Airfield pavement deterioration assessment using stress-dependent neural network models

dc.contributor.author Gopalakrishnan, Kasthurirangan
dc.contributor.author Ceylan, Halil
dc.contributor.author Ceylan, Halil
dc.contributor.author Guclu, Alper
dc.contributor.department Civil, Construction and Environmental Engineering
dc.date 2018-02-15T17:42:27.000
dc.date.accessioned 2020-06-30T01:13:34Z
dc.date.available 2020-06-30T01:13:34Z
dc.date.copyright Thu Jan 01 00:00:00 UTC 2009
dc.date.embargo 2015-01-12
dc.date.issued 2009-01-01
dc.description.abstract <p>In this study, an artificial neural network (ANN)-based approach was employed to backcalculate the asphalt concrete and non-linear stress-dependent subgrade moduli from non-destructive test (NDT) data acquired at the Federal Aviation Administration's National Airport Pavement Test Facility (NAPTF) during full-scale traffic testing. The ANN models were trained with results from an axisymmetric finite element pavement structural model. Using the ANN-predicted moduli based on the NDT test results, the relative severity effects of simulated Boeing 777 (B777) and Boeing 747 (B747) aircraft gear trafficking on the structural deterioration of NAPTF flexible pavement test sections were characterized. The results indicate the potential of using lower force amplitude NDT test data for routine airport pavement structural evaluation, as long as they generate sufficient deflections for reliable data acquisition. Therefore, NDT tests that employ force amplitudes at prototypical aircraft loading may not be necessary to evaluate airport pavements.</p>
dc.description.comments <p>This is an accepted manuscript of an article published by Taylor & Francis in <em>Structure and Infrastructure Engineering</em> on August 12, 2009, available online: http:/ /www.tandf.com/10.1080/15732470701311977.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/ccee_pubs/50/
dc.identifier.articleid 1044
dc.identifier.contextkey 6520264
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath ccee_pubs/50
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/13959
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/ccee_pubs/50/2007_CeylanH_AirfieldPavementDeterioration_manuscript.pdf|||Sat Jan 15 00:40:53 UTC 2022
dc.source.uri 10.1080/15732470701311977
dc.subject.disciplines Civil and Environmental Engineering
dc.subject.disciplines Maintenance Technology
dc.subject.keywords CNDE
dc.subject.keywords airport flexible pavement systems
dc.subject.keywords artificial neural networks
dc.subject.keywords NAPTF
dc.subject.keywords non-destructive test
dc.subject.keywords non-linear
dc.title Airfield pavement deterioration assessment using stress-dependent neural network models
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication 3cb73d77-de43-4880-939a-063f9cc6bdff
relation.isOrgUnitOfPublication 933e9c94-323c-4da9-9e8e-861692825f91
File
Original bundle
Now showing 1 - 1 of 1
Name:
2007_CeylanH_AirfieldPavementDeterioration_manuscript.pdf
Size:
205.54 KB
Format:
Adobe Portable Document Format
Description:
Collections