Validation of an indirect data collection method to assess airport pavement condition

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Short

communication

Validation

of

an

indirect

data

collection

method

to

assess

airport

pavement

condition

Bertha

Santos

a,

*

,

Pedro

G. Almeida

b

,

Ianca

Feitosa

c

,

Débora

Lima

d

a_University_of_Beira_Interior,_Department_of_Civil_Engineering_and_{Architecture,}_6200-358,_Covilhã,_Portugal_and_CERIS_(ECI/04625),_Civil

EngineeringResearchandInnovationforSustainability,Lisbon,Portugal

b

UniversityofBeiraInterior,DepartmentofCivilEngineeringandArchitectureandGEOBIOTEC(GEO/04035),GeoBioSciences, GeoTechnologiesandGeoEngineering,6200-358,Covilhã,Portugal

c

UniversityofBeiraInterior,DepartmentofCivilEngineeringandArchitecture,6200-358,Covilhã,Portugal

d

CapeVerdeAirportsandAirSafety(ASA,SA),SalIsland,CapeVerdeandUniversityofBeiraInterior,DepartmentofCivilEngineeringand Architecture,6200-358,Covilhã,Portugal

ARTICLE INFO Articlehistory: Received11March2020

Receivedinrevisedform28July2020 Accepted5August2020

Keywords:

Airportpavementmanagementsystem Datacollection

Imageprocessing Statisticalcomparison

ABSTRACT

Inthisstudytheauthorscomparetwomethodsforairportasphaltpavementdistressdata

collectionappliedonthemainrunwayofAmílcarCabralinternationalairport,locatedatSal

IslandinCapeVerde.Thetwomethodsusedfortestingweretraditionalvisualinspection

(on-foot)andanindirectmethodusingavehicleequippedwithimagecaptureandrecording,lasers

andgeolocationdevices(in-vehicleinspection).Theaimofthisresearchistocontributetothe

validation oftheproposedlow-costin-vehicle pavementdistressinspectionsystemwith

semiautomatic data processing in order to be considered in the implementation ofthe pavement

conditionassessmentcomponentofairportpavementmanagementsystems(APMS).Thisisa

particularlyimportantcomponentasfromthecollecteddistressdataitispossibletoassessthe

conditionofthepavementsanddeﬁneinterventionstrategies.Validationoftheindirectdata

collectionmethodisevaluatedbystatisticalcomparisonofthecollecteddistressdataand

pavementconditionindex(PCI)obtainedfrombothmethods.Statisticallynon-signiﬁcant

differencesbetweentheresultsetsvalidatetheproposedindirectmethod,howevertheanalysis

evidenced two aspects that need improvement in the proposed system, namely the quality ofthe

capturedimagestoidentifydistresseswithlowerseverity levelandinspectortrainingforproper

allocationof severitylevels duringimage analysis.Thisresults insigniﬁcantadvantages

consideringthatthetotalamountoftherunwaypavementareaisinspected.Inspectiontimeis

reducedanddatacollectioncostcanbereduced.ProcessingandresultsvisualizationonGIS

environment allows revaluation ofthe dataset on the in-vehicle method. Data interpretation and

measurementsqualitycontrolbecomessimplerandfaster.

BY-NC-NDlicense(http://creativecommons.org/licenses/by-nc-nd/4.0/).

1.Introduction

ThedevelopmentandimplementationofanAPMSmakesitpossibletomanageassetsinapracticalandsustainable manner, assisting managementpersonnel indecision makingthrough prioritysetting,cost quantiﬁcationand activity scheduling,resultinginthedevelopmentofeconomicallyviablestrategiesforpavementmaintenance[1–3].Fig.1presents themaincomponentsthatareconsideredontheimplementationofanAPMStooperateatthenetworkorprojectlevel[4].

*Correspondingauthor.

