Digital filters for Hot-Mix Asphalt Complex Modulus Test Data Using Genetic Algorithm Strategies
The dynamic or complex modulus test is considered
to be a mechanistically based laboratory test to reliably characterize
the strength and load-resistance of Hot-Mix Asphalt (HMA) mixes
used in the construction of roads. The most common observation is
that the data collected from these tests are often noisy and somewhat
non-sinusoidal. This hampers accurate analysis of the data to obtain
engineering insight. The goal of the work presented in this paper is to
develop and compare automated evolutionary computational
techniques to filter test noise in the collection of data for the HMA
complex modulus test. The results showed that the Covariance
Matrix Adaptation-Evolutionary Strategy (CMA-ES) approach is
computationally efficient for filtering data obtained from the HMA
complex modulus test.
[1] T. Harman, "Using the Dynamic Modulus Test to Assess the Mix
Strength of HMA," Public Roads 64 (6), 2001
[2] NCHRP, "Guide for Mechanistic-Empirical Design of New and
Rehabilitated Pavement Structures," NCHRP Project 1-37A,
Transportation Research Board, Washington, D.C., 2004.
[3] C.E. Dougan, J.E. Stephens, J. Mahoney, and G. Hansen. "E* - Dynamic
Modulus. Test Protocol - Problems and Solutions," Report No. CT-SPR-
0003084-F-03-3, Connecticut Department of Transportation, Rocky
Hill, Connecticut, 2003.
[4] C.W. Schwartz, "ENCE 447: Pavement Engineering." Unpublished
Course Material - Spring 2005, Department of Civil Engineering.
University of Maryland, College Park, Maryland, 2005.
[5] NCHRP 1-37, "A Draft Test Method A1. Standard test method for
dynamic modulus of asphalt concrete mixtures and master curves,"
Arizona State University, Tempe, AZ, 2000.
[6] J.S. Daniel, G.R. Chehab, and Y.R. Kim. "Issues Affecting
Measurement of the Complex Modulus of Asphalt Concrete," Journal of
Materials in Civil Engineering 16(5): 469-476, 2004.
[7] T. Pellinen, "Comparison of analysis techniques to obtain modulus and
phase angle from sinusoidal test data," Proc. 6th International RILEM
Symposium on Performance Testing and Evaluation of Bituminous
Material, PTEBM -03, Zurich, Switzerland, 2003.
[8] R. Levy and S.B. Cohn, "A History of Microwave Filter Research,
Design, and Development," IEEE Transactions on Microwave Theory
and Techniques, Vol. MIT-32, No. 9, 1984.
[9] D.E. Goldberg, Genetic Algorithms in search, optimization and machine
learning, Addison-Wesley, 1989.
[10] K.S. Tang, K.F. Man, S. Kwong and Q. He, "Genetic Algorithms and
their applications," IEEE Signal Processing Magazine, 13(6), 1996.
[11] J.H. McClellan and T.W. Parks, "A unified approach to the design of
optimum FIR linear-phase digital filters," IEEE Tran. Circuit theory,
CT-20: 697-701, 1973.
[12] J.W. Adams and A.N. Jr. Wilson, "A new approach to FIR digital filters
with fewer multipliers and reduced sensitivity," IEEE Trans. Circuits
and Systems, CAS-30: 277-283, 1983.
[13] J.W. Adams and A.N. Jr. Wilson, "Some efficient digital pre-filter
structures," IEEE Tran., CAS-31: 260-265, 1984.
[14] Y. Neuvo, D. Cheng-Yu, and S.K. Mitra, "Interpolated finite impulse
response filters," IEEE Trans. Acoustics, Speech, and Signal Processing,
ASSP-32: 563-570, 1984.
[15] G. Wade, P. Van-Eetvelt, and H. Darwen, "Synthesis of efficient loworder
FIR filters from primitive sections," IEE Proc. G, 137: 70-87,
1984.
[16] D. Suckley, "Genetic algorithm in the design of FIR," IEE Proceedings,
Part G: Electronic Circuits & Systems, 138(2): 234-238, 1991.
[17] A. Roberts and G. Wade, "A structured GA for FIR filter design," Proc.
workshop on Natural Algorithms in Signal Processing, Vol. 1,
Chelmsford, UK, 1993.
[18] X.J. Dexiang and M.L. Delay, "Design of Finite Word Length digital
filters using a parallel genetic algorithm," Proc. IEEE Conference, pp.
834-837, 1992.
[19] P. Gentili, F. Piazza, and A. Uncini, "Improved power-of-two
sharpening filter design by genetic algorithm," ICASSP, Proc. IEEE Int.
Conf. on Acoustics, Speech and Signal Processing, Vol. 3, Piscataway,
NJ, pp. 1375-1377, 1996.
[20] MATLAB. MATLAB® online help. MathWorks, Inc., 2004.
[21] H.P. Schwefel, Evolution and Optimum Seeking, Sixth Generation
Computer Technology Series, New York: John Wiley and Sons Inc.,
1995.
[22] N. Hansen and A. Ostermeier, "Completely Derandomized Self-
Adaptation in Evolution Strategies," Evolutionary Computation, 9(2):
159-195, 2001.
[23] N. Hansen, S. D. M├╝ller, and P. Koumoutsakos, "Reducing the Time
Complexity of the Derandomized Evolution Strategy with Covariance
Matrix Adaptation (CMA-ES)," Evolutionary Computation, 11(1): 1-18,
2003.
