Roughness Model Sample Clauses

Roughness Model. The HDM roughness model uses faulting, spalling, transverse cracking, patching, and initial roughness after original construction. It does not account for studded tire wear, which is considered one of the primary factors affecting roughness on WSDOT pavements. The use of studded tires during the winter in Washington State, which averages about 10 percent of vehicles in Western Washington and 32 percent of vehicles in Eastern Washington (WSDOT, 2005), seems to be the primary contributor to wheelpath wear in PCC pavements. Wear depths range from barely measurable up to about 0.75 inches, depending upon pavement age and location.
Sample 1
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Roughness Model. The NCHRP 1-37A roughness model does not consider studded tire wear. The model only considers inputs of transverse cracking, spalling, faulting, and a related site factor (based mostly on local climate). The elasticity for each factor is 0.011 for cracking, 0.003 for spalling, 0.077 for faulting, and 0.003 for the related site factor, where faulting has a much larger elasticity than other factors. On the basis of elasticity, the roughness condition is mainly dependent on faulting. As with the faulting model, the most critical input factors for roughness were base type, traffic load, and climate (figures 25, 26 and 27). The differences among inputs were quite similar to those of faulting, and the progression curves also had the same trend. Roughness (m/km) 1 0 10 20 30 40 Time since Original Construction (year) Granular Base ATB Figure 25 Default NCHRP 1-37A estimated IRI vs. base type (9–in. undoweled slabs, 9– in. base, 15-ft. joint spacing, 1.6 million ESALs/year/designate, Seattle). Roughness (m/km) 1 0 0 10 20 30 40 Time since Original Construction (year)
Sample 1
Roughness Model. Calibration results: The roughness model was calibrated in three groups: undoweled, undoweled for mountain passes, and DBR. The calibrated curves for undoweled pavements are shown in Figure 33. Figure 34 shows the estimation for DBR sections. 3,4 5 1 Granular, high traffic, MP Roughness (m/km)
Sample 1
Roughness Model. RIt = Kjp * (RI + 2.6098 * TFAULT + 1.8407 * SPALL + 2.2802 *10−6 * TCRACKS3 ) Where: RIt roughness at time t (inch/mile). Kjpr calibration factor for roughness (default t= 1) RI0 initial roughness at the time of pavement construction (inch/mile). Use=98.9 as the default. TFAULT total transverse joint faulting per mile (in/mile), and given by FAULT * 5280 . JTSPACE JTSPACE average transverse joint spacing (ft). SPALL percentage of spalled joints. TCRACKS total number of cracked slabs per mile, and given by PCRACK * 5280 . JTSPACE *100 PCRACK the percentage of slabs cracked with transverse crack. Because WSDOT has no such data, the transverse cracking estimated by HDM (using the default calibration factor for cracking) is used here. APPENDIX B: NCHRP 1-37A PCC PAVEMENT DETERIORATION MODELS
Sample 1
Roughness Model. IRI = IRII + C1*CRK + C2* SPALL + C3*TFAULT + C4* SF Where, IRI predicted IRI (inch/mile). IRII initial smoothness measured as IRI (inch/mile). CRK percent slabs with transverse cracks (all severities). SPALL percentage of joints with spalling (medium and high severities). Given by SPALL = ⎡ AGE ⎤ ⎡ 100 ⎤ ⎢⎣ AGE + 0.01⎥⎦ ⎢⎣1+1.005(−12*AGE+SCF ) ⎥⎦ SCF scaling factor based on site-, design-, and climate-related variables. Given by SCF = −1400 + 350 * AIR% *(0.5 + PREFORM ) + 3.4 fc * 0.4 − 0.2 ( FTCYC * AGE ) + 43hPCC − 536WC _ Ratio AGE pavement age since construction (year). AIR% PCC air content (percent). PREFORM 1 if preformed sealant is present; 0 if not. fc PCC compressive strength (psi). FTCYC average annual number of freeze-thaw cycles HPCC PCC slab thickness (inch). WC_Ratio PCC water/cement ratio. TFAULT total joint faulting cumulated per mi (inch). C1, C2, C3, and C4 calibration factors. SF site factor. Given by AGE (1 + 0.5556 * FI )(1 + P200 ) *10−6 . FI freezing index (oF-days).
Sample 1

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