Neural Networks Analysis of Airfield Pavement Heavy Weight Deflectometer Data
Identifiers and Pagination:Year: 2008
First Page: 15
Last Page: 23
Publisher Id: TOCIEJ-2-15
Article History:Received Date: 1/1/2008
Revision Received Date: 11/3/2008
Acceptance Date: 17/3/2008
Electronic publication date: 4/4/2008
Collection year: 2008
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
The Heavy Weight Deflectometer (HWD) test is one of the most widely used tests for assessing the structural integrity of airport pavements in a non-destructive manner. The elastic moduli of the individual pavement layers predicted from the HWD deflection measurements through inverse engineering analysis are effective indicators of pavement layer condition. The primary objective of this study was to develop a tool for backcalculating non-linear pavement layer moduli from HWD data using Artificial Neural Networks (ANN) for rapid structural evaluation of airfield pavements. A multilayer, feed-forward backpropagation ANN which uses an error-backpropagation algorithm was trained to approximate the HWD backcalculation function. The synthetic database generated using an axisymmetric pavement finite-element program was used to train the ANN. Using the ANN, the Asphalt Concrete (AC) moduli and subgrade moduli were successfully predicted. Apart from the moduli, an attempt was made to predict the critical pavement structural responses using ANN models. The final product was used in backcalculating pavement layer moduli and predicting subgrade deviator stresses from actual field data acquired at the Federal Aviation Administration’s National Airport Pavement Test Facility (NAPTF).