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Standard Title Page- Report on State ProjectReportNo.

VTRC 96 R20

Title and Subtitle

Authors

ReportDate

March 1996

No. Pages:27

Period Covered:

TypeReport:

Final Report

ProjectNo.:9119 020 940

Contract No.:

Key Words:Final Report:Assessment of Virginia sHybridSouth Dakota RoadProfilingSystem

K. K. McGhee

PerformingOrganizationName and Address:

VirginiaTransportationResearch Council530 EdgemontRoadCharlottesville,VA 22903

Ultrasonic roughnesstestingLaser roughnesstestingSouth Dakota road profilerRide qualityEquipmentevaluation

SponsoringAgencies Name and Addresses

VirginiaDepartmentof Transportation1401 E. Broad StreetRichmond,VA 23219SupplementaryNotes

AbstractSouth Dakota Road Profiling(SDRP)systems have been widelysanctioned for use in assessing

roadroughnessand rutting

athighwayspeeds.Traditionally,these high-speedprofilingsystems have beenbuilt around ultrasonic heightsensors. More recently,some designshave begun to substitute laser sensors

to combat many of the limitations of sound based equipment. The functional improvementspossiblewithlasers are expensive,however. Whether the potentialof lasers for high-speedprofilingjustifies the greatincrease in cost was a legitimatequestion.

This reportdescribes the steps that the VirginiaDepartmentof Transportationtook to address thatissue. In general,this research has found that both sensor types are capableof performingvery well andwith reasonable agreement. Repeatabilitytests indicated that the roughnessindices calculated from laser-based profilesare less subjectto run to run variabilitythan those developedfrom ultrasonic profiles. Teststo evaluate the effects of vehicle speedand samplingrate on the estimate of roughnesshave returned lessconclusive results. Both tests suggestedthat the ultrasonic-based data was more highlycorrelated with thecontrol data i.e., that based on data collected with the Face CompaniesDipstickR than the laser-based.The laser data, however,could be shown to be slightlymore accurate, on average, than that based on theultrasonic.

Unfortunately,althoughthe performanceof the ultrasonic sensors on conventional surfaces hasbeen adequate,their reliabilityhas become a serious concern In Virginia,humid surveyingconditions areoften unavoidable and the problemof wet ultrasonics has become more than a simpleinconvenience.Hardware reliabilitymay be the most compellingreason to migrateto pure laser roughnessmeasurementThe adoptionof onlylaser-based equipmentin future purchasesor leases is recommended.

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FINAL REPORT

ASSESSMENT OF VIRGINIA S HYBRID SOUTH DAKOTAROAD PROFILING SYSTEM

Kevin K. McGhee,P.E.Research Scientist

The opinions,findings,and conclusions expressed in thisreport are those of the authors and not necessarily those of

the sponsoringagencies.)

Virginia TransportationResearch CouncilA CooperativeOrganizationSponsoredJointlyby the

Virginia Departmentof Transportation andthe University of Virginia)

Charlottesville, Virginia

March 1996VTRC 96 R20

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opyright 1996 ommonwealth of Virginia

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ABSTRACT

South Dakota Road Profiling(SDRP)systems have been widelysanctioned for usein assessingroad roughnessand ruttingat highwayspeeds.Traditionallythese high-speedprofilingsystemshave beenbuilt around ultrasonic heightsensors. More recentlysome designshave begun to substitute laser sensors to combat many of the limitations ofsound based equipment. The functional improvementspossiblewith lasers are expensivehowever. Whether the potentialof lasers for high-speedprofilingjustifies the greatincrease in cost s a legitimate question.

This reportdescribes the steps that the VirginiaDepartmentof Transportationtook to address that issue. In general this research has found that both sensor types arecapableof performingvery well and with reasonable agreement. Repeatabilitytestsindicated that the roughnessindices calculated from laser-based profilesare less subjecttoran to run variabilitythan those developedfrom ultrasonic profiles. Tests to evaluate theeffects of vehicle speedand samplingrate on the estimate of roughnesshave returned lessconclusive results. Both tests suggestedthat the ultrasonic-based data was more highlycorrelated with the control data (based on data collected with the Face CompaniesDipstick• than the laser-based. The laser data however,could be shown to be slightlymore accurate, on average, than that based on the ultrasonic.

Unfortunately,althoughthe performanceof the ultrasonic sensors on conventionalsurfaces has been adequatetheir reliabilityhas become a serious concern In Virginiahumid surveyingconditions are often unavoidable and the problemof wet ultrasonics hasbecome more than a

simpleinconvenience. Hardware

reliabilitymaybe the most

compellingreason to migrateto pure laser roughnessmeasurement. The adoptionof onlylaser-based equipmentin future purchasesor leases s recommended.

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FINAL REPORT

ASSESSMENT OF VIRGINIA S HYBRID SOUTH DAKOTA ROAD PROFILINGSYSTEM

Kevin K. McGhee,P.E.Research Scientist

INTRODUCTION

Road and bridgeroughnesshas been activelystudied at the Research Councilsince the mid 1960s. 1 4 For most of that time, the instruments used to assess rideabilityhave been response-typeroad roughnessmeasurement RTRRM) systems. As the nameimplies,RTRRMs estimate rideabilitybased on the response of the instrumentedvehicle(or trailer) travelingat a specifiedspeedover a pavement'ssurface. Unfortunately,thenature of these systems make their results extremely instrument-specificand subjecttowide variability. Anychangethat affects the response of the vehicle to the surface(suspensionor tire wear, vehicle and operator weightchanges,etc.) will affect theestimate of roughness.

