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.
Knowledge discovery and data mining in pavement inverse analysis
2013
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Expanding the Use of Pavement Condition Data Through Knowledge Discovery in Databases
British Library Conference Proceedings | 2002
|Knowledge Discovery for Flyback-Booster Aerodynamic Wing Design Using Data Mining
Online Contents | 2008
|Multimedia Data Mining and Knowledge Discovery Valery A. Petrushin and Latifur Khan
British Library Online Contents | 2008
|