Red mud nano-fines potential for improving the geotechnical properties of ameliorated reconstituted black cotton soil

Abstract

Nearly 51.8 million hectares of land area in India is covered with expansive soils (mainly black cotton soil (BCS)). The property of the expansive soils, in general, is that they are very hard when in dry state, but they lose all of their strength when in wet state. In light of this property, these soils pose problems worldwide and a serious challenge to overcome for the Geotechnical Engineers. One of the most important aspects for construction purposes is soil stabilization, because stabilization regime improves engineering properties of the soil like volume stability, strength and durability. Red mud is a byproduct in the process of extraction of alumina from bauxite, which has over 82% of its mass in the nano scale. The process is called Bayer’s process. It is an insoluble product and is generated after digestion with sodium hydroxide at elevated temperature and pressure. In this research work, characteristic properties of nanostructured red mud (NRM) were determined and its possible use as a supplementary cement in the expansive soil stabilization was studied. The red mud average particle size was reduced to nano-red mud (NRM) by ball-milling and passing the material through 150 nm sieve to increase its reactive surface. Basic properties like specific gravity, particle size distribution, Atterberg limits, OMC and MDD are determined in conformity with the Indian Standard Codes, British Standard and American Standards and test results are discussed in geotechnical point of view. Furthermore, intelligent models were predicted using the genetic programming, artificial neural network and evolutionary polynomial regression techniques. Generally, the results showed that NRM has the potential to improve the hydro-mechanical properties of the ameliorated BCS especially CBR and UCS to be used a pavement compacted subgrade material and in landfill liner construction with strength improvements exceeding those given by design standards. The intelligent models performed optimally with ANN with R² of 88% outperforming GP and EPR in the prediction of UCS and GP with R² of 98% outperforming ANN and EPR in the CBR prediction. But GP generated a closed form equation that allows its flexibility to be applied manually as well as intelligently and with a minimal speed of 3 h instead 12 h average speed of machine learning techniques.