Structural Health Monitoring Based on Data Science Techniques PDF
Preview Structural Health Monitoring Based on Data Science Techniques
Structural Integrity 21 Series Editors: José A. F. O. Correia · Abílio M. P. De Jesus Alexandre Cury · Diogo Ribeiro · Filippo Ubertini · Michael D. Todd Editors Structural Health Monitoring Based on Data Science Techniques Structural Integrity Volume 21 SeriesEditors JoséA.F.O.Correia,FacultyofEngineering,UniversityofPorto,Porto,Portugal AbílioM.P.DeJesus,FacultyofEngineering,UniversityofPorto,Porto,Portugal AdvisoryEditors MajidRezaAyatollahi,SchoolofMechanicalEngineering,IranUniversityof ScienceandTechnology,Tehran,Iran FilippoBerto,DepartmentofMechanicalandIndustrialEngineering,Facultyof Engineering,NorwegianUniversityofScienceandTechnology,Trondheim, Norway AlfonsoFernández-Canteli,FacultyofEngineering,UniversityofOviedo,Gijón, Spain MatthewHebdon,VirginiaStateUniversity,VirginiaTech,Blacksburg,VA,USA AndreiKotousov,SchoolofMechanicalEngineering,UniversityofAdelaide, Adelaide,SA,Australia GrzegorzLesiuk,FacultyofMechanicalEngineering,WrocławUniversityof ScienceandTechnology,Wrocław,Poland YukitakaMurakami,FacultyofEngineering,KyushuUniversity,Higashiku, Fukuoka,Japan HermesCarvalho,DepartmentofStructuralEngineering,FederalUniversityof MinasGerais,BeloHorizonte,MinasGerais,Brazil Shun-PengZhu,SchoolofMechatronicsEngineering,UniversityofElectronic ScienceandTechnologyofChina,Chengdu,Sichuan,China StéphaneBordas,UniversityofLuxembourg,ESCH-SUR-ALZETTE, Luxembourg NicholasFantuzzi ,DICAMDepartment,UniversityofBologna, BOLOGNA,Bologna,Italy LucaSusmel,CivilEngineering,UniversityofSheffield,Sheffield,UK SubhrajitDutta,DepartmentofCivilEngineering,NationalInstituteofTechnology Silchar,Silchar,Assam,India PavloMaruschak,TernopilIPNationalTechnicalUniversity,Ruska,Ukraine ElenaFedorova,SiberianFederalUniversity,Krasnoyarsk,Russia TheStructuralIntegritybookseriesisahighlevelacademicandprofessionalseries publishing research on all areas of Structural Integrity. It promotes and expedites the dissemination of new research results and tutorial views in the structural integrityfield. The Series publishes research monographs, professional books, handbooks, edited volumes and textbooks with worldwide distribution to engineers, researchers,educators,professionalsandlibraries. Topicsofinterestedincludebutarenotlimitedto: – Structuralintegrity – Structuraldurability – Degradationandconservationofmaterialsandstructures – Dynamicandseismicstructuralanalysis – Fatigueandfractureofmaterialsandstructures – Riskanalysisandsafetyofmaterialsandstructuralmechanics – FractureMechanics – Damagemechanics – Analyticalandnumericalsimulationofmaterialsandstructures – Computationalmechanics – Structuraldesignmethodology – Experimentalmethodsappliedtostructuralintegrity – Multiaxialfatigueandcomplexloadingeffectsofmaterialsandstructures – Fatiguecorrosionanalysis – Scaleeffectsinthefatigueanalysisofmaterialsandstructures – Fatiguestructuralintegrity – Structuralintegrityinrailwayandhighwaysystems – Sustainablestructuraldesign – Structuralloadscharacterization – Structuralhealthmonitoring – Adhesivesconnectionsintegrity – Rockandsoilstructuralintegrity. **Indexing:ThebooksofthisseriesaresubmittedtoWebofScience,Scopus, GoogleScholarandSpringerlink** This series is managed by team members of the ESIS/TC12 technical committee. Springer and the Series Editors welcome book ideas from authors. Potential authors who wish to submit a book proposal should contact Dr. Mayra Castro, SeniorEditor,Springer(Heidelberg),e-mail:[email protected] Moreinformationaboutthisseriesathttp://www.springer.com/series/15775 · · · Alexandre Cury Diogo Ribeiro Filippo Ubertini Michael D. Todd Editors Structural Health Monitoring Based on Data Science Techniques Editors AlexandreCury DiogoRibeiro DepartmentofAppliedandComputational