Artificial neural network model to predict anchored pile-wall displacements on Istanbul greywackes

Abstract

The greywackes are the common soil formation of Istanbul locally known as the Trakya Formation. It is mostly weathered and extensively fractured. The stress relief induced by deep excavations causes excessive displacements in horizontal direction. As a result, predicting excavation-induced wall displacements is critical for avoiding collapse. The aim of this study is to develop an Artificial Neural Network (ANN) model to predict anchored-pile-wall displacements at different stages of excavation performed on Istanbul's greywacke formations. A database was created on excavation and monitoring data from 11 individual projects. Five variables were used as input parameters, namely, excavation depth, maximum ground settlement measured behind the wall, system stiffness, standard penetration test N value of the soil depth, and index-of-observation. The proposed model was trained, validated, and tested. Finally, two distinct projects were numerically modeled by applying the finite element method (FEM) and then used to test the performance of the ANN model. The displacements predicted by the ANN model were compared with both the computed values obtained from the FEM analysis and in situ measured displacements. The proposed ANN model accurately predicted the displacement of anchored pile walls constructed in greywackes at different stages of excavation.