistributionofvariablesgivenevidence
(observations)
A;
eÞ
randomvariables,consistingof(RusselandNorvig,2003):
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