Adaptive neuro-fuzzy inference approach for prediction the stiffness modulus on asphalt concrete

被引:10
|
作者
Ozgan, Ercan [1 ]
Korkmaz, Ibrahim [2 ]
Emiroglu, Mehmet [1 ]
机构
[1] Duzce Univ, Tech Educ Fac, Struct Dept, Duzce, Turkey
[2] Duzce Univ, Duzce Vocat Higher Sch, Machine Dept, Duzce, Turkey
关键词
Asphalt concrete; Stiffness modulus; Prediction model; Sugeno fuzzy inference; Temperature effect; Exposure times;
D O I
10.1016/j.advengsoft.2011.09.015
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In this study, stiffness modulus parameters of asphalt concrete were determined experimentally for different temperature and exposure times. The stiffness modules were calculated according to Nijboer stiffness module. Basic physical properties and the quantity of bitumen of asphalt core samples were designated for determining the stiffness modules. The samples were exposed to 17 degrees C (reference temperature), 30, 40 and 50 degrees C temperatures for 1.5, 3, 4.5 and 6 h respectively and then Marhall Stability tests were done for each samples. By using the test results a prediction model with Sugeno type based on the adaptive neuron-fuzzy inference system (ANFIS) was alternatively developed to predict the stiffness modules of asphalt core samples. As a result, it was seen that the developed prediction model could be used as a prediction model for unperformed situations which are not suitable for experiments. (C) 2011 Elsevier Ltd. All rights reserved.
引用
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页码:100 / 104
页数:5
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