1、Designation: E 1489 08Standard Practice forComputing Ride Number of Roads from Longitudinal ProfileMeasurements Made by an Inertial Profile MeasuringDevice1This standard is issued under the fixed designation E 1489; the number immediately following the designation indicates the year oforiginal adopt
2、ion or, in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval. Asuperscript epsilon () indicates an editorial change since the last revision or reapproval.1. Scope1.1 This practice covers the mathematical processing oflongitudinal profile m
3、easurements to produce an estimate ofsubjective ride quality, termed Ride Number (RN).1.2 The intent of this practice is to provide the highwaycommunity a standard practice for the computing and reportingof an estimate of subjective ride quality for highway pave-ments.1.3 This practice is based on a
4、n algorithm developed inNational Cooperative Highway Research Project (NCHRP)123 (1 and 2),2two Ohio Department of Transportation ridequality research projects (3 and 4), and work presented in Refs(5 and 6).1.4 The computed estimate of subjective ride quality pro-duced by this practice was named Rid
5、e Number (RN) inNCHRPResearch Project 123 (1 and 2) to differentiate it fromother measures of ride quality computed from longitudinalprofile. Eq 1 of Section 8.2 represents the mathematicaldefinition of Ride Number.1.5 This standard does not purport to address all of thesafety concerns, if any, asso
6、ciated with its use. It is theresponsibility of the user of this standard to establish appro-priate safety and health practices and determine the applica-bility of regulatory limitations prior to use.2. Referenced Documents2.1 ASTM Standards:3E 177 Practice for Use of the Terms Precision and Bias in
7、ASTM Test MethodsE 867 Terminology Relating to Vehicle-Pavement SystemsE 950 Test Method for Measuring the Longitudinal Profileof Traveled Surfaces with an Accelerometer EstablishedInertial Profiling ReferenceE 1170 Practices for Simulating Vehicular Response toLongitudinal Profiles of Traveled Surf
8、acesE 1364 Test Method for Measuring Road Roughness byStatic Level MethodE 1500 Practice for Computing Mean Square Numericsfrom Road Surface Elevation Profile Records4E 1656 Guide for Classification of Automated PavementCondition Survey EquipmentE 1927 Guide for Conducting Subjective Pavement RideQu
9、ality Ratings3. Terminology3.1 Terminology used in this standard conforms to thedefinitions included in Terminology E 867.3.2 Definitions of Terms:3.2.1 Rideability Index (RI)an index derived from con-trolled measurements of longitudinal profile in the wheel tracksand correlated with panel ratings o
10、f rideability.3.2.2 Ride Number (RN)rideability index of a pavementusing a scale of 0 to 5, with 5 being perfect and 0 beingimpassable.4. Summary of Practice4.1 The practice presented here was developed specificallyfor estimating subjective ride quality from longitudinal profilemeasurements.4.2 This
11、 practice uses longitudinal profile measurements fortwo wheel tracks as an input to a mathematical computation ofestimated subjective ride quality (RN). The profile must berepresented as a series of elevation values taken at constantintervals along the wheel tracks.4.3 The range of the computed subj
12、ective ride qualityestimate is 0 to 5.0 Ride Number (RN) units where an RN of5.0 is considered to be a perfect ride quality road. The 0 to 5.0Ride Number scale is defined in Refs (1, 2, 3, 4, and 5).4.3.1 In the 0 to 5.0 Ride Number rating scale, the endpoints and some of the intermediate points hav
13、e the followingdescriptions:1This practice is under the jurisdiction of ASTM Committee E17 on Vehicle -Pavement Systems and is the direct responsibility of Subcommittee E17.33 onMethodology for Analyzing Pavement Roughness.Current edition approved June 1, 2008. Published June 2008. Originallyapprove
14、d in 1996. Last previous edition approved in 2007 as E 1489 98 (2007).2The boldface numbers in parentheses refer to the list of references at the end ofthis standard.3For referenced ASTM standards, visit the ASTM website, www.astm.org, orcontact ASTM Customer Service at serviceastm.org. For Annual B
15、ook of ASTMStandards volume information, refer to the standards Document Summary page onthe ASTM website.4Withdrawn.1Copyright ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959, United States.Ride Number Rating ScaleDescription Ride NumberPerfect 5.0Very good 4
16、.54.0Good 3.53.0Fair 2.52.0Poor 1.51.0Very poor 0.5Impassable 0.04.3.2 The end points are further defined by the followingdescriptions (1):Perfect: A road which is so smooth that at the speed you are travel-ing you would hardly know the road was there. You doubtthat if someone made the surface smoot
17、her that the ridewould be noticeably nicer.Impassable: A road which is so bad that you doubt that you or the carwill make it to the end at the speed you are travelinglikedriving down railroad tracks along the ties.4.4 The quality of the computed subjective ride qualityestimate (RN) produced by this
