Prediction of Geotechnical Parameters Using Machine Learning Techniques

In the present study, relationships between in-place density using SPT N-value, compression index (Cc) using liquid limit (LL) and void ratio (e), and cohesion (c) and angle of internal friction (ϕ) using SPT N-value have been established using machine learning techniques. Geotechnical data up to a depth of 50 m from 1053 borehole locations covering almost every district in the state of Haryana have been considered to develop models and statistical correlations. A general trend has been recorded in the observed data and accordingly, the outliers have been excluded. Several models have been developed to establish functional correlations. These correlations have been ranked on the basis their coefficient of determination (R2) value and mean absolute error (MAE). Subsequently, the model with the highest R2 value and minimum mean absolute error has been considered for the development of correlations. Analysis has also been carried out for all the developed models to assess their individual performance. For this purpose, all the developed models have been evaluated by fitting a straight line between observed and modelled values, and in all the cases, a good value of R2 has been observed. The R2 values obtained for all the models range from 0.798 to 0.988. On comparison, it has been observed that the values of geotechnical parameters obtained are in close agreement with the existing work.