All relevant data are within the paper and Reference 4: “Evaluation of the AASHTO Design Equations and Recommended Improvements,” Strategic Highway Research Program, National Research Council, Washington, DC, 1994. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: The author confirms that, for approved reasons, some access restrictions apply to the data underlying the findings. Received: AugAccepted: OctoPublished: November 14, 2014Ĭopyright: © 2014 Mesut Tiğdemir. PLoS ONE 9(11):Įditor: Wen-Bo Du, Beihang University, China It is concluded that the neural network may be an appropriate tool for the development of databased-nonparametric models of pavement performance.Ĭitation: Tiğdemir M (2014) Re-Evaluation of the AASHTO-Flexible Pavement Design Equation with Neural Network Modeling. Here we aimed to demonstrate that the proposed neural network model can more accurately represent the loads values data, compared against the performance of the AASHTO formula. The existing AASHTO flexible pavement design equation does not currently predict the pavement performance of the strategic highway research program (Long Term Pavement Performance studies) test sections very accurately, and typically over-estimates the number of equivalent single axle loads needed to cause a measured loss of the present serviceability index. The artificial neural network method is used for this purpose. Thus, those design traffic values that might result in deterioration can be better calculated using the neural networks model than with the AASHTO design equation. More importantly, we find that the neural network model gives the coefficients to be able to obtain the actual load values using the AASHTO design values.
#FLEXIBLE PAVEMENT DESIGN AASHTO EXAMPLE TRIAL#
Validation of the models is carried out against results from model tests, full-scale experimental trial sections and in-service pavement performance based on in-situ measurements and case study analyses.Here we establish that equivalent single-axle loads values can be estimated using artificial neural networks without the complex design equality of American Association of State Highway and Transportation Officials (AASHTO). In developing the proposed models, the basic causes, characteristics and fundamental mechanisms were definitively taken into account. Universal models for predicting progressive rut depth and geometry for both unreinforced and geosynthetics reinforced pavements, developed in this Study are presented. In order to successfully carry out such performance evaluation and prediction, the development of reliable models with as high precision and confidence levels as possible becomes exceedingly important. As a result, rutting is one of the major parameters considered for the performance evaluation of geosynthetics reinforced/improved pavement structures and the design thereof. The practical use of geosynthetics above a subgrade and/or within pavement layers has demonstrated the benefit of reducing rut depths and prolonging pavement life. Long-term pavement performance and extensive wheel tracking experimental testing has shown that rutting which is visible at the surface of asphalt layers can be caused by shear deformation within the bituminous mixture especially at low traffic speeds and high temperatures and/or plastic deformation in the underlying unbound layers constituting the foundation and/or subsoil.