Effect of Increased Precipitation (Heavy Rain Events) on Minnesota Pavement Foundations

Project Details
STATUS

In-Progress

START DATE

01/28/22

END DATE

01/31/24

FOCUS AREAS

Infrastructure

RESEARCH CENTERS InTrans, PROSPER
SPONSORS

Minnesota Department of Transportation

Researchers
Principal Investigator
Halil Ceylan

Director, PROSPER

Co-Principal Investigator
In-Ho Cho
Co-Principal Investigator
Sunghwan Kim

Associate Director, PROSPER

Co-Principal Investigator
Eugene S. Takle

About the research

This study will perform a long-term assessment of Minnesota air and soil temperatures, precipitation, and freeze-thaw events, to determine the susceptibility of pavement foundation to heavy precipitation events, and to provide a foundation vulnerability assessment of the Minnesota Department of Transportation (MnDOT) road network in response to such heavy precipitation.

A two-phase research approach was developed to achieve these objectives. In the Phase 1 research, a long-term assessment of Minnesota air and soil temperatures, precipitation, and freeze-thaw events was performed. Analysis of Minnesota weather data indicated a 0.5 to 1 in. increase in precipitation before and at the end of the cold season. Based on the findings from Phase I, researchers will study the implications of increased precipitation due to climate change and the performance of road foundations using weather stations and extensive instrumentation data available at MnROAD while also developing a vulnerability map for the state road network.

Focusing on these goals, thin-walled Shelby tube samples will be collected from the MnROAD test facility to determine the hydraulic and thermal properties of pavement materials, in addition to a comprehensive review of existing laboratory data available in the MnROAD database. The thermal and hydraulic properties will be compiled with the in-situ monitoring data to develop mechanistic-based models for predicting the susceptibility of pavement foundation to heavy precipitation events. The developed mechanistic-based prediction models will generate a pavement foundation vulnerability map using a geographic information system (GIS).

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