E-mailaddresses:bsantos@ubi.pt(B.Santos),galmeida@ubi.pt(P.G. Almeida),ianca.feitosa@ubi.pt(I.Feitosa),debora.lima@asa.cv(D.Lima).

https://doi.org/10.1016/j.cscm.2020.e00419

ContentslistsavailableatScienceDirect

Case

Studies

in

Construction

Materials

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Informationrelatedtothecharacteristicsoftheairportpavementnetwork,asconstruction,maintenance,traf_ﬁcand condition data are collected in the network inventory component. In condition assessment, condition data, deﬁned accordinglytotheevaluationmethodandsurveyfrequencyadoptedbythesupervisingagency,isusedtodeterminethe pavementcondition.Dataisorganizedandstoredinadatabaseforuseindataanalysis,whereevaluationandprioritization oftherehabilitationandbudgetneedsovertheanalysisperiod isperformed.Finally,themain outputs(resultsofthe analyses)areorganizedindifferentformatsforconsiderationofengineersandmanagers.

Thefeedbackloop,oftenoverlooked,establishesaprocesswhereactualperformanceandcostdataareinputtedbackinto themodelsusedonpavementmanagementanalyses[4].

Fromtheabove,itispossibletoconcludethatefficientairportmanagementneedsasignificantamountofdataregarding theconditionofairportpavements.Qualityofthecollecteddataisthereforeessentialtoidentifydeficienciesandproperly diagnose pavement condition. This allows to detect current and future maintenance needs predicting the effects of interventionstrategyoverpavements’lifetime[6,7].

Dataisgenerallycollectedbyvisualinspectionofairportpavements.However,visualcollectionofsurfacedistressesis becoming increasingly challenging for airport maintenance teams as operational constraints limit access time for inspectionsonhighprioritypavements.Moreoverreductionsin fundingandoperationalstaffresourcesleadtovisual inspectionconstraints[8].Consequently,choosingthedatacollectionmethodthatwillprovidethenecessaryinformationto evaluatetheconditionofpavementsisvitalforAPMSsuccess.

Datacollectionbyvisualinspectionhasevolvedfromthetraditionalvisualconditionsurveyperformedonfoot,where distresstype,severity,andquantityareidentifiedandrecordedinpaperform,totheuseofmultifunctionalvehicles.Data collectionsystems installed onthese vehicles arecomposed by one or moreacquisition devices and post-processing applicationsforsemiautomaticorautomateddataextractionprocedures,basedoncomputervisionandimageprocessing algorithms[9].Devicesaregenerallyusedbothonroadandairportenvironments.Amongthemostcommon,thosestanding outareGNSS(locationreferencing)and videologgingmodules(forpavementsand360 imagery),lasercrackand rut measurementsystem(cracks,transverseprofileandrutting),highresolutionodometer(lineardistance),laserprofilometer (roughness– IRI,macrotextureand longitudinalprofile)andlandmobilelightdetectionand ranging(LiDAR)systems. Severalreferencedauthorstested,discussedanduseddataobtainedbythesemodulesandsystemsonairportpavement inspection[8,10–12].

Alsoimportanttonoticeisthelatestdevelopmentsinroadandairportassetinspectiontechnology,thatfocusontheuse ofunmannedaerialvehicle(UAVs-drones)includingLiDARtechnology[13–15].

Inthis context,anin-vehiclesystemwithimagecapture andrecording,lasersand geolocationdevices thatcanbe classiﬁedas alow-cost, mobilemultifunctionsystemwithsemiautomatic dataprocessingisproposed. Thisin-vehicle approachhasbeendevelopedoverthelast4yearsatthedepartmentofcivilengineeringandarchitectureoftheUniversityof BeiraInterior(UBI)[5,16–22],aimingtoimprovetheefﬁciencyofdatacollectionatlocalandsmallinternationalairports.So, thisstudyaimstoverifytheeffectivenessoftheindirectin-vehicleinspectionmethod,that‘àpriori’seemstobeamore practicalmethodologytouseasitrequireslesstimeontherunwayandfewerresourcesthanon-footinspection(withoutthe aidofspecializedtechnicalapparatus).

Forthispurpose,resultsobtainedfromatraditionalon-footpavementsurfacedistressinspectionandtheproposed in-vehicleinspectionsystemdevelopedatUBIarecompared.Acomparativestatisticalanalysiswasperformedontheresultsof pavementsurfacedistressdata(type,severitylevelsanddensities)collectedatexactlythesamesamplingunitsofthemain runwayofAmílcarCabralinternationalairport(ICAO:GVAC),locatedatSalIslandinCapeVerde.

