Prediction of Bearing Capacity of Stone Columns Placed in Soft Clay Using SVR Model

Abstract

It is well known that the construction on soft clayey soil is always a great challenge to the geotechnical engineers. The soft clay poses high compressibility and low bearing capacity. It is a common practice to improve the properties of the soft clay prior to any construction on it. In this respect, ground improvement by stone columns is a usual choice of the geotechnical engineers. The stone columns increase the bearing capacity and reduce the settlement of the soft clay. Many theories are developed to determine the bearing capacity of the soft soil reinforced with stone columns. However, most of the theories are site-specific and do not show a very good match with the field observations. In this study, a large numbers of data were collected from previously reported studies from various parts of the globe and an empirical formula based on support vector regression (SVR) technique for the determination of the ultimate bearing capacity of the stone columns is achieved. Two different techniques, namely tenfold cross-validation ( $${q}_{\mathrm{TFCV}}$$ ) and non-cross-validation ( $${q}_{\mathrm{NCV}}$$ ), are presented for the construction of the SVR model. It is observed that the SVR method gives a better prediction than artificial neural network method. Laboratory experiments were conducted to validate the SVR-ERBF empirical approach. The formula is also validated with two field observations by two other investigators.