Parametric Investigation of Corner Effect on Soil Nailed Walls and Prediction Using Machine Learning Methods

Abstract

The performance of soil nailed walls is evaluated based on lateral displacements, especially in high walls. In this study, the displacement behavior of nailed walls, which are frequently preferred in retaining wall systems in hard clayey soils, was examined by taking into account the corner effect. The nailed wall model was created using Plaxis 2D v.23, and the performance of the model was verified with the results of inclinometer measurements taken on-site. To assess the influence of excavation pit dimensions on the corner effect, 25 three-dimensional and 25 plane–strain slice models were created using Plaxis 3D v.23, and the effect of excavation pit dimensions on the plane–strain ratio (PSR) was determined. Then, analysis studies were carried out by creating 336 3D and 336 plane–strain slice models with variable parameters, such as slope angle (β), wall angle (α), nail length (L/H), excavation depth (H), and distance from the corner (xH). Its effects on PSR were determined. The interactions of the parameters with each other and PSR estimation were evaluated using machine learning (ML) methods: artificial neural networks (ANN), classifical and regression tree (CART), support vector regression (SVR), extreme gradient boosting (XGBoost). The proposed ML prediction methods and PSR results were compared with performance metrics and reliable results were obtained.