18、practice is based on theprocessing of the longitudinal profile as measured with a roadprofile measuring device that meets the Class 1 requirements ofASTM Standard E 950.NOTE 1Less accurate Ride Number values will result from RoadProfile Data obtained from Profile Measuring Devices that are lessaccur
19、ate than class I (E 950).5. Significance and Use5.1 This practice provides a means for obtaining a quanti-tative estimate of a pavement property defined as ride qualityor rideability using longitudinal profile measuring equipment.5.1.1 The Ride Number (RN) is portable because it can beobtained from
20、longitudinal profiles obtained with a variety ofinstruments.5.1.2 The RN is stable with time because true RN is basedon the concept of a true longitudinal profile, rather than thephysical properties of particular type of instrument.5.2 Ride quality information is a useful input to the pave-ment mana
21、ge systems (PMS) maintained by transportationagencies.5.2.1 The subjective ride quality estimate produced by thispractice has been determined (6) to be highly correlated (r =0.92) with measured subjective ride quality and to produce alow standard estimate of error (0.29 RN units) for the ridequality
22、 estimate.5.2.2 The subjective ride quality estimates produced by thispractice were found to be not significantly different withrespect to pavement type, road class, vehicle size, vehiclespeed (within posted speed limits), and regionality over therange of variables included in the experiment (1, 2,
23、3, and 4).5.2.3 The subjective ride quality estimates produced by thispractice have been found to be good predictors of the need ofnon-routine road maintenance for the various road classifica-tions (3).5.3 The use of this practice to produce subjective ridequality estimates from measured longitudina
24、l profile eliminatesthe need for expensive ride panel studies to obtain the sameride quality information.6. Longitudinal Profile Measurement6.1 The elevation profile data used in this practice musthave sufficient accuracy to measure the longitudinal profileattributes that are essential to the comput
25、ation of estimatedsubjective ride quality.6.1.1 The quality of the Ride Number estimates cited in thispractice are based on the use of elevation profile measurementsmade with a Class 1 road profile measuring device as definedin Test Method E 950.6.1.2 Wave Length ContentThe measured longitudinalprof
26、ile used as input to this practice must have the wavelengthcontent required for the application.6.1.2.1 As a guide to the wavelength requirement, a repeat-ing sine wave of the following wavelengths and peak-to-peakamplitudes in the absence of any other roughness will producethe following RN values:A
27、mplitude, Wavelength,mm m RN25.4 91.4 4.95Amplitude, Wavelength,inch feet RN1.00 300 4.956.1.2.2 The quality of Ride Number estimates cited in thispractice are based on measured longitudinal profile withwavelength content up to 91.4 m (300 feet).7. Precision and Bias7.1 Precision and BiasThe accurac
28、y of the computedsubjective ride quality estimate produced by this practice willbe a function of the accuracy of the longitudinal profilemeasurements.7.1.1 CorrelationThe ride quality estimates (RN) com-puted by this practice have been determined to have acorrelation coefficient of .92 (r) with actu
29、al measured subjec-tive ride quality (3, 4, 5, and 6).7.2 Standard Error of EstimateThe ride quality estimates(RN) computed by this practice have been determined to havea Standard Error of Estimate of .29 RN units when comparedto actual measured subjective ride quality (3, 4, 5, and 6).7.3 The Corre
30、lation Coefficient and Standard Error of Esti-mate values cited in Sections 7.1 and 7.2 are based onlongitudinal profile measurements made with a road profilemeasuring device that meets the requirements of a Class 1measuring device as defined by ASTM Standard E 950 andwavelength content up to 100 m
31、(300 feet).7.4 It is not known how road profile measuring equipmentwith lesser resolution and precision and greater bias wouldaffect the accuracy of computed ride numbers.8. Ride Number Program8.1 This practice consists of the computation of RideNumber (RN) from an algorithm developed in National Co
32、op-erative Highway Research Project (NCHRP) 123 (1 and 2),two Ohio DOT ride quality research projects (3 and 4), and thework presented in Refs (5) and (6).E14890828.2 Ride Number is defined in this practice by the equation:RN 5 5e2160PI!(1)where:PI 5PIL21 PIR22(2)and where:PILand PIRare Profile Inde
33、xes for the left and right wheelpaths, respectively, and are the computed root mean square(RMS) of the filtered slopes of the measured elevation profilesof the individual right and left wheel paths (6). The wavelength components of the profile slopes are modified by thefilter shown in Fig. 7 (6).8.3
