About the research
Granular roads account for more than 75% of roads managed by Iowa counties. Granular roads experience rapid deterioration and more quickly develop various localized problems compared to paved roads. Frequent and regular maintenance is needed to keep the roadway performance at a desired reliability over its lifetime. Current granular road asset management practices are primarily on an ad hoc and reactive basis, which may not be the most efficient approach. The lack of a reliable and practical tool to estimate gravel loss over time, and thus the required amount of aggregate (rock) that needs to be purchased, is one of the major problems that local agencies currently face.
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.