Artificial Neural Network Model to Predict Anchored-Pile-Wall Displacements on Istanbul Greywackes

Abstract

Istanbul's main lithological unit is a greywacke formation locally known as the Trakya Formation. It is weathered and extensively fractured, and the stress relief induced by deep excavations causes excessive displacements in the horizontal direction. Therefore, predicting excavation-induced wall displacements is critical for avoiding damages. The aim of this study is to develop an Artificial Neural Network (ANN) model to predict anchored-pile-wall displacements at different stages of excavations performed on Istanbul's greywacke formations. A database was created on excavation and monitoring data from 11 individual projects in Istanbul. 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 point. 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 examine 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 actual measured displacements. The proposed ANN model accurately predicted the displacement of anchored pile walls constructed on Istanbul's greywackes at different excavation stages. 

Keywords

• Artificial neural network
• Anchored pile walls
• Finite element method
• Wall displacement
• Istanbul greywackes

Artificial Neural Network Model to Predict Anchored-Pile-Wall Displacements on Istanbul Greywackes

Abstract

Istanbul's main lithological unit is a greywacke formation locally known as the Trakya Formation. It is weathered and extensively fractured, and the stress relief induced by deep excavations causes excessive displacements in the horizontal direction. Therefore, predicting excavation-induced wall displacements is critical for avoiding damages. The aim of this study is to develop an Artificial Neural Network (ANN) model to predict anchored-pile-wall displacements at different stages of excavations performed on Istanbul's greywacke formations. A database was created on excavation and monitoring data from 11 individual projects in Istanbul. 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 point. 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 examine 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 actual measured displacements. The proposed ANN model accurately predicted the displacement of anchored pile walls constructed on Istanbul's greywackes at different excavation stages. 

Keywords

• Artificial neural network
• Anchored pile walls
• Finite element method
• Wall displacement
• Istanbul greywackes

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