National Center for Wood Transportation Structures

About the research

The Midwest region of the United States, including Minnesota, has been experiencing an increase in the number of heavy precipitation events. Historical precipitation data confirmed an increasing trend of heavy precipitation in Minnesota in the 21^st century. This study focused on assessing the impact of heavy-precipitation events on moisture levels and stiffness of pavement foundation layers at the MnROAD facility.

A two-step approach was adopted for predicting changes in saturation and for estimating corresponding resilient modulus values using the resilient modulus prediction equation employed in AASHTOWare Pavement Mechanistic-Empirical (ME) design. PLAXIS 3D, a finite element analysis tool, was used to simulate the movement of moisture within the pavement layer under varying heavy rainfall scenarios. Multiple linear regression models were developed from rainfall simulation data of the PLAXIS 3D model to predict base layer saturation based on rainfall characteristics and hydraulic conductivity of the material. ArcGIS Pro was then used to develop a framework to generate a preliminary vulnerability map showing changes in the resilient modulus of the pavement base layer from rain events. Four regression models were developed and used in ArcGIS Pro to predict changes in resilient modulus for distinct aggregate types under heavy rainfall events, revealing significant reductions in the base layer’s resilient modulus. Recycled aggregate (a mix of recycled concrete aggregate and recycled asphalt pavement) emerged as more susceptible, with initial reductions in modulus values higher under heavy rainfall.