Shaik Subhan Alisha

In recent years, in developing nations, importance of village roads with sustainable and low-cost are gaining importance. In this study an attempt has been made to understand the strength and stiffness of mechanically altered soils. The locally available Lateritic soil and black cotton soil can be used in the construction field to improve the properties of soils for building foundations, roadways, and other engineering structures. The main goal of this study was to investigate the effects of mixing different percentages of red soil and black cotton soils on the physical and mechanical properties of the blended soil. Experimental investigation was carried to determine various properties such as shear strength, compressibility, water content, and differential swell index, of blended soils. The results of these tests were then compared to the properties of the individual soils to determine the effects of mechanically alteration on the overall properties of the soil. This experiment is important in the construction field as it allows engineers and contractors to determine the optimal mix of soils for specific construction projects. This experiment can be used to improve the overall soil quality in construction sites, which can lead to cost savings and improved durability of structures built on the site. Finally, the results thus obtained are trained to an Artificial Neural Network (ANN) and the relation between the engineering properties, UCS, and CBR are obtained. The prediction models show higher performance with correlation coefficients of 0.986 and 0.980, respectively. Overall, the blending of red soil and black cotton soil is an important experiment in the construction field that provides valuable information on the properties of blended soils and how they can be used in construction projects to improve the overall quality, durability, and stability of engineering structures.

The current study aims to estimate the strength performance of recycled aggregate concrete (RAC) applied in structural concrete by partially substituting cement with fly ash and ground granulated blast furnace slag (GGBS). The purpose of this project is to get a deeper understanding of the characteristics of RAC using GGBS and fly ash as additional cement materials. Concrete specimens containing various ratios of recycled coarse aggregate, fly ash, and GGBS are casted as part of our experiment. Analysis and comparison of the outcomes with the reference concrete mixture are performed. The results of this study will help advance sustainable concrete technology and serve as a foundation for designing RAC for structural applications that incorporates recycled coarse aggregate, fly ash, and GGBS. In our study we conducted tests like Compressive strength of concrete, split tensile strength of the concrete, Flexural strength of the concrete, recycled aggregate used partially in our study is 20%, 40%,60% and GGBS powder used partially in cement with percentages of 10,15,20 and parlay with fly ash content partially used in cement with percentages of 10,15,20. Our observation from this project is we good acceptable values combinedly at the percentages of 40% Recycled aggregate and 15 percentages of both GGBS and FA .We can have further research work by implementing the same research with wide verity range of cement materials.

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.