NSF Award
2100780
Research Initiation Awards provide support for junior and mid-career faculty at Historically Black Colleges and Universities who are building new research programs or redirecting and rebuilding existing research programs. It is expected that the award helps to further the faculty member's research capability and effectiveness, improves research and teaching at the home institution, and involves undergraduate students in research experiences. The award to Savannah State University will support research to develop machine learning models that will be used as an aggregate quality classification tool by highway agencies. The project will also develop an undergraduate research program that will prepare undergraduate students for careers in transportation geotechnics.<br/><br/>The primary goal of this project is to analyze and catalogue physical, mineralogical, and chemical properties of representative coarse aggregates used by different U.S. State Departments of Transportation (DOTs) in pavement construction and to identify possible relationships within those properties and pavement performance using Machine Learning (ML) approach. Chemical, physical shape, and petrographic properties especially grain size and composition of the minerals will be correlated to determine what influence, if any, one property might have on another to developed ML based prediction models. Those petrographic characteristics with the significant correlations with shape properties will be considered the most useful predictive properties. This comprehensive study of the fundamental properties of aggregates will help to better understand material quality related aggregate type and selection factors and how they affect pavement performance.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
