Artificial Neural Networks Approach for Earthquake Deformation Determination of Geosynthetic Reinforced Retaining Walls

Tahir Erdem Ozturk

Abstract

Back-to-back Mechanically Stabilized Earth (MSE) walls are commonly used for bridge approach embankments.  Artificial Neural Network (ANN) analysis conducted in this study was applied for the first time in literature to estimate the seismic-induced permanent displacements of retaining walls under dynamic loads. For this purpose, a parametric study of seismic response analysis of reinforced soil retaining structures was performed to train the ANN using finite element analysis. The variables used to define wall geometry were reinforcement length, reinforcement spacing, wall height and facing type. The harmonic motion had three different levels of peak ground accelerations, namely 0.2g, 0.4g and 0.6g and had a duration of 6 sec with a frequency of 3 Hz.  Although developing an analytical or empirical model is feasible in some simplified situations, most data manufacturing processes are complex and, therefore, models that are less general, more practical and less expensive than the analytical models are of interest. The agreement of the neural network predicted displacements and deformation classification with Finite Element Analyses results were encouraging by the means of correlation since the coefficient values of R=0.99 for ANN regression analysis were achieved.

Keywords

Finite Element Analysis; Artificial Neural Network; reinforced Soil wall

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