DepartmentofCivilEngineering,School Mechanics ofEngineering FederalUniversityofJuizdeFora PolytechnicInstituteofPorto JuizdeFora,Brazil Porto,Portugal FilippoUbertini MichaelD.Todd DepartmentofCivilandEnvironmental DepartmentofStructuralEngineering Engineering UniversityofCaliforniaSanDiego UniversityofPerugia LaJolla,CA,USA Perugia,Italy ISSN2522-560X ISSN2522-5618 (electronic) StructuralIntegrity ISBN978-3-030-81715-2 ISBN978-3-030-81716-9 (eBook) https://doi.org/10.1007/978-3-030-81716-9 ©TheEditor(s)(ifapplicable)andTheAuthor(s),underexclusivelicensetoSpringerNature SwitzerlandAG2022 Thisworkissubjecttocopyright.AllrightsaresolelyandexclusivelylicensedbythePublisher,whether thewholeorpartofthematerialisconcerned,specificallytherightsoftranslation,reprinting,reuse ofillustrations,recitation,broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,and transmissionorinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilar ordissimilarmethodologynowknownorhereafterdeveloped. Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublication doesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevant protectivelawsandregulationsandthereforefreeforgeneraluse. Thepublisher,theauthorsandtheeditorsaresafetoassumethattheadviceandinformationinthisbook arebelievedtobetrueandaccurateatthedateofpublication.Neitherthepublishernortheauthorsor theeditorsgiveawarranty,expressedorimplied,withrespecttothematerialcontainedhereinorforany errorsoromissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregardtojurisdictional claimsinpublishedmapsandinstitutionalaffiliations. ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland Foreword Onecanspeculatethattherehasbeenaninterestindetectingdamageinengineered systemssincemanhasusedtools.Overtime,earlyadhocqualitativedamagedetec- tion procedures, many of which were vibration-based, evolved into more refined approachesthatbecamewhatweknowtodayasnon-destructiveevaluation(NDE) methods. One drawback of most NDE methods is that the system being inspected must be taken out of service and often disassembled before such methods can be used.Structuralhealthmonitoring(SHM)attemptstoaddressthisshortcomingby developing more continuous, automated in situ damage detection capabilities that strivestominimizethehuman-in-loopaspectoftheassessmentprocess. The term structural health monitoring begins to appear regularly in the tech- nicalliteraturearoundthelate1980sandearly1990s.Theseearlystudiesfocused primarilyondeterministic,inversephysics-basedmodelingapproachesthatidenti- fiedthepresence,location,andextentofdamage.Whenresearchersandpractitioners attemptedtoapplysuchmethodstoinsitustructures,variouslimitationswereiden- tifiedincludingdifficultieshandlingthemismatchbetweenmeasuredandanalytical degreesoffreedom,thealmostexclusiveuseoflinearmodelswhensimulatingboth theundamagedanddamagedsystemresponse,andtheinabilitytohandletheopera- tionalandenvironmentalvariabilitythatallreal-worldsystemsexperience.Thelatter limitationassociatedwiththefactthatoperationalandenvironmentalvariabilitywill causechangesintheSHMsystemsensorreadingsandthesechangesmustbedistin- guishedfromchangesinsensorreadingcausedbydamagehasproventobeoneofthe mostsignificantchallengesassociatedwithtransitioningSHMresearchtopractice. In the late 1990s and early 2000s, various research groups started to recog- nize that SHM is not a deterministic problem. Instead, they proposed to address SHM through more data-driven approaches based on general statistical-pattern- recognition-basedmethodologies.Althoughmanyvariationsofthisstatisticalpattern recognitionapproachhavebeenproposedindifferentSHMstudies;almostallencom- passthreecommoncomponents:1.Adeployedsensingsystemtypicallymonitoring kinematic response quantities; 2. the extraction of damage-sensitive features from therawsensordata;and3.thestatisticalclassificationofthosefeaturesintodamage