[1] T. Harman, "Using the Dynamic Modulus Test to Assess the Mix
Strength of HMA," Public Roads 64 (6), 2001
[2] NCHRP, "Guide for Mechanistic-Empirical Design of New and
Rehabilitated Pavement Structures," NCHRP Project 1-37A,
Transportation Research Board, Washington, D.C., 2004.
[3] C.E. Dougan, J.E. Stephens, J. Mahoney, and G. Hansen. "E* - Dynamic
Modulus. Test Protocol - Problems and Solutions," Report No. CT-SPR-
0003084-F-03-3, Connecticut Department of Transportation, Rocky
Hill, Connecticut, 2003.
[4] C.W. Schwartz, "ENCE 447: Pavement Engineering." Unpublished
Course Material - Spring 2005, Department of Civil Engineering.
University of Maryland, College Park, Maryland, 2005.
[5] NCHRP 1-37, "A Draft Test Method A1. Standard test method for
dynamic modulus of asphalt concrete mixtures and master curves,"
Arizona State University, Tempe, AZ, 2000.
[6] J.S. Daniel, G.R. Chehab, and Y.R. Kim. "Issues Affecting
Measurement of the Complex Modulus of Asphalt Concrete," Journal of
Materials in Civil Engineering 16(5): 469-476, 2004.
[7] T. Pellinen, "Comparison of analysis techniques to obtain modulus and
phase angle from sinusoidal test data," Proc. 6th International RILEM
Symposium on Performance Testing and Evaluation of Bituminous
Material, PTEBM -03, Zurich, Switzerland, 2003.
[8] R. Levy and S.B. Cohn, "A History of Microwave Filter Research,
Design, and Development," IEEE Transactions on Microwave Theory
and Techniques, Vol. MIT-32, No. 9, 1984.
[9] D.E. Goldberg, Genetic Algorithms in search, optimization and machine
learning, Addison-Wesley, 1989.
[10] K.S. Tang, K.F. Man, S. Kwong and Q. He, "Genetic Algorithms and
their applications," IEEE Signal Processing Magazine, 13(6), 1996.
[11] J.H. McClellan and T.W. Parks, "A unified approach to the design of
optimum FIR linear-phase digital filters," IEEE Tran. Circuit theory,
CT-20: 697-701, 1973.
[12] J.W. Adams and A.N. Jr. Wilson, "A new approach to FIR digital filters
with fewer multipliers and reduced sensitivity," IEEE Trans. Circuits
and Systems, CAS-30: 277-283, 1983.
[13] J.W. Adams and A.N. Jr. Wilson, "Some efficient digital pre-filter
structures," IEEE Tran., CAS-31: 260-265, 1984.
[14] Y. Neuvo, D. Cheng-Yu, and S.K. Mitra, "Interpolated finite impulse
response filters," IEEE Trans. Acoustics, Speech, and Signal Processing,
ASSP-32: 563-570, 1984.
[15] G. Wade, P. Van-Eetvelt, and H. Darwen, "Synthesis of efficient loworder
FIR filters from primitive sections," IEE Proc. G, 137: 70-87,
1984.
[16] D. Suckley, "Genetic algorithm in the design of FIR," IEE Proceedings,
Part G: Electronic Circuits & Systems, 138(2): 234-238, 1991.
[17] A. Roberts and G. Wade, "A structured GA for FIR filter design," Proc.
workshop on Natural Algorithms in Signal Processing, Vol. 1,
Chelmsford, UK, 1993.
[18] X.J. Dexiang and M.L. Delay, "Design of Finite Word Length digital
filters using a parallel genetic algorithm," Proc. IEEE Conference, pp.
834-837, 1992.
[19] P. Gentili, F. Piazza, and A. Uncini, "Improved power-of-two
sharpening filter design by genetic algorithm," ICASSP, Proc. IEEE Int.
Conf. on Acoustics, Speech and Signal Processing, Vol. 3, Piscataway,
NJ, pp. 1375-1377, 1996.
[20] MATLAB. MATLAB® online help. MathWorks, Inc., 2004.
[21] H.P. Schwefel, Evolution and Optimum Seeking, Sixth Generation
Computer Technology Series, New York: John Wiley and Sons Inc.,
1995.
[22] N. Hansen and A. Ostermeier, "Completely Derandomized Self-
Adaptation in Evolution Strategies," Evolutionary Computation, 9(2):
159-195, 2001.
[23] N. Hansen, S. D. M├╝ller, and P. Koumoutsakos, "Reducing the Time
Complexity of the Derandomized Evolution Strategy with Covariance
Matrix Adaptation (CMA-ES)," Evolutionary Computation, 11(1): 1-18,
2003.
@article{"International Journal of Architectural, Civil and Construction Sciences:52520", author = "Madhav V. Chitturi and Anshu Manik and Kasthurirangan Gopalakrishnan", title = "Digital filters for Hot-Mix Asphalt Complex Modulus Test Data Using Genetic Algorithm Strategies", abstract = "The dynamic or complex modulus test is considered
to be a mechanistically based laboratory test to reliably characterize
the strength and load-resistance of Hot-Mix Asphalt (HMA) mixes
used in the construction of roads. The most common observation is
that the data collected from these tests are often noisy and somewhat
non-sinusoidal. This hampers accurate analysis of the data to obtain
engineering insight. The goal of the work presented in this paper is to
develop and compare automated evolutionary computational
techniques to filter test noise in the collection of data for the HMA
complex modulus test. The results showed that the Covariance
Matrix Adaptation-Evolutionary Strategy (CMA-ES) approach is
computationally efficient for filtering data obtained from the HMA
complex modulus test.", keywords = "HMA, dynamic modulus, GA, evolutionarycomputation.", volume = "2", number = "6", pages = "114-7", }