To counter the shortcomingsof RTRRMs, more comemporary road roughnessequipmentand strategieshave attemptedto mitigatethe bias imposedby ride meter

suspensionsby basingroughnessestimates on the actual road surface profile. The lastformal road roughnessprojectconducted at the Research Council evaluateda profile-based system.

4 The K.J. Law Model 8300 RoughnessSurveyorevaluated in that studycollects the profileof a singlewheelpathusing,among other instruments, a bumper-mounted ultrasonic heightsensor. The studyobjectiveswere to assess its suitabilityforcollectinginventorydata, performingconstruction qualitycontrol and acceptancetesting,and performingresearch-oriemed surveys. This study,which began in 1985 and wascompletedin November 1992,was plaguedby equipmemfailures and manufacturerdelays. Ultimately,the studyfound that althoughthe Model 8300 Surveyorwas a usefulroughnesstestingdevice, t had serious limitations. Most significantof which were thestrong influences of testingspeedand temperatureon roughnesstest results.

Since the developmentof the first high-speedprofiler,many improvementshavebeen made in the ability to collect and process road roughnessinformation. In the late1980s, in order to collect the Federallyrequiredroughnessfor Virginia sapproximately3000 HighwayPerformance MonitoringSystem(HPMS)sites, VirginiapurchasedaSouth Dakota style road profiler. This system, based on the research led by Mr. DavidHuff of the South Dakota Departmentof Transportation,6 7 simultaneouslycollects three

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profiles one for each wheelpathand one for the lane center). From these profiles,software calculates a measure of roughnessand an estimate of ruttingat specifiedintervals alongthe roadway.

The South Dakota Road Profiler SDRP)and the K. J. Law Model 8300 both fallinto a category of instruments called accelerometerestablished inertial road profilingAEIRP)systems. Three fundamentalcomponentsare necessary to enable an AEIRP tocollect profiles. The first is the accelerometer s)with which a positionalreferenceplane s)for the vehicle is maintained. The second is an electronic distance transducer,which providesa record of horizontal distance traveled. The third is the heightsensorwhich measures the distance of the pavementsurface below the reference planeestablished by the accelerometer. The synchronizedcalculation of the difference betweenthe vehicle displacementand heightmeasurements allows the system to computethehighwayprofile.

Because t is the heightsensor that actuallyinterfaces with the highwaysurface,insome respects t is the most critical element in the system. Manyof the first heightsensors used on inertial road profilingsystems were ultrasonic and operatedon much thesame principleas the first auto-focus cameras More recently,fabricators have beguntosubstitute laser sensors to combat the inherent limitations of sound-based equipment.Themost significantof these limitations is the effect of temperature,wind,and moisture onthe propogationof sound. The speedof the collection vehicle and the amount ofmacrotexture present in a surface have also been shown to influence the performanceofultrasonics.

The functional improvememspossiblewith lasers are expensive.Whether the

potentialof

lasersfor

high-speedroad profilingand rut-depthmeasurement justifies thegreat increase in cost is a legitimatequestion.Compatibilityand consistencywith olderultrasonic systemsalso concern highwayagenciestryingto exploitnew technologieswithout sacrificingtheir in-placeinvestments. As ride qualityspecificationsincorporatingthese devices receive more serious consideration,officials are becomingmore preoccupiedwith the detail accuracy and repeatabilityof these instruments.

PURPOSE AND SCOPE

In early 1994,to address these concerns and investigatethe potentialof laser-based profiling,the VirginiaDepartmentof Transportationacquireda laser-enhancedSDRP. This hybridsystem is equippedwith three ultrasonic heightsensors oneultrasonic correction sensor, and two laser heightsensors. It is capableof collectingroad profiledata left and right wheel paths)with both ultrasonic and laser instruments,simultaneously.With this system, a uniqueopportunityexisted for makinga conclusivecomparisonof the capabilitiesand limitations of both sensor types. Correlation analyses

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did not have to contend with the errors that result when independentvehicle systemstrack different paths,start and stop at different locations,and often have differentoperators.

The objectiveof this studywas to criticallyevaluate laser and ultrasonic sensors

for use as the height sensingcomponentson Virginia sSDRP vehicles. Investigationofthe followingfive issues constituted the largestportionof this comparisonanalysis:

Laser and ultrasonic profileaccuracy (idealprofilingconditions).Laser and ultrasonic profilerepeatability(idealprofilingconditions).Evaluation of the effects of varied sampling and averagingrates on computedindices.Evaluation of the effects of varied operatingspeedson computedindices.Surface texture influence on equipmentalternatives.

Surfaces tested duringthe instrument repeatabilitytests included standard mediumtextured hot mix asphaltconcrete HMAC),a latex emulsion asphaltslurryseal, machine-tined and burlap-draggedportlandcement concrete (PC(;),a gap-gradedstone matrixasphaltconcrete SMA),and a chip seal. Surfaces specificallytargetedfor samplingrateand vehicle speedtests also included the medium textured HMAC the slurryseal, themachine-tined PCC,and the SMA surface,as well as two compositesections mediumtextured asphalt overlayover jointedPCC)and a flushed and rotted smooth HMAC Allof these surfaces are ommon in Virginia.

