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Backcalculation of pavement layer parameters using Artificial Neural Networks

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dc.creator Terzi, Serdal
dc.creator Saltan, Mehmet
dc.date 2004-01-31T22:00:00Z
dc.date.accessioned 2020-10-06T11:26:22Z
dc.date.available 2020-10-06T11:26:22Z
dc.identifier dfdc72cc-35fb-4dc4-82a9-c2a4b80bd3e7
dc.identifier https://avesis.sdu.edu.tr/publication/details/dfdc72cc-35fb-4dc4-82a9-c2a4b80bd3e7/oai
dc.identifier.uri http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/74150
dc.description In this paper, a new formulation based on Artificial Neural Networks (ANN) is presented for backcalculation of pavement layer moduli. In structural analysis of flexible pavements, the procedures as Layered Elastic Theory, Equivalent Layer Thickness (ELT), and Finite Elements Method (FEM) generally have complex formulations and give approximate results. Therefore, it is extremely difficult to perform realistic analysis for flexible pavements, especially in view of modelling the material properties of layers in these methods. Setting the finite element mesh and iteration procedure of backcalculation takes rather long time. The proposed ANN procedure requires significantly less computation time. ELT method is used for simplicity. It is impossible or very hard to model the visco-clastic and non-linear behaviour of layer materials in layered elastic theory. The use of ANN is proliferating with high rate in simulation. The ability of ANN is to learn complex nonlinear relationships. A new formulation using ANN is presented here.
dc.language eng
dc.rights info:eu-repo/semantics/closedAccess
dc.title Backcalculation of pavement layer parameters using Artificial Neural Networks
dc.type info:eu-repo/semantics/article

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