Thepavementconditionindex(PCI)obtainedbyLima(2016)[21]forbothmethodsisalsocompared.PCIforairport pavementswasdevelopedbytheUSArmyCorpsofEngineersthroughfundingprovidedbytheU.S.AirForce.Thisindex describesthestructuralintegrityandoperationalconditionofpavementsanditsdeterminationisbasedontheidentiﬁcation andassessmentoftheseveritylevelof17distressesobservedonpavementssurfaces[23].Itrangesfrom0(worstcondition) to100(bestcondition)anditsratingscalecanberelatedtomaintenanceandrehabilitationactivities[2,3,12,20].

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Thispaperisorganizedin5sections:thefirstsectionpresentstheconceptofAPMSandtheimportanceofchoosingthe airportpavementinspectiontechnologyforthesuccessofthepavementconditionassessment;thefollowingthreesections describethestatisticalapproachadoptedtovalidatetheindirectpavementdatacollectionmethodtested,presentthecase studyandthestatisticalanalysisresults;finally,thelastsectionconcludeshighlightingsomereflectionsoftheauthorsbased ontheresultsobtainedandpresentingtopicsforfurtherdiscussion.

2.Methodology

Tovalidatetheproposedindirectdatacollectionmethod(in-vehicle),thecollecteddistressdataandPCIvaluesobtained throughonfootandin-vehicleinspectionwerestatisticallycompared.

Statisticalcomparisonofgroupsdependsonhowthevariablesunderstudyaremeasured(qualitative/quantitative),its qualityandquantity,typeofsampling(probabilisticorrandom/non-probabilisticornon-random),relationshipbetween samples(independent/paired),thesamples’normalitydistribution(normal/non-normal)andthenumberofgroupstobe compared.

Inthisstudy,variablesreferringtopavementdistresstype,severityanddensitywereevaluatedonsamplesfromthe GVACrunway01 19(studypopulation).Samplingwasde_ﬁnedaccordingtoASTMD5340-12[23].Thisstandardadopts systematicrandomsamplingforselectionofsampleunitstobeinspectedforpavementconditionassessment.

Sincethestudycomparesbothpavementinspectionmethods,pairedsamplesconstitutedbythesamesubjectswere considered(comparisonoftwo-pairedgroups),sopavementdistressdatawascollectedusingthetwoinspectionmethods ontheexactlysamesamplingunitsoftherunway.

Statisticalcomparisonofthesamplesisthenevaluatedbystatisticaltests.Testchoicedependsfundamentallyonthe number ofgroupstobecompared andonnormalityofthedata(variables),assessedbytheKolmogorov-Smirnovand Shapiro-Wilkstests.

Variables with normal distribution are statistically analysed by parametric t-test, while those with non-normal distributionareevaluatedusingtheWilcoxonnon-parametrictest[24].t-testisusedtodeterminetheoccouranceofa signiﬁcantdifferencebetweenthemeansoftwo-pairedgroups.OntheWilcoxontest,differencesbetweensetsofpairsare calculatedandanalyzedinordertoestablishifthetwogroupsarestatisticallysigniﬁcantlydifferent[24].

3.Casestudy

Thecasestudywasdevelopedatrunway01 19oftheAmílcarCabralinternationalairport(ICAO:GVAC),locatedabout threekilometressouthofthecityofEspargos,atSalIsland,CapeVerde.CapeVerde'sairportsystemconsistsofsevenairport infrastructures.ItisthemaindriveroftheCapeVerdeaneconomyasthecountrymainactivityistourism.Inaddition,the geographyoftheterritory,anarchipelago,contributestotheincreasedrelevanceofairtransport.Issuessuchasaccessibility betweenislandsandtoothercountriesbecomesacentraldemandfortheeconomicdevelopmentandsocialactivityof citizens.

Fordistressinspection,theairportpavementnetworkisdividedinto4brancheswitha totalof16sections.GVAC's runwayis45mwideby3000mlong,with7.5mwideshoulders,comprising3oftheaforementionedsectionswith270 samplingunits.Theentiresurfaceoftherunwaypavementwasinspectedbythein-vehiclesystem(270samplingunits)and 43wereinspectedonfoot.This43samplingunitsconstitutestheminimumnumbertobeinspectedaccordingtoASTM D5340-12forPCIevaluation[20].Table1summarizesthesegmentationandadoptedcodinginformation.

