Abstract
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.
Highlights
The structure of a pavement consists of the subbase, the base course, and the surface course
The resilient modulus is used in the design of asphalt pavements to compute stresses, strains, and deformations induced in the pavement structure by the applied traffic loads (FAKHRI; GHANIZADEH, 2014)
To compare the performance of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for different NF values, three metrics described in the last section were considered
Summary
The structure of a pavement consists of the subbase, the base course, and the surface course. The surface course or surface layer is the layer of a pavement structure designed to accommodate the traffic load. Determined directly from dynamic tests, MR is one of the main mechanical properties of asphalt mixes. This modulus is defined as the ratio of the applied cyclic stress to the recoverable (elastic) strain after many cycles of repeated loading and is a direct measure of stiffness for unbound materials in pavement systems (AASHTO, 1986). The resilient modulus is used in the design of asphalt pavements to compute stresses, strains, and deformations induced in the pavement structure by the applied traffic loads (FAKHRI; GHANIZADEH, 2014). The temperature is an important parameter, since it can change the viscosity of the mix and may considerably affect its compactibility
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Schedule a callMore From: Revista Mundi Engenharia, Tecnologia e Gestão (ISSN: 2525-4782)
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