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Author 1
Amir Khan
College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518061, China
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Open Access | Research Article | 31 May 2025
Machine Learning Prediction of the Improvement of Black Cotton Soil by Partial Displacement with Quarry Dust and Fly Ash for Sustainable Road Construction
Sustainable Intelligent Infrastructure | Volume 1, Issue 2: 52-66, 2025 | DOI: 10.62762/SII.2025.901022
Abstract
In this research paper, advanced artificial intelligence (AI) techniques have been applied in predicting the mechanical properties of black cotton soil (BCS) treated by the method of partial displacement of the soil. The materials of the displacement operation were fly ash (FA) and quarry dust (QD), which are both solid wastes derived from coal combustion in power plants and quarrying of stones for the production of aggregates. Previous activities show that BCS has never been treated by displacement of the soil sample but by the addition of these cementitious materials as wt % of the dry soil. The advanced AI techniques were the ANN, GP and the EPR, which executed forty data entries collecte... More >

Graphical Abstract
Machine Learning Prediction of the Improvement of Black Cotton Soil by Partial Displacement with Quarry Dust and Fly Ash for Sustainable Road Construction