Application of the Machine Learning Method to Determine Spring Load Limits and Winter Weight Premium

Abstract

Freight transportation plays a crucial role in sustaining the Canadian economy. 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 Premium (WWP) is used to reduce the impact of SLR on trucking operations by allowing higher axle loads in winter. However, existing policies apply fixed dates each year for these restrictions, regardless of the actual structural capacity of the pavement. Different methods have been proposed to improve the application of SLR and WWP; however, they rely mainly on indirect indices, such as the cumulative thawing index and cumulative freezing index, which pose challenges in their calculation. This study explores the practical implementation of machine learning models for accurately determining the start and end dates of SLR and WWP. In a novel approach, machine learning models directly derive the start and end dates of SLR and WWP from frost and thaw depths in the pavement structure which are determined by pavement temperature and moisture content. In contrast to previous studies that neglected the influence of soil moisture content on determining the start and end dates of SLR and WWP, this study examines the variation in soil moisture content to evaluate the validity of existing theories. The findings reveal a high level of agreement between the machine learning model’s estimations of frost and thaw depths and the measured values, with R2 values exceeding 0.91.