• DEBABRATA DEBNATH
• SANDEEP KUMAR CHOUKSEY

The unconfined compressive strength (UCS) of soft soils is a critical parameter in determining their stability and suitability for construction purposes. In recent years, copper slag-based geopolymers have emerged as a sustainable and effective soil stabilization method, improving the mechanical properties of soft soils. However, the prediction of UCS for such stabilized soils remains complex due to the variability in soil properties, geopolymer mix ratios, and environmental conditions. This research explores the application of Artificial Intelligence (AI) techniques, specifically machine learning models, for predicting the UCS of soft soils stabilized with copper slag geopolymer. By leveraging a comprehensive dataset of experimental results, AI models, including Artificial Neural Networks (ANN), were developed and trained to predict UCS based on input variables such as copper slag content, activator concentration, curing time, and soil properties. The results demonstrate the efficiency and accuracy of AI in predicting UCS, providing a powerful tool for optimizing soil stabilization practices and reducing the need for extensive laboratory testing. This study highlights the potential of AI-driven approaches in geotechnical engineering, contributing to the advancement of sustainable soil stabilization technologies.

"Application of Artificial Intelligence for the prediction of unconfined compressive strength of soft soil stabilized with copper slag geopolymer", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 1, page no.h518-h522, January-2024, Available :http://www.jetir.org/papers/JETIR2401762.pdf

"Application of Artificial Intelligence for the prediction of unconfined compressive strength of soft soil stabilized with copper slag geopolymer", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 1, page no. pph518-h522, January-2024, Available at : http://www.jetir.org/papers/JETIR2401762.pdf