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

Year 2020, Volume: 31 Issue: 4, 10147 - 10166, 01.07.2020

Özgür Yıldız , Mehmet M. Berilgen

https://doi.org/10.18400/tekderg.492280

Cited By: 6

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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