Isolated Effect and Sensitivity of Agricultural and Industrial Waste Ca-Based Stabilizer Materials (CSMs) in Evaluating Swell Shrink Nature of Palygorskite-Rich Clays

Abstract

This paper evaluates the suitability of sugarcane bagasse ash (SCBA) and waste marble dust (WMD) on the geotechnical properties of Palygorskite-rich expansive clays located in northwest Pakistan. These problematic soils exhibit undesirable characteristics which greatly affect the pavements, boundary walls, slab-on-grade members, and other civil engineering infrastructures. A series of geotechnical tests were performed on soil specimens using prescribed percentages of the aforementioned Ca-based stabilizer materials (CSMs). The investigation includes X-Ray Diffraction (XRD) Analysis, Scanning Electron Microscopy (SEM), X-Ray Fluorescence (XRF) tests, and physicomechanical properties such as moisture-density relationship, Atterberg’s limits, swell pressure, and an ANN-based sensitivity analyses of overall swell pressure development. The outcomes of these experimental investigations showed that the addition of CSMs into the expansive soils increased to 4% SCBA and 10% WMD, the plasticity index reduced by 30% and 49%, the volumetric swell decreased from approximately 49% to 86% and 63%, and the swelling pressure reduction was from 189 kPa to 120 kPa and 160 kPa (about 15% and 36%), respectively. It is interesting to note that replacement with specified CSM accelerated the strength of soil at extended curing periods and the optimum improvement in the strength behavior of the soil was also recorded. Moreover, with addition of the respective CSMs, the compactability and strength characteristics were ameliorated, while plasticity was significantly lowered. Given the amount of SCBA and WMD produced annually, their utilization for the stabilization of problematic soils, even in relatively low concentrations, could potentially have a substantial impact on the sustainable reuse of these waste materials.

Prediction Models for Evaluating Resilient Modulus of Stabilized Aggregate Bases in Wet and Dry Alternating Environments: ANN and GEP Approaches

“…The ANNs are AI-inspired biological neural networks and problem-solving machine learning models that mimic the cellular structures of the human brain and nervous system. They directly take into consideration the relationship between the model input variables and the corresponding outputs without giving simple mathematical expression, thus inhibiting their practical implications; however, their degree of accuracy is comparatively higher [48][49][50][51][52][53][54][55][56]. On the other hand, Cramer invented genetic programming (GP) in 1985, which was ameliorated with the help of various shapes and sizes.…”

Indirect Estimation of Swelling Pressure of Expansive Soil: GEP versus MEP Modelling

“…Figure1: Percentage of covered area of expansive soils across diferent countries where such soils are found in abundance[22].…”