Experimental and computational response of strip footing resting on prestressed geotextile-reinforced industrial waste

Geotechnical engineering practices involves the use of geosynthetic as one of the major construction materials for stabilizing terrains and these materials have been also proven to be technically efficient. In view of the above, a series of model load tests with variation in the depth of geotextile and prestressing force were carried out to study the strength and deformation characteristics of ferrochrome slag reinforced with single prestressed geotextile layer. The present study provides a sustainable replacements solution for industrial waste such as ferrochrome slag. It is also found that pretensioning of reinforcements is effective in comparison with simple reinforcement. The load settlement curves demonstrate that reinforcements and prestressing significantly reduce the settlement of a strip footing resting on geotextile-reinforced ferrochrome slag. Also, a pretensioning force of 9 kN/m is found to have the least settlement. Further, this study proposes the use of artificial neural network and extreme learning machine (ELM) to predict settlement using basic input parameters. Application of computational models provides an innovative solution for predicting the settlement of footing with ease and in a cost-efficient manner. The computational model concludes that the developed ELM models are efficient and effective in predicting the settlement of a footing and can be used as a robust tool for preliminary assessments.