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From page 58...
... 47 CHAPTER FIVE ENHANCEMENT OF MODELING IN PAVEMENT ENGINEERING INTRODUCTION Pavement performance is a measure of the extent to which a pavement fulfills its principal objective. Performance models are tools to predict performance; they may ultimately be used in pavement management systems, in the structural design of pavements, and in the development of performance-related specifications.
From page 59...
... 48 0% 20% 40% 60% 80% 100% 0 0.5 1 1.5 2 2.5 3 n/N P ro ba bi lit y of fa ilu re 0.5 1 2 3 5 FIGURE 12 Weibull probability of failure distribution for different β-values. TABLE 1 SOME MATHEMATICAL MODELS USED TO DESCRIBE DAMAGE WITH LOADING Models Equation Nbay ⋅+= Linear 2NcNbay ⋅+⋅+= Quadratic L+⋅+⋅+⋅+= 32 NdNcNbay Polynomial )
From page 60...
... 49 Multiple (linear or nonlinear) regression analysis may be used to determine regression constants.
From page 61...
... 50 Rut depth may be related to trafficking in the same way that it may be related to subgrade strain. Odermatt et al.
From page 62...
... 51 12 10 R ut D ep th , m m 8 6 y = 0.004x 0.59264 FIGURE 13 Subgrade rutting model of TxMLS tests on Pad F5 in Victoria. The model shown in Figure 13 was derived relating the rut depth on the surface to applied TxMLS loads for one of the test sections using data provided in the paper by Chen and Lin (1999)
From page 63...
... 52 total surface rut. Current research is therefore aimed at developing permanent deformation models for individual pavement layers, to enable the designer to predict each layer's contribution to the total permanent deformation of the pavement system.
From page 64...
... 53 subgrade CBR ranging from 4% to 28%. They pointed out that no single subgrade criterion is appropriate for all conditions.
From page 65...
... 54 tests on subgrade materials. The model considers the moisture content of the subgrade material.
From page 66...
... 55 differed significantly; however, the performance cannot be attributed to differences in base material because of the different temperatures and rainfalls monitored during the tests on these sections.
From page 67...
... 56 the s Material characterization parameters are also included in asphalt permanent deformation modeling of APT data.
From page 68...
... 57 TA VARIAB TO PREDICT ASPHALT RUTTING (Hand Description BLE 2 LES USED Traffic Initial oids fic mum gyrations Dust proportion SA ft2/lb FT Microns #4GRAD50 n/a p200 Percent Sbit kPa hange in rut depth in pr ent -- ESALs, Delta SALs, or cumulative ESALs in-place air v et al.
From page 69...
... 58 A model was between the other ruts known as "Affected Rut(s) " and the "Bench t, uence factors: • Temperature (F )
From page 70...
... 59 This equation represents the onset of cracking in a relavely thin asphalt surface over a dense-graded base on a silty– low-volume road section failed after ions of a 35 months of tr ayer, mode of ading, rest periods, healing, etc. Furthermore, these mod997)
From page 71...
... 60 Equation (3)
From page 72...
... 61 -0.05 1.E+00 1.E+01 1.E+02 1.E+03 1.E+04 1.E+05 1.E+06 1.E+07 1.E+08 Number of Repetitions to Fatigue Failure FIGURE 14 Comparison of FSHCC and PCC fatigue performance. stresses in the slab were back-calculated from measured flections.
From page 73...
... 62 longitudinal unevenness difficult. This is not serious, beause riding quality is not necessarily related to the strucdeformaon through observation of in-service highways.

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