Table1

CodingandsegmentationofGVACrunway01–19.

Pavement Code Description/Comments

Pavementnetwork–Airport SID GVACpavementnetwork

Pavementbranches R01 Mainrunway(01 19)

TWY Taxiway

BER Shoulders

APR Apron

R01pavementsections A 900mmeasuredfromend01

(touchdownarea)

B 1200mlocatedinthecentralpartofthe runway

C 900mmeasuredfromend19

(touchdownarea) Pavementsamplingunits–totalrunwayarea

(segmentationaccordingtoASTMD5340-12sampleareacriteria)

A:81samplingunits 500m2

samplingunits(100mby5m) B:108samplingunits In-vehiclevisualinspection

C:81samplingunits Pavementsamplingunits–sampleareaforinspection

(minimumareatoinspectaccordingtoASTMD5340-12)

A:14samplingunits 500m2_sampling_units₍₁₀₀_m_by₅_m)

B:15samplingunits Traditionalandin-vehiclevisual inspection

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Itshouldbenotedthatdespitehavingperformedatotalrunwayin-vehicleinspection,onlythecommon43sampling unitswereusedonthecomparativestatisticalanalysis.Fig.2presentsthelocationofthe3pavementsectionsstudied.

Onthein-vehicleinspectionthefollowingpavementdistressesaredirectlyidenti_fiedondaytimeimages:alligator cracking;bleeding;blockcracking;jet-blasterosion;jointreflectioncracking;longitudinalandtransversecracking;oil spillage;patching andutilitycut patch;polishedaggregate; ravelling; shoving;slippage crackingandweathering. Laser-lineprojectionimagescapturedatnightallowstheidenti_ficationofcorrugation,rutting,swellinganddepression distresses.These 17 distressesare the ones consideredin ASTMD5340-12 standardfor PCIcalculation.A detailed description,aswellasthedefinitionofseveritylevels(low,mediumandhigh)andhowtomeasuredistressescanbe consultedin[23,25].

Thus,inordertocollectallthedistressdata,twoin-vehicleinspectionswerecarriedout,oneduringthedaytimeandthe otherat night.Takingintoaccounttheaccessrestrictionstotherunway duetoaircrafttrafﬁc,allinformation forthe completerunwaywascollectedwithin3days,whiletheon-footinspectiontook3weeksforonly43samplingunits.

Uponcompletionofbothinspections,ageoreferenceddatasetofimageswasobtainedandprocessedforvisualizationon ageographicinformationsystem(GIS).Imageswerecollectedtoformacontinuousstreamwithenoughresolutiontoallowa spatialresolutionoflessthan0.003mperpixelatthecentreoftheimageandreducingspatialresolutiontowardsthe marginsduetogeometricdistortionassociatedwiththeopticalsystem.Latertheimageswerecorrelatedwithpositioning datagatheredonadualbandGPSreceiverandpost-processedonaPPP(precisepointpositioning)strategyusingonlinefree processingresources.Thisallowedforlocationerrorsfarbelow0.05mwhilemaintainingequipmenttoaminimum.Image and positioning data correlation quality was veri_ﬁed using control points on the ground. Image georeferencing and orientationwasperformedusinganin-housedevelopedsoftwarepackage.

Imagevisualizationoccursonacontinuouscoverageoverimposedreferencefeaturesoftherunwaytopographicsurvey. Thisvisualizationallowedidentiﬁcationofsurfacepavementdistressesandseveritylevelaswellasmeasurementofareas andlengthofinﬂuence.Theon-screendigitisationprocessofdistresseslengthandareafromcapturedimagesallowsfora vertexplacementaccuracythatis,onaverage,of2pixels,correspondingto0.006m.Measurementsperformedon-footusing measuringwheel presentsa sub-decimetricaccuracy forlineardistresses.Surfacedistressareaiscalculatedusingthe productoftwoapproximatelyorthogonallinearmeasurements.Consequently,measurementofareasusingimages,basedon careful,on-screenrastertovectordigitisation,aremoreaccuratethaninsitumeasurementsusingameasuringwheel.