Unfortunately,an all-inclusive comparativeanalysisof sensor types has not beenpossible.The most importantissue that has not been formallyaddressed is the relative

sensitivityof sensor

typesto

temperatureextremes. Circumstances that

have preventedor hamperedtesting include vehicle breakdown,instrument malfunction, and otherobligationsof the vehicle operators and the research staff.

METHODS

Within the scope of work just described,two data sets were developed.The firstwas primarilyintended to support the instrument repeatabilitytests. The second,somewhat overlappingthe first was established to allow an assessment of the effects ofvaried samplingrates and vehicle speeds. Sensor accuracy and the influence of surfacetexture could also be addressed fromwithin these data.

The roughnessof each test performedis recorded in terms of the IntemationalRoughnessIndex (IRI). The IRI, which is the most standardized method of reportingroughness, is producedthrougha reference quarter car simulation RQCS).• Thissimulation is appliedto a measured longitudinalprofileand can be calculated for nearly

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any interval. Strictlyspeaking,IRis are specificto an individual profile(andnormallycorrespondto an individual wheelpath).For much of this project,however,roughnessisreportedand comparedby lane average. This lane average is the m e n of the valuesreportedfor the left and right wheelpathsof a singletest. Lane averages were used tohelpconsolidate the data, and because lane values are most consistent with current and

proposedreportingprocedureswithin the state.

Repeatability

TestingProcedures

The proceduresused to test repeatabilitywere identical to those used in theassessment of the KJ Law 8300.4 Like the earlier study, a test of one surface involved 30consecutivereplicatesof the s me pavementsection. Because data from sites used toconduct more rigorousfield testing (withtexture tests, baseline profiling,etc.) were beinglumpedwith those which could be conducted under traffic, all repeatabilityanalyseswereconfined to selected lengthsof 173 meters (0.1 mile).

Data Analysis

Separatereportswere developedfor each run and consolidated usingthe maker ssoftware. These reports were parsedinto spreadsheetsthat contained just the selected 173meter (0.1 mile)section for each of the 30 back-to-backruns. From these worksheets,a

separate series of statistics w s generatedfor each surface,sensor type, and wheelpath.Specifically,those statistics included the mean, the standard deviation,the coefficient ofvariation,the maximum,the minimum,and the IRI range for the 30 replicates.Alsocalculated for each surface and instrument type were the coefficientsof variation, v v istaken as the standard deviationas a percentage of the mean).Perhapsthe mostencompassingstatistic w s the N(req d)value which estimates the number of samplesrequiredto find the real roughness,givenspecifiederror tolerances and confidencelevels. The equationused to calculate N(req d)is

Nreqd--[ t) v)]2E

where is a probabilityfactor 2 for 95 percent confidence level and 30 samples),v is thecoefficient of variation,and E is the tolerance (5 in this case). The reader is againreferred to K.H. McGhee 4 for a table of probabilityfactors correspondingto the degreesof freedom and desired confidence levels.

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Seasonal Repeatability

The initial repeatabilityassessment was conducted in the springof 1995.Followingmost of the overlayseason of the summ r of 1995,the repeatabilitysitesestablished in the springwere visited againin late fall. There were two objectivesindoingso. The first was simplyto examine what influence,if any, the temperaturedifferences had on the abilityof the respectiveheightsensingdevices to providerepeatableresults. The second,assumingthat reliable informationwould still be possibleunder fall conditions,was to observeany legitimatechangein ride that may occur over atwo season timespan(spring/summer,summer/fall).

Just as in the 30-run repeatabilityassessments conducted with the single-seasondata from Apriland May,the November and December tests includedmultipleruns overthe same test site. In the interests of time,the fall tests were terminatedafter 10 iterationsper site. Otherwise,the same statistics generatedfor the springrepeatabilityassessmentwere

producedfor each site. Of

course,the

probabilityfactor used to calculate the

number of samplesrequiredto determinereal roughnesswas adjustedfor the reduceddegreesof freedom insteadof 2, was taken as 2.23).

Rate and SpeedTests

Site Selection and Preparation

With minor modification,the convention appliedto selection and preparationofthe rate and speedsites was borrowed from the Road Profiler User s Groupstudyfrom1993.9 Each site was a measured 173 meter 0.1 mile)section of homogeneoussurface,selected to avoid any exaggeratedhorizontal or vertical curvature and also anyinterference by adjoiningroads anddriveways.The primarydifference from the RPUsites was that these sites were located usinga lead-in section of an even multipleof sitelength2 or 3 x 173 m, or 0.2 to 0.3 mile). The need for artificial bumpsto preciselylocate sections was eliminatedby iniatingeach run from a completestop at the beginningof this lead-in.

Once a 173 meter 0.1 mile)section was selected and the beginningestablished,the wheelpathswere located. The exact positionof the wheelpathsrelative to the centerline and the edgeof pavementvaried dependingon the width of the lane. Lane widthsvaried from approximately3.3 to 4 meters 10 to 12 feet).Regardlessof the width of thelane, all site wheelpathswere located 1.75 meters 69 inches)apartto correspondwith theknown spacingof the wheelpathsensors. With two peopleusinga measuringtape andan operatorcloselymonitoringthe vehicle distance measuringinstrument(DMI),thecenter of each wheelpathwas marked with keel at 8.2 meter 25 foot)intervals. Withthese intervals established,a crew of three people placedchalk lines longitudinallyfor the

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extem of each 173 meter (0.1 mile)test section. Chalk lines were also snappedtransverselyat the marked 8.2 meter (25 foot) intervals.

