Abstract
Low-volume roads covering the northern part of Ontario, Canada, are a critical asset; they enable the movement of goods from remote resource areas to markets. However, challenged by a combination of heavy, low-frequency traffic loading and a high number of freeze-thaw cycles for which most have not been structurally designed, such highways often experience seasonal damage and premature traffic-induced deterioration. To mitigate these impacts, the Ontario Ministry of Transportation and other departments of transportation place seasonal load restrictions (SLRs) every year during the spring thaw. For economic reasons, the duration of SLRs is usually fixed in advance and is not applied according to conditions in a particular year. Rigidity in the schedule may result in economic losses because the payload can be unnecessarily restricted or pavement deterioration can occur. The latest attempts to address this issue include the use of climatic and deflection data to assess the bearing capacity of the roadway better. The use of frost and thaw depth predictors to track spring thaw weakening could improve the scheduling of load restrictions. On the basis of field data captured in Northern Ontario, a good correlation was found between the amount of frost depth in the pavement and weather conditions monitored by road weather information systems. An empirical methodology for site-specific calibration of the predictors is proposed, and the steps toward its development and the calculation algorithms are detailed.
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