Table Of ContentStructural 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
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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
