Quantifying the Differential Phase Angle Behaviour of Asphalt Concrete Mixtures Using Artificial Neural Networks

2021 ◽  
Author(s):  
Fizza Hussain ◽  
Yasir Ali ◽  
Muhammad Irfan
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Phase Angle ◽  
Asphalt Concrete ◽  
Differential Phase ◽  
Concrete Mixtures ◽  
Artificial Neural

Application of Artificial Neural Networks for Estimating Dynamic Modulus of Asphalt Concrete

2009 ◽  
Vol 2127 (1) ◽  
pp. 173-186 ◽  
Author(s):  
Maryam Sadat Sakhaei Far ◽  
B. Shane Underwood ◽  
S. Ranji Ranjithan ◽  
Y. Richard Kim ◽  
Newton Jackson
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Asphalt Concrete ◽  
Dynamic Modulus ◽  
Artificial Neural

Asphalt concrete stability estimation from non-destructive test methods with artificial neural networks

2012 ◽  
Vol 23 (3-4) ◽  
pp. 989-997
Author(s):  
Serdal Terzi ◽  
Mustafa Karaşahin ◽  
Süleyman Gökova ◽  
Mustafa Tahta ◽  
Nihat Morova ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Asphalt Concrete ◽  
Test Methods ◽  
Stability Estimation ◽  
Destructive Test ◽  
Artificial Neural ◽  
Non Destructive

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

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Nicola Baldo ◽  
Evangelos Manthos ◽  
Marco Pasetto
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Mechanical Behaviour ◽  
Asphalt Concrete ◽  
Mechanical Response ◽  
Production Plant ◽  
Concrete Production ◽  
Artificial Neural ◽  
And Performance ◽  
Hidden Layer

The current paper deals with the numerical prediction of the mechanical response of asphalt concretes for road pavements, using artificial neural networks (ANNs). The asphalt concrete mixes considered in this study have been prepared with a diabase aggregate skeleton and two different types 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 feedforward type, were characterized by one hidden layer and 10 artificial neurons. The results have been extremely satisfactory, with coefficients of correlation in the testing phase within the range 0.98798–0.91024, depending on the considered model, thus demonstrating the feasibility to apply ANN modelization to predict the mechanical and performance response of the asphalt concretes investigated. Furthermore, a closed-form equation has been provided for each of the four ANN models developed, assuming as input parameters the production process, the bitumen type and content, the filler/bitumen ratio, and the volumetric properties of the mixes. Such equations allow any other researcher to predict the mechanical parameter of interest, within the framework of the present study.

Use of Artificial Neural Networks to Detect Aggregates in Poor-Quality X-Ray CT Images of Asphalt Concrete

2008 ◽  
Author(s):  
M. Emin Kutay ◽  
Edith Arambula ◽  
Nelson Gibson ◽  
Jack Youtcheff ◽  
Katherine Petros
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Asphalt Concrete ◽  
Poor Quality ◽  
Ct Images ◽  
X Ray ◽  
Artificial Neural
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