8.2 m (25 ) Spa. on Transverse

350 m to 520 m(0.2 mi to 0.3 mi) --•Lead-in Section

DiameterCircle (Typical)

73 m 0.1 mi)Test Section

Figure 1. Test Site Layout

TestingProcedures

With site layoutcomplete,a DipstickR instrument (manufacturedby The FaceCompanies)was used to collect control profiles. A boxing techniquewas used tosupplementthe accuracy of these profiles. This techniqueconsisted of a continuoussurvey down one wheelpath,across the lane at the site end, a return walk down theoppositewheelpath,and then closingthe box back to the startingpointas shown inFigure1. Test Site Layout.

While the DipstickR operator performedhis survey, two other techniciansconducted sand patchtests. These tests, which were conducted in accordance with

STM Standard E 965 87, provideda simple, objectivemeasure of surfacemacrotexture These values were later used to investigatethe influence of surface textureon equipmentalternatives.

Lastly, transverse profileswere collected with the DipstickRat the marked 8.2meter (25 foot) intervals,for a quickestimate of full transverse ruttingfor each site.

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When control data collection w s completed,the site w s cleared and testingwiththe hybridSDRP began.A completetest of a selected site includedfour repeat runs at 32km h 20 mph),one run at 48 km h 30 mph),one at 64 km h 40 mph),and at least threeat 80 km h 50 mph). Table lists the mn sequencing,completewith samplingrates andvehicle speeds.The varyingof rate is onlypossibleat 32 km h 20 mph)due tocharacteristics of the ultrasonics which preventhigherresolution samplingat higherspeeds. As the table shows,once the n at 32 km h 20 mph)and a samplingrate of 6for both instrumentsw s complete,the samplingrate scheme w s set to 6 and 3 for theultrasonics and lasers respectively,and maintainedfor the remainderof the tests. Thesesamplingsettingswere used becausethey represent the normal mode of operationforroad roughnesstesting the lasers are routinelyoperatedat twice the samplingresolutionof the ultrasonics).

RunN•mberTable 1. Rate and SpeedTest Sequence

Ultrasonic Sal•i•iingRate Laserg•plingRate Vehi;]•Speed2 120mm/sample) 2 120mm/sample)32km/h 20mph)

2 4 240mm/sample) 4 240mm/sample) 32km/h 20mph)3 6 360mm/sample) 6 360mm/sample)32km/h 20mph)4 6 3 180mm/sample)32km/h 20mph)5 6 3 48km/h 30mph)6 6 3 64km/h 40mph)7 6 3 80km/h 50mph)8 6 3 80kin/h 50mph)9 6 3 80km/h 50mph)

Data Analysis

To ensure completeflexibilityin future analyses,the data from every test werestored in their rawest state. In binary machinelanguage),the completedata sets arelarge. Completelyconvertedto ASCII,the database size would have been intractable. Toconserve storage space and minimize confusion,reports of the results onlyextracted theinformationrelevant to the current analysis.

As such,reports for each run of each site for each sensor type were generatedandsummarized usingsoftware providedby the vehicle fabricator InternationalCyberneticsCorporation).Typically,only a singleline out of 10 to 15) of each roughnessreport w snecessary. Fortunately,a summarizingroutine providedby the manufacturer w s helpfulfor gleaningthat data record. Once these records were lumpedinto a singletext file thefile w s drawn temporarilyinto MicrosoftExcel. Appropriatelyparsed,the resultingspreadsheetswere importedinto a MicrosoftAccess database. The entire database w scomposedof three tables. The first includeda record for each n and describedthe

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sensor type, the vehicle speed,and samplingrate used for the test. The secondtable alsostored a record per run, but includedonlythe measured results for roughnessand rutting.The last table providedspecificlocation information,site type i.e., rate speedand/orrepeatability),operatoridentityand testingdate, and measured texture data and anydistress information.With the databasemanager unchanginginformationabout a givensite,such as location,type, and texture, could be recordedonce, stored independently,and combined as necessary.

Once the databasewas functional,analysiscould begin. First, the roughnessdatawas queriedto gleanonlythose records containinginformationof a certain type forexample,sensor laser, collection rate 2, speed 32 km/h or 20mph). Combiningindividual queries,new spreadsheetswere built to investigatethe various relationshipshypothesizedto exist within the data. Of obviousinterest was the performanceof therespectiveheight-sensingdevices,comparedto each other and againstthe DipstickROne series of spreadsheetswas developedto statisticallyrelate the three 32 km/h 20mph)runs at each site for each instrumentand samplingrate. A secondseries ofspreadsheetslumpedthe data associatedwith the tests conducted at normal samplingratesand increasingspeeds.Contributingto the graphicaland numericalassessments of eachset of results were scatter plots with linear trend lines, correlation analysesof oneinstrumentversus another,bar plots of the differences observedby each instrumentatchangingrates and speeds,and a correlation analysisof instrumentdifference and speed.

