Natural Selection of Asphalt Mix Stiffness Predictive Models with Genetic Programming

dc.contributor.author Siddhartha, Khaitan
dc.contributor.author Gopalakrishnan, Kasthurirangan
dc.contributor.author Ceylan, Halil
dc.contributor.author Kim, Sunghwan
dc.contributor.author Ceylan, Halil
dc.contributor.author Khaitan, Siddhartha
dc.contributor.department Civil, Construction and Environmental Engineering
dc.date 2018-02-15T20:39:39.000
dc.date.accessioned 2020-06-30T01:11:13Z
dc.date.available 2020-06-30T01:11:13Z
dc.date.copyright Fri Jan 01 00:00:00 UTC 2010
dc.date.embargo 2015-02-23
dc.date.issued 2010-01-01
dc.description.abstract <p>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.</p>
dc.description.comments <p>This is a manuscript of an article in <em>ANNIE 2010, artificial Neural Networks in Engineering</em>, St. Louis, Missouri, November 1-3, 2010. Posted with permission.</p>
dc.identifier archive/lib.dr.iastate.edu/ccee_conf/17/
dc.identifier.articleid 1024
dc.identifier.contextkey 6711095
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath ccee_conf/17
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/13637
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/ccee_conf/17/2010_CeylanH_NaturalSelectionAsphalt_manuscript.pdf|||Fri Jan 14 21:10:17 UTC 2022
dc.subject.disciplines Civil and Environmental Engineering
dc.subject.disciplines Construction Engineering and Management
dc.subject.disciplines Electrical and Computer Engineering
dc.subject.keywords genetic programming
dc.subject.keywords genetic algorithms
dc.subject.keywords Hot-Mix asphalt
dc.subject.keywords Mechanistic Empirical Pavement Design Guide
dc.title Natural Selection of Asphalt Mix Stiffness Predictive Models with Genetic Programming
dc.type article
dc.type.genre conference
dspace.entity.type Publication
relation.isAuthorOfPublication 517da13c-65a2-4f2c-9a04-44601859a48d
relation.isAuthorOfPublication 3cb73d77-de43-4880-939a-063f9cc6bdff
relation.isOrgUnitOfPublication 933e9c94-323c-4da9-9e8e-861692825f91
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