首页|Research from University of Alberta Has Provided New Study Findings on Machine L earning (Application of the Machine Learning Method to Determine Spring Load Lim its and Winter Weight Premium)
Research from University of Alberta Has Provided New Study Findings on Machine L earning (Application of the Machine Learning Method to Determine Spring Load Lim its and Winter Weight Premium)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on artificial intelligence is now available. According to news originating from Edmonton, Canada, by NewsR x correspondents, research stated, "Freight transportation plays a crucial role in sustaining the Canadian economy." Our news journalists obtained a quote from the research from University of Alber ta: "However, heavy truck transportation also puts enormous pressure on roadway networks. Spring Load Restrictions (SLR) are implemented to minimize road damage caused by heavy traffic during the thaw-weakening season, and Winter Weight Pre mium (WWP) is used to reduce the impact of SLR on trucking operations by allowin g higher axle loads in winter. However, existing policies apply fixed dates each year for these restrictions, regardless of the actual structural capacity of th e pavement. Different methods have been proposed to improve the application of S LR and WWP; however, they rely mainly on indirect indices, such as the cumulativ e thawing index and cumulative freezing index, which pose challenges in their ca lculation. This study explores the practical implementation of machine learning models for accurately determining the start and end dates of SLR and WWP. In a n ovel approach, machine learning models directly derive the start and end dates o f SLR and WWP from frost and thaw depths in the pavement structure which are det ermined by pavement temperature and moisture content."
University of AlbertaEdmontonCanadaNorth and Central AmericaCyborgsEmerging TechnologiesMachine Learning
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