Artificial Neural Network Approach to Estimating Stiffness Behavior of Rubberized Asphalt Concrete Containing Reclaimed Asphalt Pavement

Accurately predicting the stiffness of asphalt pavements is difficult due to the complex behavior of materials under various loading, pavement structure, and environmental conditions. This study explores the utilization of the artificial neural network (ANN) in predicting the stiffness behavior of rubberized asphalt concrete mixtures with reclaimed asphalt pavement (RAP). A total of 296 asphalt mixture beams were constructed from two different rubber types (ambient and cryogenic), two different RAP sources, and four rubber contents (0, 5, 10, and 15%). All samples were tested at two different testing temperatures of 5 and 20°C. The regression statistical method was used to predict the stiffness behavior of these mixtures via the 7 input variables covering the material engineering properties of the asphalt beams. In addition, the data were organized into 5 independent variables and one dependent variable (the stiffness values of the modified mixture beams) in ANN models. Results showed the ANN techniques to be more effective in predicting the fatigue life of the modified mixture than traditional regression-based prediction models.

[1]  K. S. Wong,et al.  Estimation of lateral wall movements in braced excavations using neural networks , 1995 .

[2]  İlker Bekir Topçu,et al.  Prediction of properties of waste AAC aggregate concrete using artificial neural network , 2007 .

[3]  Mihail Chiru-Danzer.  Estimation of Liquefaction-Induced Vertical and Horizontal Displacements Using Artificial Neural Networks and Regression Analysis , 1999 .

[4]  Maria Q. Feng,et al.  Application of neural networks for estimation of concrete strength , 2002 .

[5]  Youssef M A Hashash,et al.  Visualization of material stiffness in geomechanics analysis , 2006 .

[6]  Rafiqul A. Tarefder,et al.  Neural Network Model for Asphalt Concrete Permeability , 2005 .

[7]  Yingjie Yang,et al.  A hierarchical analysis for rock engineering using artificial neural networks , 1997 .

[8]  Tuncer B. Edil,et al.  Evaluating stiffness and strength of pavement materials , 2005 .

[9]  Feipeng Xiao,et al.  Development of Fatigue Predictive Models of Rubberized Asphalt Concrete (RAC) Containing Reclaimed Asphalt Pavement (RAP) Mixtures , 2006 .

[10]  C. Hsein Juang,et al.  Rutting Resistance of Rubberized Asphalt Concrete Pavements Containing Reclaimed Asphalt Pavement Mixtures , 2007 .

[11]  C. Hsein Juang,et al.  A neural network approach to estimating deflection of diaphragm walls caused by excavation in clays , 2007 .

[12]  Mohammad Jamal Khattak,et al.  Fatigue and Permanent Deformation Models for Polymer-Modified Asphalt Mixtures , 2001 .

[13]  R. E. Link,et al.  Effects of Binders on Resilient Modulus of Rubberized Mixtures Containing RAP Using Artificial Neural Network Approach , 2009 .

[14]  E. Drumm,et al.  Environmental Effects on the Predicted Service Life of Flexible Pavements , 2007 .

[15]  Jinxia Chen.  Risk-based Liquefaction Potential Evaluation Using Cone Penetration Tests and Shear Wave Velocity Measurements , 1999 .

[16]  C. Hsein Juang,et al.  CPT‐Based Liquefaction Evaluation Using Artificial Neural Networks , 1999 .

[17]  R B Leahy,et al.  Crumb rubber modifier (CRM) in asphalt pavement: Summary of practices in Arizona, California, and Florida. Interim report, 1 February-30 June 1995 , 1995 .

[18]  Seongkyu Chang,et al.  Application of Probabilistic Neural Networks for Prediction of Concrete Strength , 2005 .

[19]  Philippe L. Bourdeau,et al.  Assessing the Liquefaction Susceptibility at a Site Based on Information from Penetration Testing , 1997 .

[20]  Kamil E Kaloush,et al.  Validation Of Initial and Failure Stiffness Definitions in Flexure Fatigue Test for Hot Mix Asphalt , 2007 .

[21]  A. Goh.  Seismic liquefaction potential assessed by neural networks , 1994 .

[22]  Serji N. Amirkhanian,et al.  Resilient Modulus Behavior of Rubberized Asphalt Concrete Mixtures Containing Reclaimed Asphalt Pavement , 2008 .

[23]  C. Hsein Juang,et al.  Prediction of Fatigue Life of Rubberized Asphalt Concrete Mixtures Containing Reclaimed Asphalt Pavement Using Artificial Neural Networks , 2009 .