In Ontario, low volume roads comprise about 20% (3,715 center line kilometers) of the total provincial highway network. These roads are subjected to infrequent but intensive traffic loading as well as a high number of freeze‐thaw cycles. During the thaw periods, the water released by the subsurface materials is trapped beneath the pavement resulting in weakening of the base, sub‐base and sub‐grade materials causing premature deterioration of the pavement. As a result, seasonal load restrictions (SLRs) are applied during the spring thaw to low volume highways which are not structurally designed to carry heavy loads during saturated periods. Currently, methods used to apply SLRs are based on visual observation, field testing, prescheduled dates, and empirical models. If the application and removal dates used for SLRs effectively coincide with the load‐carrying ability of the pavements, pavement damage, pavement maintenance costs and economic losses to industries will be minimized. There is a need to develop a rational, quantitative procedure to determine the best time to apply SLRs based on measured or predicted frost conditions and pavement response. As a consequence, a primary objective of this research is to develop models that can be used to estimate the pavement strength as a function of frost/thaw depths, characteristics of pavement structures and other variables. For this purpose, the first step is to explore the application of thermal numerical modelling to estimate frost/thaw depths based on variables related to climate, pavement, base, sub‐base and sub‐grade conditions. Once these models are developed and calibrated, the second step would be to relate the frost/thaw depths to pavement strength. This research also uses the Mechanistic Empirical Pavement Design Guide (ME‐PDG) software to evaluate the pavement performance in terms of rutting, cracking, and surface roughness considering SLRs during the design life of the pavement and to observe changes in these values when simulated SLRs are included in the ME‐PDG modelling. The focus of this paper is on the second step as well as the ME‐PDG results. Information such as pavement type and thickness, soil characteristics, traffic loading, and other available material properties for the selected sites were collected. Additionally, deflection data from the pavement have been collected using a portable light weight deflectometer (LWD). The work of this research is in progress and this paper presents preliminary results obtained up to date.
