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Volume 13 | Issue 4 | Year 2026 | Article Id. IJCE-V13I4P106 | DOI : https://doi.org/10.14445/23488352/IJCE-V13I4P106

Industrial Waste Importance on Sustainable Ground Enrichment: A Comparative Analysis of Marble Dust and Synthetic Fibers in the Black Cotton Soil using Machine-Learning Support

Maluvu S, Felixkala T

Received Revised Accepted Published
07 Jan 2026 08 Feb 2026 07 Mar 2026 28 Apr 2026

Citation :

Maluvu S, Felixkala T, "Industrial Waste Importance on Sustainable Ground Enrichment: A Comparative Analysis of Marble Dust and Synthetic Fibers in the Black Cotton Soil using Machine-Learning Support," International Journal of Civil Engineering, vol. 13, no. 4, pp. 73-86, 2026. Crossref, https://doi.org/10.14445/23488352/IJCE-V13I4P106

Abstract

This paper explores the use of industrial waste as a sustainable development in enhancing black cotton soil that can be used in foundations. Waste marble dust and synthetic polyester fibers are used as stabilizing agents, and their performance is measured using the compaction test, the California bearing ratio test, the unconfined compressive strength test, and the model footing load-settlement test. Another machine-learning model is developed on the basis of Random Forest regression that helps predict bearing performance on the basis of laboratory parameters. The findings indicate that maximum dry density and bearing capacity increased significantly with marble dust inclusion, with optimum performance observed at 30% replacement. Synthetic fibers increase ductility, minimize cracking, and maximize post-peak load response, but strength increases are relatively less. The predictive model shows a high level of agreement with the trend of the experiments and indicates the variables that have the strongest effect on bearing behavior. The originality of this piece is in the fact that it combines the valorization of waste and comprehensible machine-learning assistance in a single comparative framework. The paper shows that the integration of materials, which are environment-friendly and data-driven tools, can form a viable avenue to sustainable and streamlined ground-improvement practices.

Keywords

Black cotton soil, Bearing capacity, Machine Learning, Marble dust, Synthetic fibers.

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