FIN INGS

Profiles

The initial inclination,giventhe power and sophisticationof modem hardwareand software,was to devise a method for statisticallyreducingthe raw profilesgeneratedby the competingdevices and basingany comparisonson their respectiveperformancesatthat level. The followingfigureswere developedto observethe productsof thealternative profilingdevices in their most fundamentalstates. The figuresarerepresentativeprofilesassembled from a singlewheelpathof a singletest site. Figure2illustrates the profileas collected staticallywith the DipstickR One line representstheunaltered road surfaceprofile,completewith its designslope. The other line has beenrotated to remove any slopeimposedby design.This rotated profilewas producedtoprovidea more equitablecomparisonwith the SDRP,a systemthat does not returnprofileswith absolute elevation information.

Figure3 depictsthe profile, as registeredby the laser and ultrasonic equipmentonthe SDRP. Figure4 is a zoomed portionof the same profilethat shows that the twodevices are indeed perceivingfeatures slightlydifferently.The next illustration Figure5) demonstrates that althoughthe similarities between the two SDRP instrumentsare

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remarkable,the profilestheyprovideappear quite different from that determinedstatically.Curiouslyin spite of this dramatic dissimilaritythe IRI calculated usingthelaser-based profileof this particulartest site w s within 3 percent of that calculated usingthe profilecollected with the Dipstick1• The roughnessas estimated usingthe ultrasonic-based profileof this test site differed from the Dipstick• by less than 14 percent.

Location m)

Figure 2. Dipstick Profiles

50

40

30.

20.

• 10.

trasonic

-20

Location m)

Figure 3. Laser and Ultrasonic SDRP Profiles

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Figure 4. Laser and Ultrasonic S RP Profiles (Magnified)

Location (m)

Figure5. All evice Profiles

Unfortunatelythe database that would have resulted had the analysisattemptedtocompare devices profileby profilewould have been prohibitivelylarge. In fact evenafter decidingto evaluate the instruments based on a section s calculated IRI the furtherconsolidation to lane averages, as opposedto individual wheelpathswas made for thisdiscussion.

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Repeatability

Table 2 summarizes the results of the single-seasonrepeatabilityanalyses.Thesevery generalstatistics suggestthat under the prevailingtest conditions,both instrumenttypes (ultrasonicand laser) can providevery respectableresults. A simplestatistic thatcan be fairly informativeis the range over which the measured indices vary over 30 runs.Here, an absolute value for range was included,as well as the value of range as a percentof the m e a n IRI. Generallyspeaking,the smaller the rangingcharacteristic of aninstrument,the more confidencethat can be associatedwith it Note that these resultsindicated that a laser-basedindex can be expectedto drift approximately3 less thanindices originatingfrom ultrasonics.

Also includedin the table are the averagecoefficientsof variation, v, listed byinstrumenttype. This statistic normalizes the variabilityof the equipmentby reportingthe standard deviationof a series of tests as a percentageof the mean t providesa veryconcise estimateof expectedrepeatabilityfrom one replicateto the next.

The last statistic is the number ofsamplesrequired,Nreq,d,to find the realroughness,giventhe tolerances and confidencediscussedearlier. This statistic combinesthe variabilityas reportedby the coefficient of variation with ideasof normaldistributionfrom conventionalmaterials testingtheory. These tests indicate that, on average, onemore samplewill be necessary when collectingultrasonic roughnessthan is requiredwithlasers. For a singlesite's assessment,the savingsfrom one repetitionmay not appearcritical. However,extrapolatedto the administrationof rideabilityspecifications,areduction in one run per site for every site in a season's overlayschedulewould quicklybecome significant.

Table 2. Summary of RepeatabilityTests

Two-WheelpathAverage30 REPLICATES EACH SITE

Ultrasonic LaserSTATISTIC All Surface Average All SurfaceAverage

AVG IRI(mm/m) 1.80 (114in/mi) 1.67 (106in/mi)Max IRI(mm/m) 1.91 (121in/mi) 1.75 (111 in/mi)Min IRI(mm/m) 1.69 (107in/mi) 1.58 (100in/mi)Range(mm/m) 0.22 (14 in/mi) 0.17 (11 in/mi)

Range 12.9 9.9( AVG IRI)

Std Dev. .05 .04

v( ) 3.1 2.3N(req'd) 1.7 0.9

11

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Figure6 Figure7 and Figure8 depictthe statistics discussed above as they w rgeneratedby individual site. Radar-typeplots have been used to emphasizethe relativeperformanceof each instrument. As discussed previouslya smaller value indicates better

repeatability.For this type of plot a tighterdispersionof a statistic suggests lessvariability.As expectedthe ultrasonic-basedequipmenthad the most difficultywith themor exotic surfaces. The rather pronouncedstar pointson these imagesclearlyillustrate the relative difficultythe ultrasonic equipmenthad in handlingthe stone-matrixasphalt(SMA)and the machine-tinedconcrete (JPCP)surfaces.

Figure 6. 30 Run Repeatabilityby Range

Figure 7. Coefficients of Variation

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Figure 8. Estimated Number of SamplesRequired

Seasonal Repeatability

The next several figurespresent the findingsof a late-projectlook at seasonalrepeatability.For generalcomparativepurposes Figure9 is a line plot of the averageroughnessof each site estimated usingboth SDRP device typesin both seasons Asexpected,there appears to be very little changein the ride qualityof the CRCP pavementon Interstate 64. In fact, the values reportedby both device types,in both seasons areremarkablysimilar. The same is essentiallytree for the deteriorated HMAC ( 5)pavementon Route 29 in Albemarle. On the other hand,the surface treated (ST)pavementappears to have grown slightlysmoother since the previousSpring.

