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

SPONSOR:  MICHIGAN DEPARTMENT OF TRANSPORTATION

PI:  Zhen Liu

Spring (or Seasonal) Load Restriction (SLR) policies that limit the axle loads of trucks have been implemented in many states of the United States and other countries to minimize costly roadway damage that occurs in seasonally frozen areas during the annual spring thaw and strength recovery period (Zarrillo et al., 2012). This is because concrete and asphalt, though look indestructible, can actually be quite fragile in late winter as frost comes out of the ground (County Road Association of Michigan).

The overall objective of the project is to establish a thawing model and a process for setting and removing seasonal load restrictions in a manner that will give industry the most amount of time to prepare for the restrictions and minimize the time to lift the restrictions based on the MDOT Project RC 1619. The overall objective will be accomplished through a series of objectives and tasks leveraging existing research, technology, and resources that MDOT already has in place.

  1. Evaluate existing thaw/freeze depth prediction models, practice for SLR in state DOTs and MDOT’s needs and available resources, and based on that, determine if existing thaw depth models suffice for application as a decision support tool for Michigan or if a refined model would be prudent.
  2. Identify the type, sources, and format of the soil and weather information used for analysis by the decision support tool.
  3. Building on this project and the research of RC 1619, develop a thaw depth model th!it utilizes the existing data sources in Objective 2.
  4. Identify locations for potential virtual Road Weather Information System (RWIS) sites and collect necessary data to implement those locations.
  5. Develop a user friendly decision support tool that could be easily utilized by public and private sector in estimating potential thaw conditions and setting of SLRs for any location on the MDOT road network.
  6. Recommend processes for predicting the time to post and remove SLR signs to protect the pavement structures from excessive damage during the spring thaw season.
  7. Identify opportunities to collect, present, and apply data and develop models to refine pavement designs.
  8. Develop professional training materials and course for training MDOT staff in the use of the decision support tool.
Zhen Liu
Zhen Liu
Stan Vitton
Min Wang
Min Wang