Rolling resistance is one of the key factors that affect the fuel efficiency of the national pavement system. In addition to pavement texture and pavement roughness, the dissipation of mechanical work provided by the vehicle because of viscous deformation within the pavement structure has been recognized as a relevant factor contributing to the environmental footprint of pavement systems. This dissipation depends on material and structural parameters that can be optimized to increase the fuel efficiency of pavements. Identifying the key material and structural parameters that drive this dissipation is the focus of this paper. This identification is achieved by a combination of dimensional analysis and model-based simulations of the dissipation of a viscoelastic beam on an elastic foundation. For linear viscoelastic systems, the dissipation is found to scale with the square of the vehicle weight and with the inverse of the viscous relaxation time, in addition to distinct power relations of top-layer stiffness, thickness, and subgrade modulus. These scaling relations can be used by pavement engineers to reduce such pavement-inherent dissipation mechanisms and increase the fuel efficiency of a pavement design. An example shows the application of these scaling relations with data extracted from FHWA's Long-Term Pavement Performance database for seven road classes. The scaling relations provide a means for evaluating the performance of the various road classes in terms of the fuel efficiency related to dissipation.

The accuracy and the comprehensiveness of any pavement life-cycle assessment are limited by the ability of the supporting science to quantify the environmental impact. Pavement–vehicle interaction represents a significant knowledge gap that has important implications for many pavement life-cycle assessment studies. In the current study, the authors assumed that a mechanistic model that linked pavement structure and properties to fuel consumption could contribute to closing the uncertainty gap of pavement–vehicle interaction in life-cycle assessment of pavements. The simplest mechanistic pavement model, a Bernoulli–Euler beam on a viscoelastic foundation subjected to a moving load, was considered. Wave propagation properties derived from falling weight deflectometer time history data of FHWA's Long-Term Pavement Performance program were used to calibrate top-layer and substrate moduli for various asphalt and concrete systems. The model was validated against recorded deflection data. The mechanistic response was used to determine gradient force and rolling resistance to link deflection to vehicle fuel consumption. A comparison with independent field data provided realistic order-of-magnitude estimates of fuel consumption related to pavement–vehicle interaction as predicted by the model.

The dissipation occurring below a moving tire in steady-state conditions in contact with 5 a viscoelastic pavement is expressed using two different reference frames, a fixed observer 6 attached to the pavement, and a moving observer attached to the pavement-tire contact sur-

No abstract

Pavement roughness affects rolling resistance and thus vehicle fuel consumption. When a vehicle travels at constant speed on an uneven road surface, the mechanical work dissipated in the vehicle's suspension system is compensated by vehicle engine power and results in excess fuel consumption. This dissipation depends on both road roughness and vehicle dynamic characteristics. This paper proposes, calibrates, and implements a mechanistic model for roughness-induced dissipation. The distinguishing feature of the model is its combination of a thermodynamic quantity (energy dissipation) with results from random vibration theory to identify the governing parameters that drive the excess fuel consumption caused by pavement roughness, namely, the international roughness index (IRI) and the waviness number, w (a power spectral density parameter). It is shown through sensitivity analysis that the sensitivity of model output, that is, excess fuel consumption, to the waviness number is significant and comparable to that of IRI. Thus, introducing the waviness number as a second roughness index, in addition to IRI, allows a more accurate quantification of the impact of surface characteristics on vehicle fuel consumption and the corresponding greenhouse gas emissions. This aspect is illustrated by application of the roughness–fuel consumption model to two road profiles extracted from FHWA's Long-Term Pavement Performance database.