andundamagedcategories.Acommonmisconceptionwiththeseapproachesisthat v vi Foreword theyprecludetheuseofphysics-basedmodelswhen,infact,thepatternrecognition willalwaysbeimprovedwhenitisbasedonknowledgeofthephysicsgoverningthe systemresponseinbothitsundamagedanddamagedstates. This paradigm shift from inverse deterministic modeling to statistical-pattern- recognition-basedSHMbegantheprocessofadoptingmanydata-drivenalgorithms from disparate fields such as radar and sonar detection, machine learning, speech- patternrecognition,statisticaldecisiontheoryandeconometricstotheSHMproblem. Inaggregate,thesefieldsrepresentcomponentsofthemoregeneralfieldreferredto asdatascience.ThisfocusonapplyingelementsofdatasciencetoSHMhasmostly replaced the earlier deterministic inverse modeling approaches. Furthermore, data scienceoffersapproachesthatcanbetteraddresstherandomandsystematicchanges insensormeasurementscausedbyoperationalandenvironmentalvariabilityandcan produceaquantifiedprobabilityofdetectionmeasure.Bothattributesareessential fortheadaptationofSHMbyassetownersandregulatoryagencies. Currently, all scientific and engineering fields are benefitting from the rapid advances in data science and the associated availability of general software tools forimplementingthesealgorithms.Thefieldofstructuralhealthmonitoringisone suchbeneficiary.However,asinothertechnicalfields,innovationandapplication- specificknowledgearerequiredtoeffectivelyadaptthesegeneraltoolstodomain- specificproblems.Thisbookprovidesnumerousexamplesfromaerospace,civil,and mechanical engineering applications that demonstrate how SHM researchers have taken the tools of data science and creatively adapted them toaddress many prob- lemsthathavebeenlimitingthemorewidespreadadaptationofSHMbyindustry. Thechaptersinthisbookshowthebreadthofdatasciencemethodologiesthatcan beappliedtoSHM.Furthermore,thesechaptersdemonstratethatadvancesindata sciencecanimpacteveryaspectofaSHMprocess.Assuch,thisbookwillprovide experiencedresearchersnewtothedatasciencefieldanoverviewofhowsuchtools canbeusedinadamagedetectioncontext.Additionally,thisbookwillprovidethose just beginning their technical careers with ideas for new research and application directionstopursueandtheassociatedtechnologiestheywillneedtolearnthatwill bethefoundationformakingfutureadvancesinSHM. Dr.CharlesFarrar LosAlamosNationalLaboratory LosAlamos,NewMexico,USA Preface Structuralhealthmonitoring(SHM)maybedefinedasthegeneralprocessofmaking an assessment, based on appropriate analyses of in situ measured data, about the current ability of a structural component or system to perform its intended design function(s)successfully.AsuccessfulSHMstrategymayenablesignificantowner- ship cost reduction in a life cycle perspective through maintenance optimization, performancemaximizationduringoperation,unscheduleddowntimeminimization, and/orenablesignificantlifesafetyadvantagethroughcatastrophicfailuremitigation. Broadlyspeaking,SHMstrategiesformostapplicationsnecessarilyintegratereal- timedataacquisition,featureextractionfromtheacquireddata,statisticalmodeling ofthefeatures,andclassificationofthefeaturestomakeinformeddecisions;theulti- mateglobalgoalofSHMsystemsistodirecteconomicallyefficientand/orsafety- maximized structural health decision making for the general purpose of long-term effectivelifecyclemanagement. AnexplosionofapproachesthataddresssomeorpartofthisoverallSHMstrategy has occurred in recent years, across many different structural applications ranging from civil to aerospace to industrial/mechanical systems. A significant fraction of this growth has been fueled by ubiquitous “Internet of Things (IoT)” data streams from diverse sources, advances in computing such as cloud computing, and the adoption and development of advanced analytics techniques drawn from machine learninganddatascience.ThisdomainofadvancementinSHMcanaddresssome of the paramount