Covering the Northern part of the Province, Ontario’s low-volume roads provide a link from remote resource areas to markets. Thus, preserving this transportation asset from the two main sources of pavement deterioration, namely traffic loading and the environment is extremely critical to the movement of goods and to the economy. In particular, Northern Ontario’s secondary highways are challenged by a combination of heavy, low frequency traffic loading and a high number of freeze-thaw cycles for which most of these highways have not been structurally designed. Therefore they experience environmental damage and premature traffic-induced deterioration. To cope with this issue, the Ontario Ministry of Transportation places Spring Load Restrictions (SLR) every year during spring-thaw. For economic reasons, the duration of SLRs is usually fixed in advance and is not applied proactively or according to conditions in a particular year. This rigidity in the schedule needs to be addressed, as it can translate into economic losses either when the payload is unnecessarily restricted or when pavement deterioration occurs. While the traditional approaches are usually qualitative and rely on visual observations, engineering judgment and historical records to make SLR decisions, the latest approaches resort to climatic and deflection data to better assess the bearing capacity of the roadway. The main intent of this research was to examine how the use of a predictor for frost formation and thawing could improve the scheduling of load restrictions by tracking the frost-strengthening and thaw-weakening of the pavement structure. Based on field data captured in Northern Ontario, and on a preliminary analysis that found good correlation between frost thickness in the roadway and Road Weather Information Systems (RWIS) variables, more advanced frost and thaw predictors were developed as part of this research and are presented herein. The report outlines how the model was developed, details the calculation algorithms, and proposes an empirical methodology for a systematic site-specific calibration. This research also involved several experimental and numerical tools, including the use of a Portable Falling Weight Deflectometer (PFWD) to estimate pavement strength during spring thaw, and the use of the Mechanistic-Empirical Pavement Design Guide (MEPDG) software to simulate the impact of SLR on the performance of typical Northern Ontario low volume roads.

Covering the Northern part of the Province, Ontario’s low-volume roads provide a link from remote resource areas to markets. Thus, preserving this transportation asset from the two main sources of pavement deterioration, namely traffic loading and the environment is extremely critical to the movement of goods and to the economy. In particular, Northern Ontario’s secondary highways are challenged by a combination of heavy, low frequency traffic loading and a high number of freeze-thaw cycles for which most of these highways have not been structurally designed. Therefore they experience environmental damage and premature traffic-induced deterioration. To cope with this issue, the Ontario Ministry of Transportation places Spring Load Restrictions (SLR) every year during spring-thaw. For economic reasons, the duration of SLRs is usually fixed in advance and is not applied proactively or according to conditions in a particular year. This rigidity in the schedule needs to be addressed, as it can translate into economic losses either when the payload is unnecessarily restricted or when pavement deterioration occurs. While the traditional approaches are usually qualitative and rely on visual observations, engineering judgment and historical records to make SLR decisions, the latest approaches resort to climatic and deflection data to better assess the bearing capacity of the roadway. The main intent of this research was to examine how the use of a predictor for frost formation and thawing could improve the scheduling of load restrictions by tracking the frost-strengthening and thaw-weakening of the pavement structure. Based on field data captured in Northern Ontario, and on a preliminary analysis that found good correlation between frost thickness in the roadway and Road Weather Information Systems (RWIS) variables, more advanced frost and thaw predictors were developed as part of this research and are presented herein. The report outlines how the model was developed, details the calculation algorithms, and proposes an empirical methodology for a systematic site-specific calibration. This research also involved several experimental and numerical tools, including the use of a Portable Falling Weight Deflectometer (PFWD) to estimate pavement strength during spring thaw, and the use of the Mechanistic-Empirical Pavement Design Guide (MEPDG) software to simulate the impact of SLR on the performance of typical Northern Ontario low volume roads.