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Abstract. Seasonal climatic variations in subgrade soil affect pavement responses and can reduce pavement serviceability. In addition to causing shrinkage and swelling in expansive subgrade, variations in moisture suction may alter the material properties of soil, which ultimately affect pavement performance. The current study monitored the seasonal variation of subgrade moisture content, rainfall, and pavement deformation of a section over State Highway 342, in Dallas, Texas. Moisture sensors were installed at different depths up to 4.5 m. The soil was found to be highly plastic clay (CH) in the selected site. In addition to acquiring real-time moisture data from sensors, geophysical testing was also conducted on the slope of the pavement. Electrical Resistivity Imaging (ERI) was carried out at the slope of the instrumented pavement section to observe the moisture flow at the edge of the pavement. Based on the field monitoring data, moisture variation ranged from 5% to 14%, with higher moisture contents correlating with rainfall events. While moisture sensors provided point information, resistivity imaging yielded a continuous portrayal of subsurface moisture flow. Furthermore, rainfall-associated deformation of the pavement was monitored. Based on the monitored data, it was observed that pavement deformation varied with rainfall. A total deformation of 38 mm was recorded over the monitoring period.

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Moisture variations significantly influence the strength and stiffness of expansive subgrade soils, shortening the service lives of pavements and increasing the associated maintenance costs. Accurate measurements of soil moisture can be obtained through soil sampling and testing, but the process can be extensive and costly. Empirical models can accurately predict the moisture variations in an expansive subgrade in a shorter period of time, with lower accompanying costs. The objective of the current study was to develop moisture models, using real-time field monitoring data from two hot mix asphalt roads in North Texas. The collected data were analyzed in a statistical environment to solve two first degree Fourier series. The solution produced a moisture variation model that captured variations associated with seasonal effects and temporary variations associated with rainfall. The outputs of this model were within 90% of the values measured on site. Application of the developed models will facilitate noninvasive estimations of the response of soil strength and stiffness properties to variations in moisture.

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