Creep compliance of the hot-mix asphalt (HMA) is a primary input of the current pavement thermal cracking prediction model used in the US. This paper discusses a process of training an Artificial Neural Network (ANN) to correlate the creep compliance values obtained from the Indirect Tension (IDT) with similar values obtained on small HMA beams from the Bending Beam Rheometer (BBR). In addition, ANNs are also trained to predict HMA creep compliance from the creep compliance of asphalt binder and vice versa using the BBR setup. All trained ANNs exhibited a very high correlation of 97 to 99 percent between predicted and measured values. The binder creep compliance functions built on the ANN-predicted discrete values also exhibited a good correlation when compared with the laboratory experiments. However, the simulation of trained ANNs on the independent dataset produced a significant deviation from the measured values which was most likely caused by the differences in material composition, such as aggregate type and gradation, presence of recycled additives, and binder type.
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1. S. Haykin Neural networks and learning machines Prentice Hall New York NY 1999.
2. I. Flood N. Kartam Neural networks in civil engineering. I: Principles and understanding. Journal of Computing in Civil Engineering 8 131-48 1994a.
3. I. Flood N. Kartam Neural networks in civil engineering. II: Systems and application. Journal of Computing in Civil Engineering 8 149-62 1994b.
4. M. Dougherty A review of neural networks applied to transport. Transportation Research Part C: Emerging Technologies 3 247-60 1995.
5. N.O. Attoh-Okine S. Mensah Potential applications of system identification techniques in pavement performance modeling. Proceedings of the Second International Symposium on Maintenance and Rehabilitation of Pavements and Technological Control National Center for Asphalt Technology Auburn Alabama 2001.
6. S. Chou T.K. Pellinen Assessment of construction smoothness specification pay factor limits using artificial neural network modeling. Journal of Transportation Engineering 131 563-70 2005.
7. R.A. Tarefder L. White M. Zaman Development and application of a rut prediction model for flexible pavement. Transportation Research Record 1936 201-9 2005a.
8. R.A. Tarefder L. White M. Zaman Neural network model for asphalt concrete permeability. Journal of Materials in Civil Engineering 17 19-27 2005b.
9. H. Ceylan A. Guclu E. Tutumluer M.R. Thompson Backcalculation of full-depth asphalt pavement layer moduli considering nonlinear stress-dependent subgrade behavior. International Journal of Pavement Engineering 6 171-82 2005.
10. A. Molenaar A. Meerkerk M. Miradi T. van der Steen Performance of porous asphalt concrete. Journal of the Association of Asphalt Paving Technologists 75 1053-94 2006.
11. C. Huang Y.M. Najjar S.A. Romanoschi Predicting asphalt concrete fatigue life using artificial neural network approach. Paper No. 07-1607 86th Transportation Research Board Annual Meeting (CD-ROM) Transportation Research Board National Research Council Washington DC. 2007.
12. M. Zeghal Thermal cracking prediction using artificial neural network. In Al-Qadi Scarpas & Loizos (Ed.) Pavement Cracking Taylor and Francis Group 379-86 2008a.
13. M. Zeghal Modeling the creep compliance of asphalt concrete using the artificial neural network technique. Proceedings of the Annual Congress of the Geo-Institute of ASCE (GeoCongress 2008) New Orleans LA 1-7 2008b.
14. A.T. LaCroix Y.R. Kim S.R. Ranjithan Backcalculation of dynamic modulus from resilient modulus of asphalt concrete with an artificial neural network. Transportation Research Record: Journal of Transportation Research Board 2057 107-13 2008.
15. H. Ceylan K. Gopalakrishnan M.B. Bayrakc Neural networks based concrete airfield pavement layer moduli backcalculation. Civil Engineering and Environmental Systems 25 185-99 2008.
16. F. Xiao S.N. Amirkhanian Artificial neural network approach to estimating stiffness behavior of rubberized asphalt concrete containing reclaimed asphalt pavement. Journal of Transportation 135 8 580-9 2009.
