Stochastic modelling of pavement roughness

Author

Zhu, J. Jim ; Wenli Zhu

Author_Institution

Dept. of Electr. & Comput. Eng., Louisiana State Univ., Baton Rouge, LA, USA

fYear

1996

fDate

31 Mar-2 Apr 1996

Firstpage

28

Lastpage

32

Abstract

Pavement roughness is usually characterized by a one figure statistic of pavement profile data. This approach discards a rich body of useful pavement information in the pavement profile data. In this study, informative parametric models for pavement roughness are developed where pavement roughness is treated as a stochastic signal. Based on this stochastic modeling of roughness, a new stochastic roughness index (SRI) is proposed. Case studies conducted on 18 FACE Dipstick profile data and 25 profile data collected using an Ames Profilograph shows that the new SRI, irrespective to the profiling devices, has a good correlation with the International Roughness Index

Keywords

autoregressive processes; filtering theory; parameter estimation; signal processing; surface topography; Ames Profilograph; International Roughness Index; parametric models; pavement profile data; pavement roughness; stochastic modelling; stochastic roughness index; stochastic signal; Autocorrelation; Deformable models; Measurement units; Parametric statistics; Probability distribution; Random variables; Sampling methods; Stochastic processes; Strontium; Tin;

fLanguage

English

Publisher

ieee

Conference_Titel

System Theory, 1996., Proceedings of the Twenty-Eighth Southeastern Symposium on

Conference_Location

Baton Rouge, LA

ISSN

0094-2898

Print_ISBN

0-8186-7352-4

Type

conf

DOI

10.1109/SSST.1996.493466

Filename

493466