The reuse of recycled crushed concrete aggregate (RCCA) and reclaimed asphalt pavement (RAP) can provide a sustainable solution for the disposal of C&D waste materials instead of sending them to landfills. More importantly, it will conserve energy and reduce environmental concerns. Several states in the USA have been using RCCA and RAP as base materials for years, focusing on the quality of the recycled materials. The structure of Recycled Aggregate (RA) is more complex than that of Natural Aggregate (NA). RAs have old mortar adhered on them that forms a porous surface at the interfacial transition Zone (ITZ) and prevents new cement mix from bonding strongly with the aggregates. The objective of this study was to correlate microstructural properties such as microporosity, inter and intra aggregate pores with the unconfined compressive strength (UCS) of RAP and RCCA molds, mixed at different proportions. In this paper, the quantity of micropores and their effect on the strength of mixed materials is used as the basis of microstructural analysis of recycled concrete and reclaimed asphalt. Microstructural properties obtained from analyzing scanning electron microscope (SEM) images were correlated with unconfined compressive strength. Intra-aggregate and interaggregate pores were studied for different ratios of cement treated mixture of RAP and RCCA. The results show that addition of RAP considerably increases the number of pores in the mixture, which eventually causes reduction in unconfined compressive strength. In addition, significant morphological and textural changes of recycled aggregates were observed by SEM image analysis.

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.

The tendency of expansive subgrade soil to undergo swelling and shrinkage with the change in moisture has a significant impact on the performance of the pavement. The repeated cycles of wet and dry periods throughout a year lead to considerable stress concentration in the pavement subgrade soil. Such stress concentrations leads to the formation of severe pavement cracks. The objective of the research is to develop a prediction model to estimate the deformation of pavement over expansive subgrade. Two pavement sites—one farm to market road and one state highway—were monitored regularly using moisture and temperature sensors along with rain gauges. Additionally, geophysical testing was performed to obtain a continuous profile of the subgrade soil over time. Topographical surveying and horizontal inclinometer readings were taken to determine pavement deformation. The field monitoring data resulted in a maximum movement up to 80 mm in the farm to market road, and almost 38 mm in the state highway. The field data were statistically evaluated to develop a deformation prediction model. The validation of the model indicated that only a fraction of the deformation was reflected by seasonal variation, while inclusion of rainfall events in the equation significantly improved the model. Furthermore, the prediction model also incorporated the effects of change in temperature and resistivity values. The generated model could find its application in predicting pavement deformation with respect to rainfall at any time of the year.

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.

The negative pore water pressure or soil suction has significant effect on the performance of geotechnical infrastructures (e.g., slope, pavement, embankment etc.). The unsaturated behavior of soil is not static, rather offers variation in response to climatic loading. The objective of the study was to evaluate field-based techniques of SWCC construction in terms of capturing these variation as compared to laboratory methods and predictive models. The field assessment could allow the quantification of hysteresis effect on the SWCC. Instrumentation data from one Texas, USA highway was used in this study. Soil Water Characteristic Curves (SWCCs) were regenerated utilizing co-located moisture and suction data from the field. Laboratory and field measured SWCCs from the instrumented site were fitted by van Genuchten model. Previously developed predicted models were also utilized to evaluate the SWCC parameters. Based on the evapotranspiration and rainfall amounts, distinct drying and wetting cycles were recorded. Though hourly data was collected in this study, average daily values were used for the analysis. Unsaturated flow parameters (α, n, m) were determined from both laboratory testing and field moisture-suction data along with the predictive models. Clear differences were observed between the values obtained from predictive models and field generated SWCC. The outcome from this study revealed that field reconstructed SWCCs can be used to simulate higher precision in numerical modeling in numerous geotechnical applications.