Prediction of excess pore water pressure using B-spline interpolation and artificial neural networks for soil improvement monitoring

Monitoring excess pore water pressure (EPWP) during and after soil improvement is essential under certain ground conditions. In some cases, ground monitoring may be required throughout the service life of a structure, particularly in areas where the subsoil is susceptible to environmental changes. However, installing sensors at every location across a construction site and its surrounding area is often impractical due to various site-specific constraints. This study proposes a spatial prediction framework that integrates B-spline interpolation and artificial neural networks (ANNs) to estimate EPWP at uninstrumented locations using simultaneous measurements from existing sensors. Experimental data reported in the published literature are extracted and processed in MATLAB to develop the ANN model and integrates results by B-spline interpolation. The network architecture is appropriately configured, and the available data are divided into training, validation, and testing subsets to evaluate predictive performance and reduce the risk of overfitting. The results indicate good agreement between the ANN predicted EPWP (kPa) at uninstrumented location and the experimental results reported in the literature. The proposed framework aims to apply these findings to EPWP monitoring and liquefaction assessment during and after soil improvement.