Role of learning algorithm in neural network-based backcalculation of flexible pavements

A. Burak Goktepe*, Emine Agar, A. Hilmi Lav

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

Abstract

Nondestructive testing (NDT) methods are widely used for the performance evaluation of flexible pavements. Falling weight deflectometer (FWD), which measures time-domain deflections resulting from, applied impulse loads, is the most popular technique among all NDT methods. The evaluation of the FWD data requires the inversion of mechanical pavement properties using a backcalculation tool that includes both a forward pavement response model and an optimization algorithm. Neural networks (NNs) have also emerged as alternative tools that can be employed for pavement backcalculation problems relative to their real-time processing abilities. However, there have been no comprehensive analyses in previous studies that focus on the learning algorithm and the architecture of a NN model, which considerably affect backcalculation results. In this study, 284 different NN models were developed using synthetic training and testing databases obtained by layered elastic theory. Results indicated that both the learning algorithm and network architecture play important roles in the performance of the NN based backcalculation process.

Original languageEnglish
Pages (from-to)370-373
Number of pages4
JournalJournal of Computing in Civil Engineering
Volume20
Issue number5
DOIs
Publication statusPublished - 2006

Keywords

  • Algorithms
  • Flexible pavements
  • Neural networks
  • Nondestructive tests

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