17. J.P. Hallin et al. Development of the 2002 guide for the design of new and rehabilitated pavement structures: Phase II. Report for National Cooperative Highway Research Program Transportation Research Board National Research Council Washington DC. 2004.
18. R. Roque W.G. Buttlar The development of a measurement and analysis system to accurately determine asphalt concrete properties using the Indirect Tensile mode. Journal of the Association of Asphalt Paving Technologists 61 304-28 1992.
19. H. Bahia D.A. Anderson D. Christensen The Bending Beam Rheometer; a simple device for measuring low-temperature rheology of asphalt binders. Journal of Association of Asphalt Paving Technologists 61 117-53 1992.
20. A. Zofka M. Marasteanu X. Li T. Clyne J. McGraw Simple method to obtain asphalt binders low temperature properties from asphalt mixtures properties. Journal of the Association of Asphalt Paving Technologists 80 255-82 2005.
21. A. Zofka I. Yut Alternative procedure for determination of hot mix asphalt creep compliance. ASTM Journal of Testing and Evaluation 39 1 1-11 2011.
22. A. Zofka Investigation of asphalt concrete creep behavior using 3-point bending test. Ph.D. dissertation University of Minnesota Minneapolis MN 2007.
23. A. Zofka M. Marasteanu M. Turos Determination of asphalt mixture creep compliance at low temperatures using thin beam specimens. Transportation Research Record 2057 134-9 2008a.
24. A. Zofka M. Marasteanu M. Turos Investigation of asphalt mixture creep compliance at low temperatures. Journal of Road Materials and Pavement Design 9 269-286 2008b.
25. H. Demuth M. Beale M. Hagan Neural network toolbox 5 user’s guide. The MathWorks Inc. Natick MA 2007.
26. J.E. Moody The effective number of parameters: an analysis of generalization and regularization in nonlinear learning systems. Advances in neural information processing systems 4 Morgan Kaufman Publishers San Mateo CA 1992.
27. R.D. Reed R.J. Marks Neural smithing: supervised learning in feedforward artificial neural networks The MIT Press Cambridge MA 1992.
28. W. Zhang A. Drescher D.E. Newcomb Viscoelastic analysis of diametral compression of asphalt concrete. Journal of Engineering Mechanics 123 6 596-603 1997.
29. D. Christensen Analysis of creep data from Indirect Tension test on asphalt concrete. Journal of the Association of Asphalt Paving Technologists 67 458-77 1998.
30. AASHTO Standard T 322-03 Determining the creep compliance and strength of hot-mix asphalt (HMA) using the Indirect Tensile test device. Standard Specifications for Transportation Materials and Methods of Sampling and Testing AASHTO Washington DC 2005a.
31. AASHTO Standard T313-05 Standard method of test for determining the flexural creep stiffness of asphalt binder using the Bending Beam Rheometer (BBR) Standard Specifications for Transportation Materials and Methods of Sampling and Testing AASHTO Washington DC 2005b.
32. J.M. Gere S.P. Timoshenko Mechanics of materials Third Edition PWS-KENT Publishing Company Boston MA 1990.
33. AASHTO Standard T240-03 Standard method of test for effect of heat and air on a moving film of asphalt (Rolling Thin-Film Oven Test) Standard Specifications for Transportation Materials and Methods of Sampling and Testing AASHTO Washington DC 2005c.
34. W. Sarle Neural Networks: Frequently Asked Questions. Retrieved December 1 2011 from ftp://ftp.sas.com/pub/neural/FAQ.html 1997.
35. AASHTO Standard PP Practice for accelerated aging of asphalt binder using Pressurized Aging Vessel (PAV) Standard Specifications for Transportation Materials and Methods of Sampling and Testing AASHTO Washington DC 1998.
36. M. Marasteanu et al. Investigation of low temperature cracking in asphalt pavements - National Pooled Fund Study 776. Report MN/RC 2007-43 Minnesota Department of Transportation St. Paul MN 2007.