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Demonstration and Inter-Comparison of Seasonal Weight Restriction Models - Phase II

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    Final Report
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  • Abstract:
    Many miles of roads are in seasonal frost areas that are highly susceptible to damage during the spring thaw period. As freezing occurs from the surface downward, moisture is drawn toward the freezing front, ice lenses are formed, and the roadways become stronger. When the ice lenses (above still-frozen underlying layers) melt, the structure is left in an undrained, unconsolidated condition that is highly susceptible to damage under traffic. In seasonal frost areas, some state departments of transportation (DOTs) take advantage of the period of higher strength in mid-winter by using winter weight premiums (WWPs) to increase the allowable weight that trucks can haul. Conversely, to reduce damage during the spring thaw, many road management agencies apply spring load restrictions (SLRs). The objective of this effort was to provide an understanding of the reliability, benefits, costs, and risks of alternate approaches to scheduling seasonal load restrictions on roadways. The major tasks addressed in this Phase II report were to implement the models recommended from Phase I at demonstration sites and to calibrate those models, if required. The models included both degree-day threshold and frost-thaw depth prediction models. Output from the models was then compared with validation data provided by the DOTs involved in this study. These validation data consisted of subsurface temperature data (which were reduced by the research team to compute frost and thaw depths) and, in some cases, deflection and/or stiffness data from lightweight deflectometer (LWD) and falling weight deflectometer (FWD) tests. With the results of these implementation and validation efforts, recommendations based on accuracy, simplicity of use, and cost were developed to aid road management agencies in selecting which model or protocol is most appropriate for their intended purposes, personnel, and specific conditions.
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    urn:sha-512:ea9ba7760e82a19980bd2bc471824f1d644af488c2b885c48d360316a1b1b83b2c949befca5eea727c3da18c29a3dc8bb9cf912aba46c6092889eedee3a3f725
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