The paper deals with the numerical prediction of the mechanical response of asphalt concretes for road pavements, using Artificial Neural Networks (ANN). The mixes considered in the study have been prepared with a diabase aggregate skeleton and two different type of bitumen, namely a conventional bituminous binder and a polymer modified one. The asphalt concretes were produced both in a road materials laboratory and in an asphalt concrete production plant. The mechanical behaviour of the mixes was investigated in terms of Marshall Stability, Flow, Quotient and moreover by the Stiffness Modulus. The artificial neural networks used for the numerical analysis of the experimental data, of the feed-forward type, resulted characterized by one hidden layer and 10 artificial neurons. The results of the ANN analysis have been extremely satisfactory. It has been feasible to elaborate a specific ANN model for the prediction of each of the four mechanical parameters considered, as a function of the production process, the bitumen type and content, the filler/bitumen ratio and the volumetric properties of the mixes, namely residual air voids, voids in the mineral aggregates and voids filled with bitumen.

Analysis of the Mechanical Behaviour of Asphalt Concretes Using Artificial Neural Networks

Pasetto, Marco
Supervision
2018

Abstract

The paper deals with the numerical prediction of the mechanical response of asphalt concretes for road pavements, using Artificial Neural Networks (ANN). The mixes considered in the study have been prepared with a diabase aggregate skeleton and two different type of bitumen, namely a conventional bituminous binder and a polymer modified one. The asphalt concretes were produced both in a road materials laboratory and in an asphalt concrete production plant. The mechanical behaviour of the mixes was investigated in terms of Marshall Stability, Flow, Quotient and moreover by the Stiffness Modulus. The artificial neural networks used for the numerical analysis of the experimental data, of the feed-forward type, resulted characterized by one hidden layer and 10 artificial neurons. The results of the ANN analysis have been extremely satisfactory. It has been feasible to elaborate a specific ANN model for the prediction of each of the four mechanical parameters considered, as a function of the production process, the bitumen type and content, the filler/bitumen ratio and the volumetric properties of the mixes, namely residual air voids, voids in the mineral aggregates and voids filled with bitumen.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11577/3278124
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