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Backcalculation of Pavement Layer Thickness and Moduli Using Adaptive Neuro-fuzzy Inference System

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Intelligent and Soft Computing in Infrastructure Systems Engineering

Part of the book series: Studies in Computational Intelligence ((SCI,volume 259))

Abstract

Efficient and economical methods are important in determination of the structural properties of the existing flexible pavements. An important pavement monitoring activity performed by most highway agencies is the collection and analysis of deflection data. Pavement deflection data are often used to evaluate a pavement’s structural condition non-destructively. It is essential not only to evaluate the structural integrity of an existing pavement but also to have accurate information on pavement structural condition in order to establish a reasonable pavement rehabilitation design system. Pavement structural adequacy is often evaluated by calculating elastic modulus of each layer using the so-called “backcalculation”. Backcalculating the pavement layer properties is a well-accepted procedure for the evaluation of the structural capacity of pavements. The ultimate aim of the backcalculation process from Nondestructive Testing (NDT) results is to estimate the pavement material properties. Using backcalculation analysis, flexible pavement layer thicknesses together with in-situ material properties can be backcalculated from the measured field data through appropriate analysis techniques. In this study, adaptive neural based fuzzy inference system (ANFIS) is used in backcalculating the pavement layer thickness and moduli from deflections measured on the surface of the flexible pavements. Experimental deflection data groups from NDT are used to show the capability of the ANFIS approaches in backcalculating the pavement layer thickness and moduli, and compared each other.

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Author information

Authors and Affiliations

  1. Engineering and Architectural Faculty, Civil Engineering Department, S.Demirel University, 32260, Isparta/Turkey

    Mehmet Saltan

  2. Technical Education Faculty, Structural Education Department, S. Demirel University, 32260, Isparta/Turkey

    Serdal Terzi

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  1. Mehmet Saltan
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  2. Serdal Terzi
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Editor information

Editors and Affiliations

  1. Dept. of Civil, Constr. & Env. Engg., Iowa State University, 354 Town Engineering Bldg., 50011-3232, Ames, IA, USA

    Kasthurirangan Gopalakrishnan

  2. Dept. of Civil, Constr. & Env. Engg., Iowa State University, 482B Town Engineering Bldg., 50011-3232, Ames, IA, USA

    Halil Ceylan

  3. Dept. of Civil and Environmental Engineering, University of Delaware, 343B DuPont Hall, DE 19716, Newark, USA

    Nii O. Attoh-Okine

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© 2009 Springer-Verlag Berlin Heidelberg

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Saltan, M., Terzi, S. (2009). Backcalculation of Pavement Layer Thickness and Moduli Using Adaptive Neuro-fuzzy Inference System. In: Gopalakrishnan, K., Ceylan, H., Attoh-Okine, N.O. (eds) Intelligent and Soft Computing in Infrastructure Systems Engineering. Studies in Computational Intelligence, vol 259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04586-8_6

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  • DOI: https://doi.org/10.1007/978-3-642-04586-8_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04585-1

  • Online ISBN: 978-3-642-04586-8

  • eBook Packages: EngineeringEngineering (R0)

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