A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
ANNFAA: artificial neural network-based tool for the analysis of Federal Aviation Administration’s rigid pavement systems
Three-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.
ANNFAA: artificial neural network-based tool for the analysis of Federal Aviation Administration’s rigid pavement systems
Three-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.
ANNFAA: artificial neural network-based tool for the analysis of Federal Aviation Administration’s rigid pavement systems
Rezaei Tarahomi, Adel (author) / Kaya, Orhan (author) / Ceylan, Halil (author) / Gopalakrishnan, Kasthurirangan (author) / Kim, Sunghwan (author) / Brill, David R. (author)
International Journal of Pavement Engineering ; 23 ; 400-413
2022-01-28
14 pages
Article (Journal)
Electronic Resource
Unknown
Information Systems Security: The Federal Aviation Administration's Layered Approach
| British Library Online Contents | 2000
The Federal Highway Administration's Long-Life Pavement Technology Program
| British Library Conference Proceedings | 2004
Review of Federal Highway Administration's System Architecture Program
| British Library Conference Proceedings | 1992
Life-cycle performance prediction for rigid runway pavement using artificial neural network
| Taylor & Francis Verlag | 2020