Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Evolving data into mining solutions for insights
Communications of the ACM - Evolving data mining into solutions for insights
Knowledge discovery techniques for predicting country investment risk
Computers and Industrial Engineering
Book review: Three perspectives of data mining
Artificial Intelligence
Data mining issues and opportunities for building nursing knowledge
Journal of Biomedical Informatics - Special issue: Building nursing knowledge through infomatics: from concept representation to data mining
Data Mining with SQL Server 2005
Data Mining with SQL Server 2005
Expert Systems with Applications: An International Journal
Mining demand chain knowledge of life insurance market for new product development
Expert Systems with Applications: An International Journal
A method for improving the accuracy of data mining classification algorithms
Computers and Operations Research
A finite-element and Newton-Raphson method for inverse computing multilayer moduli
Finite Elements in Analysis and Design
Hi-index | 12.05 |
Pavement deflection data are often used to evaluate a pavement's structural condition non-destructively. Pavement layers are characterized by their elastic moduli estimated from surface deflections through backcalculation. Using backcalculation analysis, flexible pavement layer in situ material properties can be backcalculated from the measured field data through appropriate analysis techniques. This study focuses on the use of data mining (DM)-based pavement backcalculation tools for determining the in situ elastic moduli and Poisson's ratio of asphalt pavement from synthetically derived Falling Weight Deflectometer (FWD) deflections at seven equidistant points. In estimation of the elastic modulus and Poisson's ratio, data mining (DM) method has not been used as a backcalculation tool before. Experimental deflection data groups from NDT are used to show the capability of the DM approaches in backcalculating the pavement layer thickness and compared each other. By looking at the results of the study, Kstar method gives fine results with respect to other DM methods. Backcalculation of pavement layer elastic modulus and Poisson's ratio with DM has been carried out for the first time.