The freeze–thaw cycle is one of the major sources of damage to granular-surfaced roadways, especially in areas where the timing of heavy agricultural traffic coincides with that of spring thawing. To help local roads agencies plan better for annual budgets and frost embargos, it is useful to be able to predict the frost depth and number of freeze–thaw cycles under a given roadway based on continually updated weather and soil data. Computational modeling can help in this regard, and may be conducted by collecting data on weather and the thermal and hydraulic properties of the soil, as well as soil temperature, moisture, and suction, and using the data directly in the analyses. In order to obtain accurate field data for model calibrations and predictions, an appropriate sensor network and data acquisition system must be carefully planned and installed. This article details the development and installation procedures for one such system of sensors for subgrade temperature, water content, and matric suction, and presents lessons learned throughout the process. Various issues are discussed relating to selection of the sensor and data acquisition system, laboratory and field checks, borehole sensor installation tools, and post-installation troubleshooting and monitoring. To ensure a successful installation beneath the granular roadway, laboratory and field trials were first performed. Salient details of a pilot installation in Hamilton County, IA are provided to guide others developing and scaling similar subgrade sensor systems.

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The performance of granular-surfaced roadways is greatly affected by annual freeze-thaw cycles, which can cause severe structural damage to the road surface. The collection of in-situ subgrade soil temperature and moisture data as well as local weather data is very important to improve our ability to understand and predict subgrade behavior during freeze-thaw cycles. In this study, the use of data from a weather station installed adjacent to a granular road test section is compared to data interpolated from the two nearest weather stations of a statewide network, to compare their performance as inputs for freeze-thaw simulations using the Simultaneous Heat and Water (SHAW) model. The subgrade temperature and moisture content predictions from the simulations are compared to those measured by sensors installed at several depths below the soil surface at the center of the test section. The results suggest that using weather input data from the statewide weather station network may result in reasonably accurate predictions from SHAW simulations.

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