Airfield pavement deterioration assessment using stress-dependent neural network models

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.

Keywords:

• Artificial neural networks
• Non-destructive test
• NAPTF
• Non-linear
• Airport flexible pavement systems

Acknowledgements

The authors gratefully acknowledge the FAA Airport Technology Research and Development Branch for providing the data referenced in this paper. Dr P. Watts is the FAA Program Manager for the Air Transportation Centers of Excellence and Dr S. Agrawal is the Manager of the FAA Airport Technology R&D Branch. The contents of this paper reflect the views of the authors, who are responsible for the facts and accuracy of the data presented within. The contents do not necessarily reflect the official views and policies of the Federal Aviation Administration. This paper does not constitute a standard, specification, or regulation.