34、 A FORTRAN computer version of this algorithm hasbeen implemented as described in Ref (6).8.3.1 This practice presents a sample computer program“RNSMP” for the computation of the Ride Number equationfrom the recorded longitudinal profile measurement.8.3.1.1 The computer program RNSMP is a general co
35、m-puter program that accepts the elevation profile data set asinput, and then calculates the Ride Number using the equationpresented in 8.2.8.3.1.2 A listing of the RNSMP computer program for thecomputation of the Ride Number transform equation is in-cluded in this practice as Appendix X1.8.3.1.3 A
36、provision has been made in the computer programlisting (Appendix X1) for the computation of the Ride Numbertransform equation from recorded longitudinal profile measure-ments in both SI and inch-pound units.8.3.2 The input to the sample Ride Number computerprogram is an ASCII profile data set stored
37、 in a 1X, F8.3, 1X,F8.3 Fortran format. In this format, the profile data appears asa multi-row, two-column array with the left wheel path profiledata points in column 1 and the right wheel path points incolumn 2. The profile data point interval is discretionary.However, the quality of the Ride Numbe
38、r estimates cited in thispractice, are based on a data point interval of 150 mm (6 in.)(see Section 5).8.3.2.1 If the input to the Ride Number computer program isin SI units, the elevation profile data points are scaled inmillimetres with the least significant digit being equal to 0.001mm.8.3.2.2 If
39、 the input to the Ride Number computer program isin inch-pound units, the elevation profile data points are scaledin inches with the least significant digit being equal to 0.001inch.8.4 The distance interval over which the Ride Number iscomputed is discretionary, but shall be reported along with the
40、Ride Number results.8.5 Validation of the Ride Number program is requiredwhen it is installed. Provisions for the RN program installationvalidation have been provided in this practice.8.5.1 The sample profile data set TRIPULSE.ASC has beenprovided in SI units in Appendix X2 for validation of thecomp
41、uter program installation.8.5.2 Using the sample profile data set TRIPULSE.ASC(Appendix X2) as input to the Ride Number computer program(Appendix X1), a Ride Number of 3.66 was computed asshown in Appendix X3 for a profile data point interval of .15m (.5 feet) and a distance interval equal to 15 met
42、ers of theprofile data set.9. Report9.1 The report for this practice shall contain the followinginformation:9.1.1 Profile Measuring DeviceThe report data shall in-clude the ASTM Standard E 950 classification of the deviceused to make the measurements, the date of the last successfuldevice calibratio
43、n, and the highpass filter wavelength used inthe profile measurement.9.1.2 Longitudinal Profile MeasurementsReport datafrom the profile measuring process shall include the date andtime of day of the measurement, the location of the measure-ment, the lane measured, the direction of the measurement,le
44、ngth of measurement, and the descriptions of the beginningand ending points of the measurement.9.1.3 Profile Data Point Interval (Sample Interval) andProfile Reporting IntervalRide Number report data shallinclude the profile data point interval and the profile reportinginterval.9.1.4 Ride Number Res
45、ultsThe Ride Number resultsshould be reported to two decimal places along with thedistance interval over which the RN was computed.10. Keywords10.1 longitudinal profile; mean panel ratings (MPR); panelrating; pavement ride quality; profile index; rideability; ridenumber (RN); subjective ride quality
46、; subjective ride qualityestimateE1489083APPENDIXES(Nonmandatory Information)X1. RIDE NUMBER COMPUTER PROGRAMX1.1 Included as Fig. X1.1 is a sample Fortran computerprogram using user-selected SI or inch-pound units that can beused to compute the Ride Number specified by this practice. Ifthe SI optio
47、n is selected, the program assumes the input roadprofile amplitudes are stored in millimetre units, if inch-pound,inches. In the sample program, the profile data sample intervalis assumed to be either .15 m or .5 feet depending on the optionselected. For the sample program, the maximum road sectionl
48、ength that can be processed is 160.9 metres (528 feet).X1.2 The elevations in the sample data file shown inAppendix X2 are in SI units (mm) and contain one thousandprofile data points. If the inch-pound option is selected, the usermust convert the profile data set from SI units to inch-poundunits to
49、 get the inch-pound output shown in Appendix X3.FIG. X1.1 Sample Fortran Program Using Subroutine GETPI to Compute Ride Number Index.E1489084FIG. X1.1 Sample Fortran Program Using Subroutine GETPI to Compute Ride Number Index (continued)E1489085FIG. X1.1 Sample Fortran Program Using Subroutine GETPI to Compute Ride Number Index (continued)E1489086FIG. X1.1 Sample Fortran Program Using Subroutine GETPI to Compute Ride Number Index (continued)E1489087FIG. X1.1 Sample Fortran Program Using Subroutine GETPI to Compute Ride Number Index (continued)E1489088FIG. X1.1
