ROUGHNESS MODEL DESCRIBING HEARY VEHICLE-PAVEMENT INTERACTION

The pavement roughness characteristics that affect interaction between pavement and heavy vehicles are addressed. A roughness model describing the pavement roughness attributes affecting heavy vehicles is presented. Dynamic vehicle response data from two sources were analyzed, namely, experimental data obtained with the instrumented vehicle developed by the National Research Council of Canada and simulated data obtained with a quarter-vehicle simulation. It was found that the vehicle response parameter of interest in this interaction is the sprung mass vehicle acceleration because it relates to both pavement and vehicle damage as well as to ride quality and cargo damage. This was demonstrated by analyzing the transfer functions of both the dynamic axle load and the vertical sprung mass acceleration over a range of pavement roughnesses and vehicle speeds. The sprung mass vertical acceleration transfer function showed sensitivity to a pavement roughness excitation frequency of 3.5 Hz. A pavement roughness statistic was proposed that is calculated as follows: (a) calculate the spectral density of the pavement roughness profile, (b) multiply this spectral density by the square of a transfer function to obtain the spectral density of the vertical sprung mass acceleration of the reference quarter vehicle selected, and (c) calculate the integral of the spectral density of the vertical sprung mass acceleration over the full frequency spectrum and take the square root. The resulting statistic has units of energy per unit mass per unit length of pavement traveled and represents the energy input from the road to the vehicle and vice versa.

Language

  • English

Media Info

  • Features: Figures; References;
  • Pagination: p. 50-59
  • Monograph Title: Pavement-vehicle interaction and traffic monitoring
  • Serial:

Subject/Index Terms

Filing Info

  • Accession Number: 00715515
  • Record Type: Publication
  • Files: TRIS, TRB
  • Created Date: Dec 28 1996 12:00AM