Title :
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
fDate :
31 Mar-2 Apr 1996
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;
Conference_Titel :
System Theory, 1996., Proceedings of the Twenty-Eighth Southeastern Symposium on
Conference_Location :
Baton Rouge, LA
Print_ISBN :
0-8186-7352-4
DOI :
10.1109/SSST.1996.493466
