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Abstract
Pavement roughness is a key parameter for controlling pavement construction processes and for assessing ride quality during the life of a pavement system. This paper describes algorithms used in processing three-dimensional (3D) stationary terrestrial laser scanning (STLS) point clouds to obtain surface maps of point wise indices that characterise pavement roughness. The backbone of the analysis is a quarter-car model simulation over a spatial 3D mesh grid representing the pavement surface. With the rich data-set obtained by 3D scanning, the algorithms identify several dynamic responses and inferences (suspension, acceleration and jerk) at each point in the domain. Variability in the indices is compared for a ‘rough’ pavement and a ‘smooth’ pavement in the spatial domain for different speed simulations of the quarter-car model. Results show high spatial variability in the various roughness indices both longitudinally and transversely (i.e. different wheel path positions). It is proposed that pavement roughness characterisation using a spatial framework coupled with univariate statistics provides more details on the severity and location of pavement roughness features compared to the (1D) one-dimensional methods. This paper describes approaches that provide an algorithmic framework for others collecting similar STLS 3D spatial data to be used in advanced pavement roughness characterisation.