Language selection

Government of Canada / Gouvernement du Canada

Search

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

From National Research Council Canada

Download
  1. (PDF, 242 KiB)
AuthorSearch for: 1; Search for: 1
Affiliation
  1. National Research Council of Canada. NRC Institute for Research in Construction
FormatText, Article
Conference18th Engineering Mechanics Division Conference of ASCE: 3 June 2007, Blacksburg, Virginia
SubjectRoads/Pavement
Abstract
This 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
In
  • 18th Engineering Mechanics Division Conference of ASCE: [Proceedings] (3 June 2007): 1–6.
LanguageEnglish
Peer reviewedYes
NRC numberNRCC 49474
NRC-IRC-18554
NPARC number20377160
Export citationExport as RIS
Report a correctionReport a correction (opens in a new tab)
Record identifier3a3043ea-4077-4703-9e12-9020f5093938
Record created2012-07-24
Record modified2020-05-10
Date modified: