Comprehensive in-situ freeze-thaw monitoring under a granular-surfaced road system. (May 2022)

• Record Type: Journal Article
• Authors: Genc, Derya; Ashlock, Jeramy C.; Cetin, Bora; Ceylan, Halil; Cetin, Kristen; Horton, Robert
• Is Part Of: Transportation geotechnics. Volume 34(2022)
• Publication Date: 2022-05
• Subjects: Granular-surfaced roadways -- Freeze-thaw -- Soil temperature -- Volumetric water content -- Matric potential -- Instrumentation; Engineering geology -- Periodicals; Soil mechanics -- Periodicals; Rock mechanics -- Periodicals; Transportation -- Periodicals; 624.15105
• DOI: 10.1016/j.trgeo.2022.100758
• Languages: English
• ISSNs: 2214-3912

Abstract

Abstract: Annual freeze-thaw cycles significantly impact the performance of granular-surfaced roadways in cold regions. Various methods are employed to limit the damage caused by these events. A seasonal load restriction (SLR) is one of these methods that is imposed by local roads agencies according to the subgrade condition. Subsurface monitoring is a reliable method to help such agencies determine the status and freeze-thaw behavior of the subgrade in real-time. To help improve the capabilities for predicting the depths and timing of soil freeze-thaw events, a comprehensive subgrade sensor network was installed below a granular-surfaced roadway in Hamilton County, Iowa. The network continuously measured temperature, volumetric water content and matric potential at selected depths below the subgrade surface, and also recorded atmospheric conditions using a weather station installed next to the instrumented roadway section. In this study, the collected dataset for the winter 2019–2020 freeze-thaw period was analyzed using contour mapping over the entire road cross-section. The results of the study revealed unsymmetrical behavior under the road surface. The freezing front was found to be deeper towards the west midpoint and shallower at the shoulders. Volumetric water content and matric potential measurements were also found to be coherent with the temperature data during freezing and thawing periods. In addition, soil water retention curves of matric potential versus water content determined in the laboratory were compared to the curves derived from the field measurements. The two datasets were found to be quite different due to both predictable and unpredictable field conditions.