- author = "Mehmet Saltan and Serdal Terzi",
- title = "Comparative analysis of using artificial neural networks (ANN) and gene expression programming (GEP) in backcalculation of pavement layer thickness",
- journal = "Indian Journal of Engineering and Materials Sciences",
- year = "2005",
- volume = "12",
- number = "1",
- pages = "42--50",
- month = feb,
- keywords = "genetic algorithms, genetic programming, gene expression programming",
- ISSN = "0971-4588",
-
URL = "
http://www.niscair.res.in/ScienceCommunication/ResearchJournals/rejour/ijems/ijems2k5/ijems_feb05.asp#a42",
-
URL = "
http://tef.sdu.edu.tr/~sterzi/GEP&ANN.pdf",
- abstract = "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 surface condition in order to establish a reasonable pavement rehabilitation design system. Pavement layers are characterised by their elastic moduli estimated from surface deflections through back calculation. Backcalculating the pavement layer moduli is a well-accepted procedure for the evaluation of the structural capacity of pavements. The ultimate aim of the back calculation process from non-destructive 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 back calculated from the measured field data through appropriate analysis techniques. In this study, artificial neural networks (ANN) and gene expression programming (GEP) are used in back calculating the pavement layer thickness from deflections measured on the surface of the flexible pavements. Experimental deflection data groups from NDT are used to show the capability of the ANN and GEP approaches in back calculating the pavement layer thickness and compared each other. These approaches can be easily and realistically performed to solve the optimisation problems which do not have a formulation or function about the solution.",
-
notes = "[IPC Code: Int. Cl.7 E01C 9/10]
CODEN : IEMSEW
See also 'Backcalculation of Pavement Layer Thickness and Moduli Using Adaptive Neuro-fuzzy Inference System, by Mehmet Saltan, Serdal Terzi, Intelligent and Soft Computing in Infrastructure Systems Engineering Volume 259 of the series Studies in Computational Intelligence pp 177-204 DOI:10.1007/978-3-642-04586-8_6",