Theinspectionsidentiﬁedthepresenceof6typesofpavementdistressesonGVACrunway.Acomparativeanalysisofthe dataobtainedbythetwoinspectionapproachesshowsadiscrepancyregardingtheamountofidentiﬁeddistresstypesand densitybyseveritylevel(seeTable2).

While6distresseswereidentifiedbyon-footinspectiononly4wereregisteredusingthein-vehiclesystem.Thetwo distressesnotidenti_fiedduringthein-vehicleinspectionpresentessentiallyalowdensityandseveritylevelontheon-foot inspection.Inthecaseofdepressions,onlyalowlevelofseveritywasidentifiedon-foot,whichisthoughttohaveimpaired identification usingthein-vehicle system asit is difficulttodetectthis levelof severityonthecapturedimages. For longitudinal and transverse cracking at low severity level, the same situation occurs, however it is possible that a misidentificationoccurredathighseveritylevel,probablyonareaspresentingalligatorcracking.Previoustestsofthe in-vehiclesystemonroadenvironmentsuccessfullyidentifiedcrackingsituationsatahighseveritylevel[22].

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Severityleveldensitydistributionshowsabiastowardsthehigherseverityclassondistressescollectedbythein-vehicle system.However,thesumofcommondistressesdensitiesforbothmethodsissimilar.Toassesswhetherthesedifferences arestatisticallysigniﬁcant,astatisticalcomparisonofthetwogroupsofsampleswasperformed.

It is worthtopoint out thatdata processingof theinformation gatheredduring thetwoinspection methodswas performedbythesameinspector.

Subsequentlyandconsideringthe6(on-footinspection)and4(in-vehicleinspection)identiﬁedstresses,PCIvalueswere calculatedperinspectedsamplingunitandpavementsectionforbothmethods(seeTable3andFig.3).Valuesobtainedby bothapproachesaresimilar,withmostoftheresultsshowingPCIvaluesintherange25–40,reﬂectingtheneedforashort terminterventionontherunwaypavement.

Fig.3presentsthescatterplotofPCIvaluespairedbysamplingunitandthecorrespondinglinearregressionmodel.The linearmodelisagoodﬁtforthedata,asdifferencesbetweenobservationsandthepredictedvaluesaresmallandunbiased (ﬁttedvaluesarenotsystematicallytoohighnortoolowintheobservationspace).Theseconclusionsareemphasizedbya R2_-value_of_0.8993_and_a_standard_error_of_the_estimates_of_3.6386.

Consideringanoptimalmodelwherein-vehiclePCIwouldbethesameason-footdeterminedPCI,representedonFig.3 asareferenceline,itisclearthatlowerPCIvaluesareoverestimatedonthein-vehicleapproachandhighervaluesare underestimated,withapivotingpointaroundPCI=32.

4.Statisticalanalysisanddiscussionofresults

Densityvaluesof4pavementdistressesthatwereidentiﬁedonGVAC’srunwaybyon-footandproposedin-vehicle inspectionsystemwereusedtostatisticallycomparethetwoinspectionapproaches.ThiswasalsoperformedforthePCI valuesobtainedfromthoseidentiﬁeddistresses.

Forthispurpose,threeanalyseswereimplemented:descriptivestatisticalanalysisconsideringdispersionandcentral tendencymeasures,anormalityanalysistodeterminethecomparativestatisticaltesttobeappliedduringthecomparative study(parametricornonparametric)andastatisticalcomparativeanalysis.

OnthenormalityanalysistheKolmogorov-SmirnovandShapiro-Wilktestswereadoptedandappliedtodensityvalues bytypeofdistressandlevelofseverity(low,mediumandhigh).TheobtainedP-valuesindicatethatbothdatasets(on-foot andin-vehicle)essentiallypresentanon-normaldistribution(p-value<0.05).Theexceptionbeingweatheringinsurface wear(highseveritylevel)onin-vehicleinspectionthatexhibitsanormaldistributionaccordingtotheKolmogorov-Smirnov test.Thisresultcanberelatedtothehighernumberofidentiﬁedcaseswhencomparedtotheother3distresses(larger sample size),suggestingan inﬂuenceontheresultof thenormalitytest (seeTables2and 4).Duetothenon-normal

Table2

Identiﬁedpavementdistressesanddensitiesbyseveritylevelandtypeofinspection.

