Asif Ahmed

Sub-surface soil hydrological characterization is one of the challenging tasks for engineers and soil scientists, especially the complex hydrological processes that combine key variables such as soil moisture, matric suction, and soil temperature. The ability to infer these variables through a singular measurable soil property, soil resistivity, can potentially improve sub-surface characterization. This research leverages various machine learning algorithms to develop predictive models trained on a comprehensive dataset of sensor-based soil moisture, matric suction, and soil temperature obtained from prototype ET covers, with known resistivity values. Different types of sensors were installed at multiple depths in the ET covers, and resistivity tests were conducted periodically at the same location. Cross-validation and feature selection methods were used to optimize model performance and identify key variables that most significantly impact soil resistivity. Strong inverse correlations between soil moisture and resistivity (r = −0.88) and weak positive correlations with temperature (r = 0.41) and suction (r = 0.34) were observed. Among the machine learning models evaluated, artificial neural networks and support vector machines demonstrated superior predictive performance, achieving a coefficient of determination (R²) above 0.77 and lower root mean square error (RMSE) values (less than 0.14). Linear regression and decision tree models exhibited suboptimal performance because of their limitations in capturing non-linear relationships and overfitting, respectively. Random forest demonstrated superior generalization capabilities compared to decision trees; however, it encountered challenges with mid-range data variability. The findings demonstrate the effectiveness of artificial neural networks in predicting field-scale soil resistivity by utilizing hydrological variables.

Electrical resistivity tomography (ERT) has turned out to be one of the most applied and user-friendly geophysical methods in geotechnical and geoenvironmental research. ERT is an emerging technology that is becoming popular nowadays for investigating subsurface conditions. Multiple attributes of the technology using various electrode configurations significantly reduce measurement time and are suitable for applications even in hardly accessible mountain areas. It is a noninvasive test for subsurface characterization and a very sensitive method used to determine geophysical properties, i.e., structural integrity, water content, fluid composition, etc. This paper aimed to elucidate the ERT technique’s main features and applications in geotechnical and geoenvironmental engineering through four case studies. The first case study investigated the possible flow paths and areas of moisture accumulation after leachate recirculation in a bioreactor landfill. The second case study attempted to determine the moisture variation along highway pavement. The third case study explored the slope failure investigation by ERT. The fourth case study demonstrated the efficiency of the ERT method in the landfill evapotranspiration (ET) cover to investigate moisture variation on a broader scale and performance monitoring. In all of the four cases, ERT exhibited promising performance.

In this paper, soil water characteristic curves (SWCCs) were reconstructed through simultaneous field measurement of soil water content and suction value. The objective of this study was to evaluate whether field-based monitoring could capture the different SWCC paths in various seasons. Moreover, field measurement of SWCC makes it possible to quantify the hysteresis phenomenon, analyzes the effect of various temporal resolutions on SWCC, and highlights the differences in reconstructed SWCC for the same soil during various weather conditions. Field data from one expansive subgrade site in Texas, USA were collected through moisture and suction sensors over a 3-year monitoring period. Analyzing the rainfall and evapotranspiration data, three distinct wetting and drying cycles were identified which allowed correct discrimination of the cycles. The pattern of changes in SWCC was identified in the various wetting and drying cycles. While the field data was fitted through the Van Genuchten equation, variations of the unsaturated soil parameters ( α, n, m) were identified in various cycles. Air entry value indicator parameter α ranged from 0.05 to 0.11 kPa⁻¹ while the slope of the SWCC curve parameter n value varied from 1.1 to 1.45. Comparing these results with previous studies, it was concluded that wetting and drying cycle parameters do not have any predefined correlation. However, these unsaturated parameters required for numerical modeling are dependent on the climatic events happening year-round.

Due to the representation of a particular field condition of soil rather than the real time scenario from laboratory experiments, the selection of unsaturated permeability and flow parameters becomes challenging when conducting numerical modeling. Keeping this in mind, the objective of the study was to determine the permeability in both directions along with the unsaturated flow parameters from field data. Although it is conventional to determine the flow parameters from the curve fitting of laboratory results, a novel approach was carried out during the course of study, wherein field soil water characteristic curves were used to determine the unsaturated flow parameters. Two two-lane roads in Kaufman County and Ellis County, Texas were selected for data acquisition and monitoring in this study. For the investigation of in situ moisture content and matric suction, soil moisture and suction sensors were installed at up to a depth of 4.5 m into the ground, while the precipitation was recorded using rain gauges installed at the sites. Field determination yielded hydraulic conductivity values in the range of 10⁻⁴ to 10⁻⁵ m/s, representing the rapid flow of water due to desiccation cracks on expansive soil. Field-generated unsaturated flow parameters also indicated variability while constructing the SWCC. Finally, PLAXIS 2D was used for the transient flow analysis. The close agreement of the FE results with the direct field measurements validated the estimated flow parameters. The approach described in the study can be used for determining permeability and unsaturated flow parameter values from field data, which offers a dynamic situation in contrast to the static laboratory condition.

The American Society of Civil Engineers’ (ASCE) quadrennial report card exhibited the grim picture of nation’s transportation infrastructure