Knowledge discovery and data mining in pavement inverse analysis

Kasthurirangan Gopalakrishnan*, Ankit Agrawal, Halil Ceylan, Sunghwan Kim, Alok Choudhary

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

This paper describes the use of data mining tools for predicting the non-linear layer moduli of asphalt road pavement structures based on the deflection profiles obtained from non-destructive deflection testing. The deflected shape of the pavement under vehicular loading is predominantly a function of the thickness of the pavement layers, the moduli of individual layers, and the magnitude of the load. The process of inverse analysis, more commonly referred to as backcalculation, is used to estimate the elastic (Young's) moduli of individual pavement layers based upon surface deflections. A comprehensive synthetic database of pavement response solutions was generated using an advanced non-linear pavement finite-element program. To overcome the limitations associated with conventional pavement moduli backcalculation, data mining tools such as support vector machines, neural networks, decision trees, and meta-algorithms like bagging were used to conduct asphalt pavement inverse analysis. The results successfully demonstrated the utility of such data mining tools for real-time non-destructive pavement analysis.

Original languageEnglish (US)
Pages (from-to)1-10
Number of pages10
JournalTransport
Volume28
Issue number1
DOIs
StatePublished - Mar 1 2013

Keywords

  • artificial neural network (ANN)
  • infrastructure
  • road
  • statistical analysis
  • transportation

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering

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