Inspectiontypeandidentiﬁeddistresses Densitybyseveritylevel(%)*

On-footinspection Low Medium High

Alligatorcracking 2.00 1.83 0.07

Patchingandutilitycutpatch 2.18 1.02 1.92

Ravelling 0.70 2.66 11.13

Weatheringinsurfacewear-densemixasphalt 4.78 7.82 16.00

Depressions 0.53 0.00 0.00

Longitudinalandtransversecracking 0.24 0.00 0.16

Sum(alldistressesidentiﬁed):53.04 10.43 13.33 29.28

Sum(distressesidentiﬁedonbothmethods):52.11 9.66 13.33 29.12

In-vehicleinspection Low Medium High

Ravelling 0.00 2.75 12.60

Sum(identiﬁeddistresses):50.88 3.15 10.59 37.14

Table3

PCIvaluesobtainedforeachsampleunitinspectedandrunwaysection(adaptedfrom[21]).

Section Typeofinspection PCIpersamplingunit PCIpersection

A On-foot 36,38,41,43,52,20,52,27,34,46,24,11,60,46 38 In-vehicle 30,40,45,40,52,22,46,30,37,46,26,15,54,48 38 B On-foot 22,32,48,25,22,15,17,18,27,56,38,53,31,35,20 30 In-vehicle 26,26,41,27,28,24,18,16,30,59,38,47,31,29,25 31 C On-foot 44,27,32,36,35,20,35,63,32,43,59,25,30,26 36 In-vehicle 47,23,32,32,29,19,35,53,32,39,54,29,24,28 34

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distributionofthedistressdensitydata,thenon-parametricWilcoxontestwasadoptedforcomparativeanalysisofboth groups.

ConsideringPCIvalues,theShapiro-Wilknormalitytestindicatesanormaldistributionofdata(P-value>0.05)forboth on-footandin-vehicleinspection.However,accordingtotheKolmogorov-Smirnovtest,PCIvaluesobtainedfromin-vehicle inspectiondatadonotfollowanormaldistribution.Giventheseresults,aPCIstatisticalcomparisonwithparametrict-test (statisticalmean)andnonparametricWilcoxon test wasperformed. Anf-testwas alsoperformed tocompare sample variance.

ResultsofthenormalitytestsandthestatisticalcomparisonanalysisarepresentedinTables4and5.

Resultsshowthatpatchingandutilitycutpatchdistressidenti_ﬁedatlowandmediumseveritylevelswerenotvalidated. Inthesecases,P-valuesarelessthan0.05,meaningthatthesamplesarenon-comparable.Thesameoccursforweatheringin surfacewearathighseveritylevelanditcanberelatedtothediscrepancyofthenormalityresultsobservedforthisparticular case.

Thenon-validationofthesecasescanberelatedtoclassificationdifficultiesofweatheringinsurfacewearataspecific severityleveloncollectedimages,aswellasotherpavementdistressespresentinglowseveritylevel.

Fig.3.PairedvaluesofPCIobtainedinthetwoinspections(on-footandin-vehicle)andlineartrendline.

Table4

Normalitytestsresults.

Test Kolmogorov-SmirnovP-value Shapiro-WilkP-value

InspectionbyfootSeveritylevel Low Medium High Low Medium High

Alligatorcracking 0.000 0.000 0.000 0.000 0.000 0.000

Patchingandutilitycutpatch 0.000 0.000 0.000 0.000 0.000 0.000

Ravelling 0.000 0.000 0.000 0.000 0.000 0.000

Weatheringinsurfacewear-densemixasphalt 0.000 0.000 0.011 0.000 0.000 0.000

PCI 0.200 0.340

In-vehicleinspectionSeveritylevel Low Medium High Low Medium High

Alligatorcracking 0.000 0.000 * 0.000 0.000 *

Patchingandutilitycutpatch 0.000 0.000 0.000 0.000 0.000 0.000

Ravelling * 0.000 0.000 * 0.000 0.000

Weatheringinsurfacewear-densemixasphalt 0.000 0.000 0.200 0.000 0.000 0.002

PCI 0.008 0.097

*

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Theremainingdistresses-severitypairsandPCIvalueswerevalidatedbytheWilcoxontest.t-testalsovalidatedPCI values.

