Airfield pavement deterioration assessment using stress-dependent neural network models

Date
2009-01-01
Authors
Gopalakrishnan, Kasthurirangan
Ceylan, Halil
Guclu, Alper
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Abstract

In this study, an artificial neural network (ANN)-based approach was employed to backcalculate the asphalt concrete and non-linear stress-dependent subgrade moduli from non-destructive test (NDT) data acquired at the Federal Aviation Administration's National Airport Pavement Test Facility (NAPTF) during full-scale traffic testing. The ANN models were trained with results from an axisymmetric finite element pavement structural model. Using the ANN-predicted moduli based on the NDT test results, the relative severity effects of simulated Boeing 777 (B777) and Boeing 747 (B747) aircraft gear trafficking on the structural deterioration of NAPTF flexible pavement test sections were characterized. The results indicate the potential of using lower force amplitude NDT test data for routine airport pavement structural evaluation, as long as they generate sufficient deflections for reliable data acquisition. Therefore, NDT tests that employ force amplitudes at prototypical aircraft loading may not be necessary to evaluate airport pavements.

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This is an accepted manuscript of an article published by Taylor & Francis in Structure and Infrastructure Engineering on August 12, 2009, available online: http:/ /www.tandf.com/10.1080/15732470701311977.

Keywords
CNDE, airport flexible pavement systems, artificial neural networks, NAPTF, non-destructive test, non-linear
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