Description

This research project developed a data-driven Granular Roadway Asset Management System (GRAMS) to assist local agencies in making more reliable gravel loss estimates and consequently determining annual aggregate (rock) requirements for proper budgeting purposes. In this study, a series of online and in person meetings and interviews were conducted along with electronic mailing surveys to gather information to develop a Microsoft Excel-based user-friendly GRAMS. Advanced statistical analysis methods such as the beta regression model and survival analysis were used as computational algorithms for estimation and risk analysis. When the user enters several input values, the GRAMS can generate a range of estimates for varying budget conditions and different levels of service. The tool is expected to significantly help local agencies to obtain consistency in terms of estimating gravel loss and determining aggregate (rock) requirements, and as a result, they can better defend their granular road maintenance budget requests and management. This tool is primarily based upon empirical data, and further calibration is recommended for enhanced estimation.