Published Paper Details:

The main aim of this research is to examine how neural networks can describe soil behavior under situations of uniaxial tension. Geotechnical engineering issues have been effectively modelled using artificial neural networks (ANNs) over the past several years. Artificial neural networks (ANNs) are a kind of artificial intelligence (AI) that aim to replicate the brain and nervous system of humans [1]. Most geotechnical engineering issues may be effectively modelled with ANNs. The goal of this work was to use ANNs to figure out how much dirt is buried under the surface. Depending on the size of the research area, it may be necessary to conduct a number of experiments and drill a number of boreholes in order to evaluate the soil layer structure [1]. The near-surface geology may be better understood by learning more about the qualities of the soil layers between boreholes. A neural network (ANN) learns from instances of data in order to grasp the nuances of functional data correlations even when the underpinnings of such interactions are obscure or difficult to understand on the physical level.