UTILIZATION OF ALUMINA AND STEEL INDUSTRIAL WASTE FOR THE DEVELOPMENT OF CONTROLLED LOW STRENGTH MATERIALS

The study aimed at incorporating Red Mud, a byproduct of bauxite ore extraction, and Ground Granulated Blast Furnace Slag (GGBS), a byproduct of the steel industry, into Controlled Low Strength Materials (CLSM). These materials were combined with M Sand and cement to develop a sustainable and cost-effective construction material. Utilizing Red Mud and GGBS addresses waste disposal challenges while enhancing CLSM properties, making it a more environmentally friendly option. By converting industrial byproducts into valuable construction components, the research contributes to sustainable development and the circular economy. The research methodology involves creating five different dry mix combinations, varying the proportions of Red Mud, GGBS, M Sand, and cement. Each combination was tested with four different water contents to evaluate their engineering and dynamic properties. This methodical approach was focused to identify the optimal mix that balances strength and workability. By analyzing these properties, the study provided a valuable insight into the behavior of CLSM under various conditions, contributing to the advancement of more efficient construction materials. The systematic testing ensured that the developed CLSM mixes met the necessary performance criteria for practical applications. The primary objective of the study was to develop a phenomenological model based on the obtained strength and flow values. This model will serve as a predictive tool for future research, aiding in the design of CLSM mixtures with desired characteristics. By demonstrating the feasibility of using Red Mud in CLSM, the research underscores its potential to offer cost-effective and sustainable solutions for various engineering applications. This innovative approach not only promotes waste utilization but also opens new possibilities for improving construction practices. The developed model will help engineers and researchers predict the performance of new CLSM formulations, streamlining the development process for future projects.