Hydraulic conductivities of sands with different gradation and grain shape were estimated experimentally at a relative density (Dr) of about 40 % and a 22 ± 2 °C of constant temperature. Narli Sand (NS) with 0.67 of sphericity (S) and 0.72 of roundness (R), and Crushed Stone Sand (CSS) with 0.55 of S and 0.15 of R values were artificially graded into sixteen different grain-size fractions (4.75–2, 2–1.18, 1.18–0.6, 0.6–0.425, 0.425–0.3, 0.3–0.075, 4.75–0.075, 2–0.075, 1.18–0.075, 0.6–0.075, 0.425–0.075, 4.75–0.6, 2–0.6, 4.75-0.425, 2–0.425, 1.18–0.425 mm). Hydraulic conductivities of the NS estimated by use of constant head test ranged from 1.61 to 0.01 cm/s, whilst those of the CSS estimated by the same test ranged from 2.45 to 0.012 cm/s. It was observed that the hydraulic conductivity values of the NS are lower than those of the CSS samples, which is likely to be the result of differences in shape, particularly in R values. The results clearly demonstrated that the hydraulic conductivity can be significantly influenced by grading characteristics (d10, d20, d30, d50, d60, cu, cc, n, Io). Furthermore, comparisons between results obtained in the present study and hydraulic conductivity estimated with other formulas available in the literature were made. The comparisons indicated that the best estimation of hydraulic conductivity changes based on the gradation and shape properties of the sands tested.

This paper presents an extensive series of laboratory works and a prediction model on the design of a road pavement subgrade with Xanthan Gum (XG) biopolymer. The experimental works were carried out using mixtures of conventional aggregate for road pavement construction and XG at the ratios of 0%, 1%, 2%, and 5%, by dry weight. Unconfined compressive strength (UCS) and California bearing ratio (CBR) tests were conducted during the experimental works at the end of the various curing periods (4, 8, 16, and 32 days). An example of an improvement in the UCS values for a specimen with 5% XG addition tested at the end of 4-daycuring yields about a 200% increment by the end of a 32-daycuring. The CBR values of clean aggregates were found to be increased by about 300% by 5% XG addition for all curing periods applied. Furthermore, the energy absorption capacity of the aggregates was observed to be increased significantly by both XG inclusion and curing period. Moreover, scaled conjugate gradient (SCG) training algorithm-based models developed for the prediction of CBR and UCS test results displayed a very high estimation performance with the regression coefficients of R² = 0.967 and R² = 0.987, respectively. Evidently, XG biopolymer is provably of use as an alternative inclusion in road pavement subgrades constructed with conventional aggregates.

The study aims to investigate the effects of three different construction and demolition materials (CDMs), including crushed waste asphalt (CWA), crushed waste bricks (CWB), and crushed waste concrete (CWC), on some geotechnical properties of low plastic clayey soil, particularly, the undrained shear strength (Su) and the hydraulic conductivity (k). A set of experimental tests were performed on clayey soil and on clayey soil-CDM mixtures at mixing ratios of 5%, 10%, 15%, and 20% by dry weight. The results show that the soil plasticity decreases as the CDMs increase. Quantitatively, it is found a maximum of 12%, 6%, and 6% decrease in the liquid limits (LL) and a maximum of 9%, 4%, and 6% decrease in the plasticity limit (PI) of the mixtures with 20% of CWA, CWB, and CWC, respectively. The results of the Su estimated empirically from the fall cone tests show that the Su decreases as the CDMs increase. The Su reduces by approximately 10% and 2% of the mixtures with 20% CWA and CWB, respectively. But the Su is not affected by the CWC additive for water content lower than approximately 35%. The k value increases as the CDMs increase. The results show that the reported k value increases by 75%, 79%, and 247% of the mixtures with 20% of CWA, CWB, and CWC, respectively. Additionally, the k values obtained from the consolidation test confirm the findings of the effect of the CDMs on the coefficient of hydraulic conductivity.

This paper presents an extensive experimental investigation on the use of ground improvement techniques and biotechnological applications on earth science. In recent years, investigations in geomicrobiology have enabled significant advances in understanding the effect of microorganisms in the development of the soil behaviour. In this paper, a series of experimental study was conducted to investigate the applicability of biotechnologies in soil stabilization. Unconfined Compression and California Bearing Ratio (CBR) tests were employed during the experimental works for an assessment of two soil parameters of major importance - namely, compressive strength and bearing capacity. From the experimental results, it was observed that the unconfined compression strength and the CBR values of the sand increased with an increase of curing time.

This study aims to evaluate the effectiveness of sewage sludge ash (SSA) as a sustainable stabilizing agent for low-plasticity clay and to assess the applicability of machine learning techniques for predicting strength parameters. SSA was mixed with CL-type clay at contents of 0%, 5%, 10%, 15%, 20%, and 30% by dry weight. A comprehensive laboratory testing program was conducted, including Atterberg limits, compaction, fall cone, vane shear, and unconfined compressive strength (UCS) tests. The results showed that Atterberg limits increased with increasing SSA contents. As the SSA content increased up to 30%, the maximum dry unit weight decreased by approximately 6%, while the optimum water content increased by about 14%. The addition of SSA significantly enhanced the shear strength, with UCS-derived strength increments ranging from 264 to 771 kPa for 4-day-cured specimens and from 515 to 1351 kPa for 32-day-cured specimens. These findings demonstrate the strong potential of SSA as an alternative and sustainable soil stabilizer. In addition, Self-Organizing Maps (SOMs) were employed to analyze the distribution and relationships of parameters within the experimental dataset, followed by Support Vector Machines (SVMs) for classification and prediction. Using the experimental results, the undrained shear strength parameters obtained from fall cone (s_u-FCT) and laboratory vane shear (s_u-LVT) tests were predicted with accuracies of 83% and 79%, respectively. The novelty of this study lies in the integrated experimental–data-driven framework, in which extensive laboratory testing is combined with machine learning methods to both validate and reliably predict the strength behavior of SSA-stabilized clay.