Research Initiation Award : Investigation of Petrographic and Mineralogical Properties of Aggregates Influencing Pavement Performance

研究启动奖为传统黑人学院和大学的初级和中期职业教师提供支持，他们正在建立新的研究项目或重新指导和重建现有的研究项目。期望该奖项有助于提高教师的研究能力和效率，改善所在机构的研究和教学，并使本科生参与研究经验。该奖项授予萨凡纳州立大学，将支持开发机器学习模型的研究，该模型将被公路机构用作综合质量分类工具。该项目还将开发一个本科生研究项目，为本科生在交通岩土工程领域的职业生涯做好准备。该项目的主要目标是分析和分类美国不同州交通部门（DOTs）在路面施工中使用的代表性粗骨料的物理、矿物学和化学特性，并使用机器学习（ML）方法确定这些特性与路面性能之间的可能关系。化学、物理形状和岩石学性质，特别是矿物的粒度和组成，将相互关联，以确定一种性质可能对另一种性质产生什么影响（如果有的话），从而开发基于ML的预测模型。那些与形状属性相关性显著的岩相特征将被认为是最有用的预测属性。对集料基本特性的全面研究将有助于更好地理解与集料类型和选择因素相关的材料质量，以及它们如何影响路面性能。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.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.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.