The next three imagesare the radar plotsdiscussed earlier with the results of theFall repeatabilitytests added. The tendencyof the ultrasonics to range significantlywhentestingthe semi-exotic surfaces SMAand tined concrete)is not as prevalentin the falltests. This may be due, in part, to the reduction in number of tests performed. That is

the 20 fewer tests conducted in the fall were 20 fewer opportunitiesfor exceptionallyhighor low measurements.

Nonetheless,there was moderate deterioration in repeatabilityexhibited by theultrasonics in the fall tests. This deterioration is emphasizedby the coefficient ofvariation and the calculated number of samplesrequired.In each,the series portrayingthe fall ultrasonic values tend to envelopthe other three data series.

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Figure 9 verage Site Roughness

Figure 10 RangingPercentage

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Figure 11 oefficient of Variation

Figure 12 umber of SamplesRequired

15

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Rate and Speed

Table 3 lists the surface typesand measured macrotextures for the evaluatedrateand speedsites.

Table 3. Surface Types and MacrotexturesSite ID Surface Type MacrotextureR15S2 S5 flushed)R15S1 $5R460C JPCPR460W Latex SealRt29S S5 alligator)R13S2 S5(compos.)R13S1 S5(compos.)Rt207 SM

0.326mm (0.01283in0.388 (0.01528in0.501 (0.01974in0.599 (0.02359in0.634 (0.02496in0.847 0.03333in0.893 (0.03515in1.331

(0.05239inThe effects of samplingrate and collection speedwere tested throughtwo

independentcomparisonswith the baseline Dipstick• information. As expected,datafrom all samplingrates and testingspeedswere highlycorrelated with the results of theDipstick•. Curiously,the lasers were not the best performersin either evaluation.

Rate

Figure13 and Figure14 are scatter plots comparingroughnessindices generatedfrom data collected by the competingSDRP instrumentsand data based on the DipstickR.The generaltrends observed in these figuresare more succinctlyreflected in Figure15,which shows simplebar representationsof the relative degreeof correlation with theDipstickR for each instrumentand samplingrate. Here,it would appear that the strongestrelationshipwith the DipstickR is demonstratedby the ultrasonic-basedequipmentsamplingat 240 0.8 foot) intervals.

Figure16, which charts the average instrumentdifferencesat the varyingsamplingrates, is somewhat contradictoryto the correlation analysisjust discussed. Itindicates that the best agreementwith the DipstickR was givenby the lasers, whichimproveddramaticallyas the samplinginterval was reduced from 4 to 2 240 to 120 mmrespectively).More specifically,the differencebetween the lasers and the DipstickR atthe smaller samplinginterval was approximatelyone fifth of its value at largersamplingintervals. These average differences also indicate that a convergence of sorts took placebetween the two alternative heightsensingdevices at the lower samplingresolution rate6 . That is this chart also appears to suggest that the ultrasonics' agreementwith theDipstickR improvedas samplingresolution decreased,or samplinginterval increased.

16

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igure 13 Laser based IRI

igure 14 Ultrasonic based IRI

17

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Figure 15 orrelation with Dipstick

Figure 16 verage Instrument Difference

18

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Speed

The approachtaken to evaluate the relative performanceof ultrasonic and laserheightsensors at varyingvehicle speedsw s much the s me as that used to ssess theeffects of samplingrate. Once again,scatter plots for each speedwere prepared,as in

Figure17 and Figure18. These plots were supplementedby a correlation analysis,theresults of which are summarized in Figure19. The best performanceagainasdetermined throughcorrelation with the DipstickR w s returned by the ultrasonicssensors operatedat 48 and 64 kilometers per hour 30 and 40 mph). In fact, on averagethe ultrasonics demonstrateda higherdegreeof correlation than the laser at every speedtested.

Figure 17. Laser based IRI

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Figure 18 Ultrasonic based IRI

Figure 19 Correlation with Dipstick

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This series of tests also suggestedthat an increase in collection speedw saccompaniedby an increased disagreementbetween the DipstickR-basedIRI and thatestimated by the SDRP As speedw s increased to 80 kilometers per hour 50 mph , theultrasonic-based roughnesstends to creep up with respect to data based on the DipstickR

Figure20). The oppositeeffect w s noted with the laser instruments,which tended tounderestimateroughnessat higherspeeds. The bar chart depictingthe difference betweenestimated ultrasonic and laser roughnessshows an almost perfectlylinear increase withthe increase in the collection vehicle speed. Figure21 represents a correlation analysiscomparinginstrument difference and speed. Throughthis analysis,a positivecorrelationcoefficient of nearlyunity w s observedbetween speedincrease and the difference in IRIbetween ultrasonics and lasers. As collection speedincreased,either the ultrasonicroughnessincreased with respect to laser roughnessor the laser roughnessdecreasedwithrespect to the ultrasonics. The s me chart indicated a fairly strongnegativecorrelationbetween laser roughnessand that based on the DipstickR Surprisingly,any correlationbetween speedchangeand the difference betweenultrasonic and DipstickR roughnessappearednegligible.This suggests that the laser equipmentw s indeed exhibitingasensitivityto speed;as speedincreased,the laser-based systemconsistentlyunderestimatedthe roughness.From that, t may be surmised that the apparentcorrelation between the ultrasonics and lasers w s due almost exclusivelyto a sensitivityof lasers to speed.