Basedontheresultsoft-test(statisticalmean)andf-test(variance),itcanbeinferredthattheuseoftheproposed in-vehiclesystemtoinspect pavementsurfacedistressesdidnotsigniﬁcantlyinﬂuencePCIresults,sincethestatistical average(t-test)ofbothsamples,aswellasitsvariance(f-test)producedP-valuesgreaterthan0.05,validatingtheproposed in-vehiclesystem.

5.Conclusions

ThecomparativestatisticalanalysisonPCIvaluesvalidatestheinnovativelow-costmethodproposedforcarryingout inspectionsusinganequippedvehicle.Foramoreconsistentvalidation,distressesidentiﬁedinbothapproacheswerealso meticulouslyanalysedbylevelofseverity.

Dataanalysisevidencedabetteridentiﬁcationofdistressesandseveritylevelsbydirectvisualization,inparticularforlow severitylevelsandweatheringinsurfaceweardistress.

Comparingthetypeofdistressesidenti_ﬁedbyeachapproach,it_’sclearthatthein-vehiclemethodcouldnotassesstwo typesofdistressesthatweresurveyedon-footatalowseveritylevelandlowdensityvalues.Still,bycomparingdistresses allocationbyseveritylevel,it’sclearthatatendencytoclassifydistressesathigherseverityleveloccursonthein-vehicle system.

Theseresults,supportedbythestatisticalcomparisonanalysis,donotcompromisetheproposedmethod,sinceitallowed toidentifyimprovementaspectsintheproposedin-vehiclesystemandinthesemi-automaticprocessingofimages.

Theauthorsconsiderthat, byimprovingthein-vehicle system,thedifﬁculties encounteredwhileidentifyingsome distresseswillbesurpassed.Thisimprovementcanbeachievedthroughtheuseofhigherimagequalitycamerasandthe additionofasecondcameraplacedtocapturepavementroughnessandsmalldeformations.Itisfurtherconsideredthat inspectors trainingonimage analysis,especially regarding distressseveritylevelallocation, aswellas theinspectors’ accumulatedexperiencecouldpositivelyinﬂuenceresultsofthecomparativestatisticalanalysis.

Bycomparingbothinspectionapproachesitwasnoticedthattheproposedin-vehicleprototypewithimage,laserand GNSSdeviceshasasthemainadvantagethereductionofthetimeneededtocollectpavementdistressinformation,as traditional(on-foot)surveysaretimeconsuming,becomingmoreexpensiveforlargerairportswithtraf_ﬁcconstraints.

Thein-vehicleapproachalsoallowstocontinuouslysurveytheconditionoftheentirepavementsurfaceandtoprocessall theinformationattheofficevisualizingdataonaGISenvironment,allowingre-evaluationandconfirmation,ifnecessary,of thepavementdistressdataobtainedunderthesameconditionsasthoseveri_fiedatthetimeofinspection(imagearchive). WiththisapproachitisalsopossibletocomparetheGISimageandalphanumericdatafromseveralinspectionscarried outoverthepavementlifetime,supportingtrendstudiesofpavementcondition.

DeclarationofCompetingInterest

Theauthorsreportnodeclarationsofinterest. Acknowledgements

TheauthorsacknowledgeUniversityofBeiraInterior,CERIS-CivilEngineeringResearchandInnovationforSustainability (ECI/04625),GEOBIOTEC-GeoBioSciences,GeoTechnologiesandGeoEngineering(GEO/04035) andASASA,CapeVerde AirportsandAirSafetyforsupportandfundingofthisstudy.

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Table5

Wilcoxontest,t-testandf-testresults.

Variable WilcoxontestP-value t-testP-value f-testP-value

Severitylevel Low Medium High Notapplicable Notapplicable

Ravelling 0.068 0.861 0.640

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