Abstract

The pavement roughness is the main variable that produces the vertical excitation in vehicles. Pavement profiles are the main determinant of (i) discomfort perception on users and (ii) dynamic loads generated at the tire-pavement interface, hence its evaluation constitutes an essential step on a Pavement Management System. The present document evaluates two specific techniques used to simulate pavement profiles; these are the shaping filter and the sinusoidal approach, both based on the Power Spectral Density. Pavement roughness was evaluated using the International Roughness Index (IRI), which represents the most used index to characterize longitudinal road profiles. Appropriate parameters were defined in the simulation process to obtain pavement profiles with specific ranges of IRI values using both simulation techniques. The results suggest that using a sinusoidal approach one can generate random profiles with IRI values that are representative of different road types, therefore, one could generate a profile for a paved or an unpaved road, representing all the proposed categories defined by ISO 8608 standard. On the other hand, to obtain similar results using the shaping filter approximation a modification in the simulation parameters is necessary. The new proposed values allow one to generate pavement profiles with high levels of roughness, covering a wider range of surface types. Finally, the results of the current investigation could be used to further improve our understanding on the effect of pavement roughness on tire pavement interaction. The evaluated methodologies could be used to generate random profiles with specific levels of roughness to assess its effect on dynamic loads generated at the tire-pavement interface and user’s perception of road condition.

Highlights

  • Pavement roughness is the main variable that produces vertical excitation on vehicles

  • From the results presented above, one can conclude that random profiles generated using a sinusoidal approach present International Roughness Index (IRI) values that are representative of a large variety of road types, from paved roads (Category A and B) with mean IRI values of 0,18 m/km and 1,8 m/km, to unpaved roads (Category E) with expected IRI values of 14,1 m/ km

  • Pavement profiles constitute a fundamental input on investigations directed to understand tire pavement interaction

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Summary

Introduction

Pavement roughness is the main variable that produces vertical excitation on vehicles. Shaping filter approach the mean squared value of road surface roughness, that is the total area of the Power Spectral Density function, does not change with the velocity of a vehicle Simulink of Matlab can be used to solve the process described by Equation (13) to generate pavement random profiles, the entry of the process is the standard deviation of the white noise process which depends of the pavement category, a 80 km / h ~ 22,2 m/s speed and a value of α = 0,127 m−1 are set. The expected IRI values obtained for the categories A, B, C, D and E using the GEV were 0,814 m/km, 1,151 m/ km, 1,633 m/km, 2,285 m/km y 3,248 m/km, respectively

Discussion of results
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