Figure 20. AverageInstrument Difference

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0.8

0.6

0.4

0.2

-0.4

-0.6

-0.8

Compared Devices

Figure 21. Correlation of EquipmentDifference and Speed

DIS USSION

Under the conditions at which these tests were conducted,both heightsensingdevices performedwell,comparedto one another and to the control information.

Device Footprint Comparison

A closer look at the physicaldetails of the sampling proceduresused by the

respectiveinstruments may

providesome

explanationfor the

surprisinglygoodperformanceregisteredby the ultrasonics,particularlyas comparedto the Dipstick•

Figure22). First, the footprintof the ultrasonic device,albeit a sound wave-front insteadof a circular metal disk, is more nearlyon the same order of magnitude,size-wise,as theDipstickl• While the size of the Dipstick• moon feet are 64 2 1/2 in) and theultrasonic transducer s footprinton the surface is about 50 2 in),the Selcom infraredlaser beam measures onlyabout 2 0.08in) in diameter.

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• l l l l l l IIIIII III II Ill Ill

/• • Dipstick63.5 mmLaser 2 mm Diameter, Diameter,Sampled2,000 Samples/Second, at 305 mm Intervals

Averagedat180 mm Intervals) Acoustic 51 mm Diameter,

Sampledat 360 mm Intervals)Figure22. Sensor FootprintComparison

In addition to footprintsize, the method of samplingand recordingthe surfaceprofilewith the DipstickR is more like that used by the ultrasonic heightsensors than thelasers. Both the DipstickR and the ultrasonic devices measure the profilein discrete steps.The lasers, on the other hand,collect data continuously,storingaverageheightsat thespecifiedintervals of distance. Theoretically,the lasers miss less surface than either theultrasonics or the dipstick.Conversely,the DipstickR and the ultrasonic sensors may notsee as much roughnessas the lasers. By the same token,there are cases where theresolution of the lasers can theoreticallyidentifymore roughnessthan is actuallyrelevant.

Finally,as the tests cycledthroughvaryingsamplingrates, those tests withintervals most closelyresemblingthe 300 mm 1.0 foot)Dipstickg foot spacingreturnedthe highestdegreeof correlation. The laser samplingrate that correlated best was at asamplinginterval of 240 mm slightlyless resolution than routinelyused withlaserroughnessmeasurements).

Instrument Reliability

Anissue that had not been targetedfor formal investigation,but which has provento be critical, is relative hardwarereliability. Unfortunately,failures and malfunctionsof

ultrasonic sensor units have become fairly routine. In fact, between June 1993 andSeptember1994 recordskeptby the N T unit show that at least 6 sensor repairs at $275each)were necessary. For some of this time period,there was only one pure ultrasonicSDRP vehicle in operation.For the periodof time that coveredthe 1995 spring-summer-fall constructionseason the number of ultrasonic repairsnecessarywas at least as high.

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Periodic sensor failure is certainlynot desirable. However,a more perplexing,and potentiallydetrimental difficultysurfaced later in the 1995 construction seasonData collection in support of a related studyhad begunwhen the operators first noticedthat the left wheel path (LWP)roughness,as measured by the LWP ultrasonic sensor, wasstartingto drift up and away from the laser-based readings.This occurred without

warningfrom the built-in hardware or software checks and would likely have continuedunnoticed for some time, had there not been accompanyinglaser information. Theproblempersisteduntil the operator had replacedthe sensor three times,implyingthat thedifficultymay be a problemwith the system in general,and not necessarilydue to amalfunctioningsensor Duringfall repeatabilitywork, the drift in ultrasonic roughnesswas againobserved. This time,however,the phenomenonwas present and very obviousin the right wheelpath.Data available from springsurveys of the same section (whenallsensors were functioningproperlyindicated that there was indeed more roughnessin theright wheelpaththan the left. In the spring,the difference between ultrasonic and laserroughnessin the right wheelpath(RWP)was 13 percent, while the LWP difference was alittle over 9 percent. With the suspectedmalfunctioningsensor in the fall surveys, theLWP difference was still just past 5 . The RWP discrepencyhad more than doubled to27 percent. Figure23 illustrates that out of the ten runs conducted in the fall test, theright wheelpathroughness as estimated usingultrasonic-based profiles was obviouslyoverestimated in at least eight.

Figure 23. Sensor Malfunction

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Obviously,a malfunctionof this nature could have been disastrous had t occurredwhile administeringrideabilityspecificationswith pure ultrasonic instruments. Hardwarereliabilitymay be the most compellingreason to migrateto pure laser roughnessmeasurement.

CONCLUSIONS

The majorityof the tests described herein were conducted under conditions thatare generallyconsidered ideal for ultrasonic profilers.The weather was dry, temperaturesmoderate,and pavementsurfaces fairlymundane. Unfortunately,these temperedconditions fail to expose the equipmentto those ambient scenarios where the laserinstruments would likelyhave performedbest by comparison.For example,experiencehas shown that the slightestmistingof precipitationwill generallyincapacitateultrasonics,while anecdotal experimentshave failed to registerany reaction from thelasers. Previous research has also shown that temperatureextremes can contribute toerratic results with ultrasonic sensors. 4 Theoretically,fluctuations in temperaturehave noeffect on lasers.

The findingsfrom this studysupport the followingconclusions

1 Laser-basedroughnessmeasurement is more repeatablethan ultrasonic-basedmeasurement.

2. At most samplingrates and vehicle speeds,ultrasonic-basedroughnessmeasurements correlate with

the DipstickR

as wellor

better than laser-based.3. However,over a range of surfaces,laser-based numbers differ less from theDipstickR than ultrasonics,and the difference decreases with increasedsamplingresolution withspeedheld constant).

