ANNFAA: artificial neural network-based tool for the analysis of Federal Aviation Administration's rigid pavement systems

Basit Öğe Kaydı
dc.authoridKAYA, ORHAN/0000-0001-6072-3882
dc.authoridRezaei Tarahomi, adel/0000-0001-5405-896X
dc.authoridBrill, David/0000-0002-6186-7906
dc.contributor.authorTarahomi, Adel
dc.contributor.authorKaya, Orhan
dc.contributor.authorCeylan, Halil
dc.contributor.authorGopalakrishnan, Kasthurirangan
dc.contributor.authorKim, Sunghwan
dc.contributor.authorBrill, David R.
dc.date.accessioned2025-01-06T17:44:17Z
dc.date.available2025-01-06T17:44:17Z
dc.date.issued2022
dc.description.abstractThree-dimensional Finite Element (3D-FE) stress computations involved in the current rigid airport pavement design methodology, are time consuming when considering top-down cracking failure mode. In this study, Artificial Neural Network (ANN) models are integrated into a tool called ANNFAA to replace such 3D-FE computations. ANNFAA makes use of the best ANN models developed in MATLAB for 156 different airplanes without requiring any additional software installation or cumbersome learning of a new program. Within ANNFAA development, about 4,000 of 3D-FE simulations and many ANN models have been developed for each of these airplanes. Three useful tools were also developed using C# and MATLAB for implementing the 3D-FE analysis, post-processing the results, training the ANN models, and determining accuracy and performance of the ANN models. ANNFAA provides an accurate and rapid procedure for practitioners, engineers, and researchers for computing the critical stress responses associated with top-down cracking in multiple-slab rigid airfield pavements. This should make pavement design and analysis more practical, especially when a significantly large number of different cases that include top-down cracking failure mode are investigated. Also, this will help when currently used bottom-up cracking mode in the FAA standard rigid pavement design procedures is being considered in a design.
dc.description.sponsorshipFederal Aviation Administration (FAA)
dc.description.sponsorshipThe authors gratefully acknowledge the Federal Aviation Administration (FAA) for supporting this study under grant number15-G-01. The contents of this paper of this paper reflect the views of the authors, who are responsible for the facts and accuracy of the data presented within. The contents do not necessarily reflect the official views and policies of the FAA. The paper does not constitute a standard, specification, or regulation.
dc.identifier.doi10.1080/10298436.2020.1748627
dc.identifier.endpage413
dc.identifier.issn1029-8436
dc.identifier.issn1477-268X
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85083659277
dc.identifier.scopusqualityQ1
dc.identifier.startpage400
dc.identifier.urihttps://doi.org/10.1080/10298436.2020.1748627
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2970
dc.identifier.volume23
dc.identifier.wosWOS:000527184500001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherTaylor & Francis Ltd
dc.relation.ispartofInternational Journal of Pavement Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectRigid airfield pavements
dc.subjectcritical tensile stresses
dc.subjectartificial neural networks
dc.subjectTop-Down cracking
dc.titleANNFAA: artificial neural network-based tool for the analysis of Federal Aviation Administration's rigid pavement systems
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu