Earth materials have been used in construction as safe, healthy and environmentally sustainable. It is often challenging to develop an optimum soil mix because of the significant variations in soil properties from one soil to another. The current study analyzed the soil properties, including the grain size distribution, Atterberg limits, compaction characteristics, etc., using multilinear regression (MLR) and artificial neural networks (ANN). Data collected from previous studies (i.e., 488 cases) for stabilized (with either cement or lime) and unstabilized soils were considered and analyzed. Missing data were estimated by correlations reported in previous studies. Then, different ANNs were designed (trained and validated) using Levenberg-Marquardt (L-M) algorithms. Using the MLR, several models were developed to estimate the compressive strength of both unstabilized and stabilized soils with a Pearson Coefficient of Correlation (R²) equal to 0.2227 and 0.766, respectively. On the other hand, developed ANNs gave a higher value for R² than MLR (with the highest value achieved at 0.9883). Thereafter, an experimental program was carried out to validate the results achieved in this study. Finally, a sensitivity analysis was carried out using the resulting networks to assess the effect of different soil properties on the unconfined compressive strength (UCS). Moreover, suitable recommendations for earth materials mixes were presented.

In this research, an attempt was made to explore the possibility of using a stabilization and solidification ($\mathrm{S}/\mathrm{S}$ ) technique for the treatment of sandy soil contaminated with diesel and crude oil using different stabilizers. For this purpose, soil samples were collected and contaminated by diesel and crude oil at three different percentages (2.5%, 5%, and 10% by weight of dry soil). Effects of oil contamination of soil were evaluated, and then $\mathrm{S}/\mathrm{S}$ treatment was carried out using ordinary portland cement (OPC), cement kiln dust (CKD), and limestone powder (LSP). The performance of $\mathrm{S}/\mathrm{S}$ -treated soils was evaluated by measuring the changes in compaction characteristics, unconfined compressive strength (UCS), permeability, and heavy metal and hydrocarbon contents. Toxicity characteristic leaching procedure (TCLP) and total petroleum hydrocarbons (TPH) tests on $\mathrm{S}/\mathrm{S}$ -treated soil were conducted to observe the effect of the $\mathrm{S}/\mathrm{S}$ treatment on the reduction of the soil pollution potential. Scanning electron microscope (SEM), energy dispersive X-ray spectroscopy (EDS), and X-ray diffraction (XRD) analyses were carried out to examine the changes in the microstructure and mineralogy of the soil after its $\mathrm{S}/\mathrm{S}$ treatment. Results of this investigation indicate that the $\mathrm{S}/\mathrm{S}$ treatment of the oil-contaminated soil improved the compaction characteristics, UCS, and resistance against permeability and leachability.

This study investigates the use of machine learning techniques to predict the unconfined compressive strength (UCS) of both stabilized and unstabilized soils. This research focuses on analyzing key soil parameters that significantly impact the strength of earth materials, such as grain size distribution and Atterberg limits. Machine learning models, specifically Support Vector Regression (SVR) and Decision Trees (DT), were employed to predict UCS. Model performance was evaluated using key metrics, including the Pearson coefficient of correlation (r²), coefficient of determination (R²), mean absolute error, and root mean square error. The findings reveal that, for unstabilized soils, both SVR and DT models exhibit remarkable performance with r² values of 0.9948 and 0.9947, respectively, with the DT model surpassing the SVR model in estimating UCS. Validation was conducted using data from four types of locally available soils in the Najd region of Saudi Arabia, although some disparities were noted between actual and predicted results due to limitations in the training data. The analysis indicates that, for unstabilized soil, grain size distribution and moisture content during testing are primary influencers of strength, whereas, for stabilized soil, factors such as stabilizer type and content, as well as density and moisture during testing, are pivotal. This research demonstrates the potential of machine learning for developing a robust classification system to enhance earth material utilization.