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Investigation of dynamic behavior of back to back reinforced soil retaining walls with finite element and artificial neural network

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dc.contributor Ph.D. Program in Civil Engineering.
dc.contributor.advisor Güler, Erol.
dc.contributor.author Öztürk, Tahir Erdem.
dc.date.accessioned 2023-03-16T10:56:39Z
dc.date.available 2023-03-16T10:56:39Z
dc.date.issued 2012.
dc.identifier.other CE 2012 O88 PhD
dc.identifier.uri http://digitalarchive.boun.edu.tr/handle/123456789/14227
dc.description.abstract Back-to-back Mechanically Stabilized Earth (MSE) wall are commonly used for bridge approach embankments. By the means of not only aesthetically pleasing appearance but also satisfactory performance under seismic loading reinforced soil retaining structures are becoming widely used in Turkey. In this study, a parametric study of seismic response analysis of reinforced soil retaining structures was performed using a finite element analysis with commercial Finite Element software, Plaxis. The aim of the study is to determine the influence of reinforcement length, reinforcement spacing, wall height and facing type on seismicinduced permanent displacements. Permanent displacements under earthquake loading conditions associated with different L/H ratios and reinforcement spacing for 5 m to 9 m height walls are investigated. In order to investigate dynamic behavior of the walls harmonic motions with 5 seconds duration have been applied. The motion had three different levels of Peak Ground Accelerations, namely 0.2 g, 0.4 g and 0.6 g. Artificial Neural Network (ANN) conducted in this study was applied for the first time in literature to estimate the deformations of retaining walls under dynamic loads. Although developing an analytical model is feasible in some simplified situations, most manufacturing processes are complex, ANN has been applied successfully in many nonlinear geotechnical engineering problems in order to make reliable predictions and to check whether the range of classical design results are within reasonable outcomes.
dc.format.extent 30 cm.
dc.publisher Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2012.
dc.relation Includes appendices.
dc.relation Includes appendices.
dc.subject.lcsh Retaining walls.
dc.subject.lcsh Reinforced soils.
dc.title Investigation of dynamic behavior of back to back reinforced soil retaining walls with finite element and artificial neural network
dc.format.pages xxii, 136 leaves ;


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