Artificial Neural Network-Based New Methodology for Modeling of Asphalt Mixtures and Comparison with IKE Method

Yıl 2018, Cilt: 1 Sayı: 2, 1 - 13, 30.12.2018

Erol İskender , Atakan Aksoy , Şükrü Özşahin Hamdi Tolga Kahraman , Semih Dinçer Konak

Öz

Artificial
Neural Networks (ANNs) are the most adopted approach in modeling of engineering
problems. In this paper, we have developed ANN-based a novel modeling approach
for asphalt mixtures. The Flow, Stability and MQ of the mixtures have been
modeled and predicted by the introduced ANN-based approach. The legibility,
comprehensibility, consistency, estimation performance, standard deviation etc.
of the presented approach has been compared with the previous study. The
experimental studies have shown that the proposed approach provides robustness,
stability and a high accuracy ratio for estimation the Flow, Stability and MQ.
While this paper has presented a novel approach to modeling the asphalt
mixtures, it has also verified the results of literature. Thus, powerful,
efficient and alternative approaches were presented to the literature for
modeling the asphalt mixtures.

Anahtar Kelimeler

Intuitive k-nearest neighbor estimator (IKE), Artificial neural networks (ANN), Asphalt mixtures, Marshall stability test

Kaynakça

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