University of Kentucky, Lexington, KY, USA Graves RC (2012) An Innovative approach to mechanistic empirical pavement design. González C, Mira J, Ojeda JA (2016) Applying multi-output random forest models to electricity price forecast. Construction and Building Materials 189:890–897 Gong H, Sun Y Shu X, Huang B (2018) Use of random forests regression for predicting IRI of asphalt pavements. Construction and Building Materials 204:203–212 Gong H, Sun Y, Hu W, Polaczyk P, Huang B (2019) Investigating impacts of asphalt mixture properties on pavement performance using LTPP data through random forests. 5(5):216–233, DOI: įederal Highway Administration (FHWA) (2017) Highway materials engineering course. Wiley Interdisciplinary Review: Data Mining and Knowledge Discovery. NCHRP 1–37A, National Cooperative Highway Research Program (NCHRP), Washington, DC, USAĪsphalt Institute (AI) (2020) US State binder specifications, Retrieved February 15, 2020, īorchani H, Varando G, Bielza C, Larrañaga P (2015) A survey on multi-output regression. (ARA) (2004) Guide for mechanistic-empirical design of new and rehabilitated pavement structures. Washington, DC, USAĪpplied Research Associates Inc. National Cooperative Highway Research Program (NCHRP), Washington, DC, USAĪmerican Association of State Highway and Transportation Officials (AASHTO) (2015) Mechanistic-empirical pavement design guide (A manual of practice). arXiv: 1907.11277v1, DOI: Īmerican Association of State Highway and Transportation Officials (AASHTO) (1993) Guide for design of pavement structures - 1993. This tool will simplify pavement design procedure based on the models in the AASHTOWare Pavement ME Design software.Īguiar GJ, Santana EJ, Mastelini SM, Mantovani RG, Jr SB (2019) Towards meta-learning for multi-target regression problems. The results indicate that the multi-output Random Forests model can accurately predict pavement distresses and thicknesses for asphalt and concrete pavements. The inputs and outputs of these design scenarios were used to develop the multioutput Random Forests model to simultaneously predict multiple pavement distresses and thicknesses of pavement layers. A total number of 79,600 pavement design scenarios (55,800 for flexible pavements and 23,800 for rigid pavements) were performed using the AASHTOWare Pavement ME Design software to consider various design inputs, such as: design life, traffic volume, climate zone, thickness, and modulus of pavement layers. To help the designer with a reasonable starting point in this iterative process, this paper introduces a machine learning method to embrace the recently updated models in AASHTOWare Pavement ME Design software for pavement design. The AASHTOWare Pavement ME Design program is a pavement analysis tool, which is typically used for design purposes through an iterative trial-and-error process.
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