Develop and Implement a Freeze Thaw Model Based Seasonal Load Restriction Decision Support Tool

US Transportation Collection2019PDFTech ReportLiu, Zhen (Leo)Vitton, StanleyBillmire, MichaelWang, MinMichigan Technological UniversityMichigan Department of Transportation (MDOT)Michigan. Dept. of Transportation. Office of Research AdministrationMichigan. Dept. of Transportation. Office of Research AdministrationUnited StatesMichiganDecision support systemsDepthForecastingFreezingGeographic information systemsMobile applicationsRoad weather information systemsSize and weight regulationsStatistical analysisThawValidationFreeze-ThawSpring Load RestrictionFreezing IndexFrost DepthThawing IndexThawing DepthRoad Weather Information SystemWeb-Based AppOR16-0092016-0067Final Report 5/01/2017 to 3/1/2020This report summarizes a study on the development and implementation of a freeze thaw model based Seasonal Load Restriction (SLR) decision support tool. A multivariable prediction approach for freezing and thawing depths were proposed. The approach was implemented with input from weather data and Road Weather Information System (RWIS) data, leading to statistical models for site-specific predictions. Predictions made with the approach were validated against freezing and thawing depths calculated with subsurface temperatures measured by temperature sensors at the Michigan Department of Transportation (MDOT) RWIS sites. A detailed procedure was proposed for predicting the start and end dates of the SLR policy, and this procedure was evaluated and validated with frost tube measurements and recorded SLR dates. The above freezing and thawing depth predictions and SLR date predictions were automated in a web-based app, i.e., www.mdotslr.org, which is available to the public. For app, weather and RWIS data starting from 2013 and Geographic Information System (GIS) data covering Michigan were imported and managed as local databases on the backend server. In addition, daily weather and RWIS data including weather 5-day forecast were imported via APIs in real time for real-time for predictions of freezing and thawing depths and SLR dates. The app provides functions for predicting and visualizing temperature, freezing/thawing indices, degree of SLR, and SLR dates in terms of curves and contour maps. Maximum freezing depth contours can also be generated for any given period of time for pavement design and other purposes. All the data is available via the data portal of the app and can be downloaded. The study provides high-accuracy methods for predicting freezing and thawing depths and SLR dates and a convenient web-based tool for road engineers and users.125