Support vector regression to predict asphalt mix performance

International Journal for Numerical and Analytical Methods in Geomechanics

Volumn 32, Issue 16, 2008, Pages 1989-1996

  Maalouf, Maher ^a   Khoury, Naji ^a   Trafalis, Theodore B ^a  

^a UNIVERSITY OF OKLAHOMA   (United States)

Author keywords

Hot mix asphalt; Pavement; Resilient modulus; Support vector regression

Indexed keywords

ASPHALT MIX; CORRELATION COEFFICIENT; HOT MIX ASPHALT; LEAST SQUARES; MATERIAL PROPERTY; MEAN SQUARED ERROR; OKLAHOMA; PAVEMENT MATERIAL; REGRESSION PROBLEM; RESILIENT MODULUS; STATISTICAL LEARNING; SUPPORT VECTOR REGRESSION; SUPPORT VECTOR REGRESSIONS;

ASPHALT; ASPHALT PAVEMENTS; LEARNING ALGORITHMS; ROADS AND STREETS; VECTORS;

REGRESSION ANALYSIS;

ALGORITHM; ASPHALT; PAVEMENT; PREDICTION; REGRESSION ANALYSIS;

OKLAHOMA [UNITED STATES]; UNITED STATES;

EID: 77956167422     PISSN: 03639061     EISSN: 10969853     Source Type: Journal    
DOI: 10.1002/nag.718     Document Type: Article

References (17)

1. Barksdale RD, Khosla P, Kim R, Lambe M, Rehman P. Laboratory determination of resilient modulus for flexible pavement design. NCHRP Project, 1997; 1-28.
2. Katicha SW. Development of laboratory to field shift factors for hot mix asphalt resilient modulus. Master's Thesis, Virginia Polytechnic Institute and State University, 2003.
3. Almudaiheem JA, Al-Sugair FH. Effect of loading magnitude on measured resilient modulus of asphaltic concrete mixes. Transportation Research Records 1991; 1317:139-144.
4. Brown ER, Foo KY. Evaluation of variability in resilient modulus test results (ASTM D 4123). Technical Report, NCAT Report No. 91-6, National Center for Asphalt Technology, 1989.
5. Schmidt RJ. A practical method for measuring the resilient modulus of asphalt treated mixes. Highway Research Record 1972; 404:22-32.
6. Vapnik V. The Nature of Statistical Learning. Springer: New York, NY, 1995.
7. Gunn SR. Support vector machine for classification and regression. Technical Report, University of Southampton, 1998.
8. Trafalis TB, Ince H, Richman M. Tornado detection with support vector machines. International Conference on Computational Science, Melbourne, 2003; 289-298.
9. Trafalis TB, Ince H. Support vector machine for regression and applications to financial forecasting. International Joint Conference on Neural Networks, vol. 6, 2000; 348-353.
10. Lee Y, Lee C. Classification of multiple cancer types by multicategory support vector machines using gene expression data. Bioinformatics 2003; 19:1132-1139.
11. Pal M. Support vector machines-based modelling of seismic liquefaction potential. International Journal for Numerical and Analytical Methods in Geomechanics 2006; 30:983-996.
12. Smola AJ, Scholkopf B. A tutorial on support vector regression. Statistics and Computing 2004; 14:199-222.
13. Shawe-Taylor J, Cristianini N. Kernel Methods for Pattern Analysis. Cambridge University Press: Cambridge, 2004.
14. Cristianini N, Shawe-Taylor J. An Introduction to Support Vector Machines and other Kernel-based Learning Methods. Cambridge University Press: Cambridge, 2000.
15. Navaratnarajah SK. Performance characteristics of selected asphalt mixes: a laboratory and field study. Master's Thesis, University of Oklahoma, 2006.
16. Berthold M, Hand DJ (eds). Intelligent Data Analysis. Springer: Berlin, 2003.
17. Chang CC, Lin CJ. Libsvm: a library for support vector machines. http://www.csie.ntu.edu.tw/~cjlin/libsvm. Accessed April 2007