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This study investigated the impact of adding different proportions of ground granulated blast furnace slag (GGBS) on the engineering properties of expansive soils. GGBS proportions ranging from 0 to 40% by weight of soil were tested. A comprehensive series of experiments was conducted to determine the consistency limits, modified compaction characteristics, unconfined compression test of soil, and California bearing ratio (CBR) values under varied curing conditions of the stabilized soil mixtures. The results indicated that a GGBS content of 30% provided the optimum improvement, enhancing the strength and stability of the material. Additionally, a laboratory dynamic cone penetration test was performed inside a CBR mould to further examine the improved performance of the expansive soil samples with incorporated GGBS. The findings demonstrate that GGBS can significantly enhance the engineering behavior of expansive soils when added at 30% by weight. The research underscores the significant positive impact of GGBS on the engineering properties of expansive soils, offering an effective and environmentally friendly means of disposing of steel industry waste.

This research paper presents a comprehensive investigation into the suitability of pond ash material for flexible pavement subbases. Extensive laboratory tests, including compaction, and CBR tests were conducted to evaluate the strength characteristics of pond ash. Based on the results of the laboratory testing, the ideal conditions of pond ash compaction parameters are 33.36% moisture and 1.1 g/cc dry density. The saturated CBR value was determined to be 16.67%. Moreover, a 15-cm laboratory CBR mould was used to perform the dynamic cone penetration tests with hammers weighing 4.6 and 8 kg. Further dynamic cone penetration tests in a 60-cm cylindrical mould at 150 mm and 200 mm depths showed that the 8-kg hammer was adequate for large-scale modelling research. These studies determined the minimal subbase thickness and the depth at which soaked CBR values matched laboratory-soaked CBR values. This study uses laboratory and advanced DCPT correlations to better understand pond ash’s subbase performance and flexible pavement applications, and also by correlating laboratory and DCPT data, the engineers better assess the pond ash performance in road construction and design flexible pavements using pond ash subbases.

Nearly 51.8 million hectares of land area in India is covered with expansive soils (mainly black cotton soil (BCS)). The property of the expansive soils, in general, is that they are very hard when in dry state, but they lose all of their strength when in wet state. In light of this property, these soils pose problems worldwide and a serious challenge to overcome for the Geotechnical Engineers. One of the most important aspects for construction purposes is soil stabilization, because stabilization regime improves engineering properties of the soil like volume stability, strength and durability. Red mud is a byproduct in the process of extraction of alumina from bauxite, which has over 82% of its mass in the nano scale. The process is called Bayer’s process. It is an insoluble product and is generated after digestion with sodium hydroxide at elevated temperature and pressure. In this research work, characteristic properties of nanostructured red mud (NRM) were determined and its possible use as a supplementary cement in the expansive soil stabilization was studied. The red mud average particle size was reduced to nano-red mud (NRM) by ball-milling and passing the material through 150 nm sieve to increase its reactive surface. Basic properties like specific gravity, particle size distribution, Atterberg limits, OMC and MDD are determined in conformity with the Indian Standard Codes, British Standard and American Standards and test results are discussed in geotechnical point of view. Furthermore, intelligent models were predicted using the genetic programming, artificial neural network and evolutionary polynomial regression techniques. Generally, the results showed that NRM has the potential to improve the hydro-mechanical properties of the ameliorated BCS especially CBR and UCS to be used a pavement compacted subgrade material and in landfill liner construction with strength improvements exceeding those given by design standards. The intelligent models performed optimally with ANN with R2of 88% outperforming GP and EPR in the prediction of UCS and GP with R2of 98% outperforming ANN and EPR in the CBR prediction. But GP generated a closed form equation that allows its flexibility to be applied manually as well as intelligently and with a minimal speed of 3 h instead 12 h average speed of machine learning techniques.

Strength and stiffness characteristics are major concern for selecting any geomaterial. However, laboratory testing of these characteristics is time associated, laborious, and high cost. So, there is a need of intelligence tools to estimate the strength and stiffness of geomaterial. The impact of sawdust ash on the stiffness and strength properties of combined expanding clays is discussed in this research. The combined expansive clays underwent tests for California bearing ratio (CBR), unconfined compressive strength (UCS), optimal moisture content, maximum dry density, plasticity characteristics (liquid limit and plastic limit), and differential free swell (DFSI). According to test results, adding more sawdust to the blended clays improves their performance. This study also investigates the artificial neural network (ANN) model that considers six input variables to forecast the CBR and UCS of blended clays. The findings demonstrate that the ANN model performs more accurately for the CBR and UCS models. This clever method may help manage the under- or overestimation of additive dosage and reduce project costs.