The Application of Artificial Neural Network for the Prediction of the Deformation Performance of Hot-Mix Asphalt

2011 ◽

Vol 304 ◽

pp. 18-23

Author(s):

Chun Hua Hu

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Test Data ◽

Hot Mix Asphalt ◽

Resilient Modulus ◽

Structure Design ◽

Forecast Precision ◽

Long Time ◽

Artificial Neural ◽

Artificial Neural Network Ann

Resilient modulus of material is an important parameter for pavement structure design and analysis. However it is very tedious to get this parameter for hot mixture asphalt in laboratory. Moreover it takes long time to do experiments. In this paper, artificial neural network (ANN) is applied to predict to resilient modulus for hot mixture asphalt. A neural network model is constructed and trained plenty of times with selected test data until precision meets requirement. Then the model is used to predict resilient modulus for hot mix asphalt. Result of contrast prediction with test data shows that forecast precision is high. This provides a new method to predict resilient modulus for hot mixture asphalt.

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Artificial neural network modeling (ANN) for predicting rutting performance of nano-modified hot-mix asphalt mixtures containing steel slag aggregates

Construction and Building Materials ◽

10.1016/j.conbuildmat.2015.03.060 ◽

2015 ◽

Vol 85 ◽

pp. 136-143 ◽

Cited By ~ 49

Author(s):

G.H. Shafabakhsh ◽

O. Jafari Ani ◽

M. Talebsafa

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Hot Mix Asphalt ◽

Network Modeling ◽

Steel Slag ◽

Asphalt Mixtures ◽

Neural Network Modeling ◽

Artificial Neural Network Modeling ◽

Artificial Neural

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Development of artificial neural network and multiple linear regression models in the prediction process of the hot mix asphalt properties

Canadian Journal of Civil Engineering ◽

10.1139/cjce-2017-0300 ◽

2017 ◽

Vol 44 (12) ◽

pp. 994-1004 ◽

Cited By ~ 6

Author(s):

Ivica Androjić ◽

Ivan Marović

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Linear Regression ◽

Multiple Linear Regression ◽

Regression Models ◽

Prediction Models ◽

Hot Mix Asphalt ◽

Linear Regression Models ◽

Artificial Neural ◽

Multiple Linear Regression Models

The oscillation of asphalt mix composition on a daily basis significantly affects the achieved properties of the asphalt during production, thus resulting in conducting expensive laboratory tests to determine existing properties and predicting the future results. To decrease the amount of such tests, a development of artificial neural network and multiple linear regression models in the prediction process of predetermined dependent variables air void and soluble binder content is presented. The input data were obtained from a single laboratory and consists of testing 386 mixes of hot mix asphalt (HMA). It was found that it is possible and desirable to apply such models in the prediction process of the HMA properties. The final aim of the research was to compare results of the prediction models on an independent dataset and analyze them through the boundary conditions of technical regulations and the standard EN 13108-21.

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