PREDICTION OF THE PERFORMANCE OF BITUMINOUS MIXES USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEMS

Jonata Jefferson Andrade, Leonardo Goliatt da Fonseca, Michèle Farage, Geraldo Luciano de Oliveira Marques

Resumo


Accurately forecast performance and durability is a critical issue for improving the design of new and existing pavements. The poor pavement performance increases traffic congestion, compromises safety, and raises maintenance costs due to frequent repairs. The resilient modulus is one of the most critical unbound material property inputs in several current pavement design procedures. Recent studies have addressed the problem of resilient modulus prediction using different methods, including computational intelligence approaches. In this paper, a hybrid intelligent system called ANFIS (Adaptive Neuro-Fuzzy Inference System) is used for predicting the resilient modulus from an experimental database of 270 distinct compositions. ANFIS achieved superior performance when estimating the resilient modulus of bituminous mixes, which can potentially save laboratory resources.


Palavras-chave


bituminous mixes; resilient modulus; ANFIS

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DOI: http://dx.doi.org/10.21575/25254782rmetg2020vol5n61367

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Direitos autorais 2020 Leonardo Goliatt, Jonata Jefferson Andrade, Michele Farage, Geraldo Luciano de Oliveira Marques

Revista Mundi Engenharia, Tecnologia e Gestão ISSN 2525-4782

Qualis: B4 - Interdisciplinar, B5 - Geografia, B5 - Administração Pública e de Empresas, Ciências Contábeis e Turismo, B5 - Comunicação e Informação, B5 - Engenharias III