Neural network modeling of resilient modulus and permanent deformation of aggregate materials

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Conference18th Engineering Mechanics Division Conference of ASCE: 03 June 2007, Blacksburg, Virginia
Pages16; # of pages: 6
AbstractThis paper assessed the suitability of artificial neural networks (ANN) as an analytical technique for developing pavement databases containing the resilient modulus and permanent deformation properties of aggregate materials. This study used a laboratory-generated database, consisting of 30 test result entries, to construct and analyse a number of ANN models. The database contained a wide range of factors known to influence the two mechanical parameters (resilient modulus, Mr, and percent permanent deformation, %PD) of the tested materials. The models were assessed for their ability to estimate the two parameters at different states of stress, moisture contents, and percent fines passing sieve # 200.
Publication date
AffiliationNRC Institute for Research in Construction; National Research Council Canada
Peer reviewedYes
NRC number49474
NPARC number20377160
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Record identifier3a3043ea-4077-4703-9e12-9020f5093938
Record created2012-07-24
Record modified2016-05-09
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