4. Estimated laser-based IRI decreases as speedincreases especiallyas speedsare increased from below 64 km/h).

5. Laser instruments were more reliable than ultrasonics.

RECOMMEND TIONS

If current trends continue,VDOT s network roughnesssurveyingwill soon beconducted almost exclusivelyby privatecontractors. This will not, in the author'sopinion,eliminate the need for VDOT to maintain the technical expertiseand physicalcapacityto ensure that the State continues to receive qualityinformation.

In more specificterms, the followingrecommendations are offered:

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1 Use ultrasonic devices carefullyfor network level surveyingwhere all surfacesarereasonablysimilar and more localized assessments are not expected.A strictcalibration regimentis imperative.The reliabilityissue, which in many cases isassociated with moisture,may make ultrasonic-basedequipmentmore practicalin

dry, moderate climates not necessarilyVirginia).2. Use laser devices for all other typesof network level surveying,especiallywhen thenetwork includes a broad range of surfacetypes. Moreover, t is the opinionof theauthor that any future network surveys conductedor authorized by VDOT shouldavoid ultrasonic sensors and instead specifya laser preference.

3. Use laser devices for any work conducted at the project level. Actually,thesuitabilityof a givenSDRP for projectlevel work is most dependenton the accuracyand calibration of the distance measuringequipment,alongwith the abilityof thehardware and software to synchonizeinstruments. This research has shown,however,that the data collected with the lasers are generallymore repeatableand thus, moreappropriatefor use where detail is critical.

4. Lastly, it is stronglysuggestedthat VDOT migrate to pure laser-based SDRPequipment. It is likewise recommended that any additional SDRP systems, purchasedor leased by The Department,be equippedwith laser-typeheightsensors

FUTURE RESE RCH

There has been much discussion regardingthe potentialuse of the SDRP forestablishingand administeringsmoothness specificationsfor maintenance overlays.Adraft

specificationhas been

developedandwork in

support of t alreadyinformallyinitiated. It is suggestedthat continued research in the road profilingprogram be devotedto formal support of the overlayride qualityspecification.Numerous obstacles threatenthe successful implementationof this specification.Not the least of these obstacles is ashared uncertaintyregardingthe many factors that influence the achievable smoothness ofan overlay. A non-comprehensivelist of issues that likelyaffect the rideabilityof a newasphaltoverlayinclude

• the ride qualityof the overlaid pavement• the predominantdistress of the overlaid pavement• the mix type and thickness• the surveyedage and cummulative traffic loadings• the experienceand skill level of a contractor's crew* the types and condition of the contractor's equipment• the placementrate maintainedby a contractor

An understandingof how these typesof variables influence the ride qualityof overlayswould be invaluable to publicofficials,the contractingindustry,and highwayusers

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CKNOWLEDGEMENTS

A number of peoplecontributed to the data collection,data analysis,and reportpreparationfor this project. The author is especiallyappreciativeof the efforts of L. E.Wood,Jr. engineeringtechnician. Thanks are also extended to J. W. French and A. J.

Wagnerof the Research Council, and D. H. Thackerand G. S. McReynoldsof the MaterialsNon-Destructive TestingSection. This research was conducted under the generaldirection ofDr. GaryR. Allen,Director of the Research Council. Specialguidancewas also providedbyT. E. Freeman,Senior Research Scientist,D. H. Grigg,Jr. District Materials Engineer,andM. A. Bowyer,TransportationEngineerProgramSupervisorof the Materials Division.

REFERENCES

1. Hilton,M. H. 1964. BridgeDeck Roughness.VirginiaCouncil of HighwayInvestigationand Research, Charlottesville.

2. Shelburne,T. E. 1965. Road RoughnessMeasurements-- 1965 Construction.Memorandum VirginiaCouncil of HighwayInvestigationand Research,Charlottesville.

3. McGhee,K. H. 1978. WorkingPlan. Road RoughnessSpecificationStudies. VHTRC 78-WP 17. VirginiaHighwayand TransportationResearch Council, Charlottesville.

4. McGhee,K. H. 1992. Evaluationof a K.J. Law Model 8300 Ultrasonic RoughnessTestingDevice. VTRC 93-R3. VirginiaTransportationResearch Council, Charlottesville.

5. HighwayPerformanceMonitoringReportField Manual. 1987. U.S. DepartmentofTransportation,Federal HighwayAdministration,Washington,D.C.

6. Descriptionand Evaluation of the South Dakota Road Profile.1989.FHWDemonstration ProjectNo. 72 (Addition),FHWA-DP-89-072-002,Washington,D.C.

7. Gramling,W. L. 1994. Current Practices in DeterminingPavement Condition. NCHRPSynthesisof HighwayPractice 203. TransportationResearch Board, National RsearchCouncil, Washington,D.C.

8. Gillespie,T. D., et al. 1986. NCHRP Report228. Calibration of Response-TypeRoadRoughnessMeasuringSystems.TransportationResearch Board, National Research Council,Washington,D.C.

9. Perera,R. W., and Starr, D. K. 1994. Road ProfilerData Analysisand Correlation FinalReport.Research Report No. 92-30. Soil and Materials Engineers,Inc., Plymouth,Michigan.