challenges in long-term monitoring of civil structures such as, butnotlimitedto,(i)structuralcomplexity,(ii)operationalandenvironmentalvari- ability (e.g., loading conditions, operating environment), (iii) complex, intercon- necteddegradationandfailuremodes,(iv)challengesinmonitoringverylarge-scale structureswithpotentiallylocalizedfailuremodes(e.g.,pittingcorrosion),and(v) datareliabilityandsecurity,includinglong-termfunctionalitiesofsensornetworks. Damageidentificationaswellascontinuousconditionmonitoringareamongthe mostimportantaspectsrelatedtoproperoperationofstructuralsystemstoensuretheir integrity,safety,anddesirableoperationalproperties.Inrecentyears,anexponential developmentofdamageidentificationmethodsaswellasconditionmonitoringhas been observed. The degradation process of structural systems is usually due to a vii viii Preface combinationofreasons,suchasmaterialsaging,ineffectivemaintenance,designor constructiveissues,unexpectedloadingevents,naturalhazards(e.g.,earthquakes), andmore. Mostdamageidentificationstrategiesaredevelopedprimarilybasedonthesignals monitored over time, often seeking for an effective fusion between heterogeneous sensordata,suchas,historyofstructuralaccelerations,displacements,strains,time seriesofenvironmentalparameters,andmore.However,withtheevolutionofcompu- tationalandinformationtechnologies,remarkableimprovementsarebeingobserved indataacquisitionsystems,which,inturn,demandfurtherdevelopmentofstructural monitoringtoolsandtechniquestodealwithlargevolumesofdata.Hence,analyses thatwereearlierperformedincipientlywithareducednumberofvariables,i.e.,by meansofmodaland/orprobability/statisticalanalyses,nowarebeingautomatically carried out with the aid of powerful machine learning methods, such as artificial neuralnetworksandsupportvectormachines. One observes, however, that some key aspects still play major roles on the performance of damage identification algorithms applied to large-scale structural systems: (i) the high dimensionality of the parameters monitored; (ii) environ- mental/operationalfactors,suchastemperature,humidity,andtraffic;(iii)structural complexity;(iv)reliabilityofthemeasureddata;(v)lowsensitivityofglobalstruc- turalresponsetolocaldamage;and(vi)theneedtointegratephysical/engineering knowledge in machine learning algorithms enabling an effective SHM data to decisionsprocess. This book has 22 chapters and contains a representative collection of actual uses of data science in SHM, ranging from civil to mechanical/aerospace system applications. Chapters 1–3 cover different Bayesian-based strategies for structural damage detection. Chapters 4–8 address the use of data-driven techniques and their aptness for real-time structural condition assessment, especially considering raw vibration measurements as inputs. Chapters 9 and 10 continue this discus- sionbybringingphysics-based andreduced ordermodeling aspects intotheSHM paradigm. Chapter 11 discusses how deep learning can assist image processing for increasing safety in construction sites. Chapters 12–15 consider the influence ofboth environmental and operational effects and presentstrategies tocircumvent themwhenitcomestostructuraldamagedetection.Chapters16–20exploresome recent concepts regarding explainable artificial intelligence, physics-informed and interpretablemachinelearning,aswellasnoveldevelopmentsinvolvingpopulation- basedSHM.Chapters21and22concludethisbookwithanoverviewofstructural damagedetectionviaremotelysenseddataandwithadiscussionaboutnewdesigns forSHMsystems. Preface ix Insummary,thisbookisaddressedtoscientists,engineers,designers,technicians, stakeholders,andcontractorswhoseekanup-to-dateviewoftherecentadvancesin thefieldofdatascienceappliedtoSHM. JuizdeFora,Brazil AlexandreCury Porto,Portugal DiogoRibeiro Perugia,Italy FilippoUbertini LaJolla,USA MichaelD.Todd
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