Document Type
Conference Proceeding
Conference
ANNIE 2010, Artificial Neural Networks in Engineering
Publication Date
2010
City
St. Louis, Missouri
Abstract
Genetic Programming (GP) is a systematic, domain-independent evolutionary computation technique that stochastically evolves populations of computer programs to perform a user-defined task. Similar to Genetic Algorithms (GA) which evolves a population of individuals to better ones, GP iteratively transforms a population of computer programs into a new generation of programs by applying biologically inspired operations such as crossover, mutation, etc. In this paper, a population of Hot-Mix Asphalt (HMA) dynamic modulus stiffness prediction models is genetically evolved to better ones by applying the principles of genetic programming. The HMA dynamic modulus (|E*|), one of the stiffness measures, is the primary HMA material property input in the new Mechanistic Empirical Pavement Design Guide (MEPDG) developed under National Cooperative Highway Research Program (NCHRP) 1-37A (2004) for the American State Highway and Transportation Officials (AASHTO). It is shown that the evolved HMA model through GP is reasonably compact and contains both linear terms and low-order non-linear transformations of input variables for simplification.
Copyright Owner
Gopalakrishnan et al
Copyright Date
2010
Language
en
Recommended Citation
Gopalakrishnan, Kasthurirangan; Kim, Sunghwan; Ceylan, Halil; and Khaitan, Siddhartha K., "Natural Selection of Asphalt Mix Stiffness Predictive Models with Genetic Programming" (2010). Civil, Construction and Environmental Engineering Conference Presentations and Proceedings. 17.
https://lib.dr.iastate.edu/ccee_conf/17
Included in
Construction Engineering and Management Commons, Electrical and Computer Engineering Commons
Comments
This is a manuscript of an article in ANNIE 2010, artificial Neural Networks in Engineering, St. Louis, Missouri, November 1-3, 2010. Posted with permission.