Rapid Finite-Element Based Airport Pavement Moduli Solutions using Neural Networks
This paper describes the use of artificial neural
networks (ANN) for predicting non-linear layer moduli of flexible
airfield pavements subjected to new generation aircraft (NGA)
loading, based on the deflection profiles obtained from Heavy
Weight Deflectometer (HWD) test data. The HWD test is one of the
most widely used tests for routinely assessing the structural integrity
of airport pavements in a non-destructive manner. The elastic moduli
of the individual pavement layers backcalculated from the HWD
deflection profiles are effective indicators of layer condition and are
used for estimating the pavement remaining life. HWD tests were
periodically conducted at the Federal Aviation Administration-s
(FAA-s) National Airport Pavement Test Facility (NAPTF) to
monitor the effect of Boeing 777 (B777) and Beoing 747 (B747) test
gear trafficking on the structural condition of flexible pavement
sections. In this study, a multi-layer, feed-forward network which
uses an error-backpropagation algorithm was trained to approximate
the HWD backcalculation function. The synthetic database generated
using an advanced non-linear pavement finite-element program was
used to train the ANN to overcome the limitations associated with
conventional pavement moduli backcalculation. The changes in
ANN-based backcalculated pavement moduli with trafficking were
used to compare the relative severity effects of the aircraft landing
gears on the NAPTF test pavements.
[1] R. G. Hicks, "Factors influencing the resilient properties of granular
materials," Ph.D. dissertation, Univ. of California, Berkeley, 1970.
[2] M. R. Thompson and Q. L. Robnett, "Resilient properties of subgrade
soils," ASCE Transportation Engineering Journal, vol. 105, no. TE1,
1979.
[3] L. Raad and J. L. Figueroa, "Load response of transportation support
systems," ASCE Transportation Engineering Journal, vol 16, no. TE1,
1980.
[4] H. Ceylan, "Analysis and design of concrete pavement systems using
artificial neural networks," Ph.D. dissertation, Univ. of Illinois at
Urbana-Champaign, December, 2002.
[5] R. W. Meir and G. J. Rix, "Backcalculation of flexible pavement moduli
from dynamic deflection basins using artificial neural networks,"
Transportation Research Record no. 1473, TRB, 1995, pp. 72-81.
[6] N. Gucunski and V. Krstic, "Backcalculation of pavement profiles from
spectral analysis of surface waves test by neural networks using
individual receiver spacing approach," Transportation Research Record
no. 1526, TRB, 1996, pp. 6-13.
[7] L. Khazanovich and J. Roesler, "DIPLOBACK: neural-network based
backcalculation program for composite pavements," Transportation
Research Record no. 1570, TRB, 1997, pp. 143-150.
[8] Y. Kim and R. Y. Kim, "Prediction of layer moduli from falling weight
deflectometer and surface wave measurements using artificial neural
network," Transportation Research Record no. 1570, 1997, pp. 143-150.
[9] R. W. Meier and G. J. Rix, "Backcalculation of flexible pavement
moduli using artificial neural networks," in Proc. 73rd Annual Meeting of
the Transportation Research Board, Washington, D.C., 1993
[10] H. Ceylan, A. Guclu, E. Tutumluer, and M. R. Thompson,
"Backcalculation of full-depth asphalt pavement layer moduli
considering nonlinear stress-dependent subgrade behavior,"
International Journal of Pavement Engineering, vol. 6, no. 3, 2005, pp.
171-182.
[11] Gopalakrishnan, K., "Performance analysis of airport flexible pavement
subjected to new generation aircraft," Ph.D. Dissertation, University of
Illinois at Urbana-Champaign, December, 2004.
[12] G. Rada and M. W. Witczak, "Comprehensive evaluation of laboratory
resilient moduli results for granular material," Transportation Research
Record no. 810, TRB, 1970.
[13] M. R. Thompson and R. P. Elliot, "ILLI-PAVE based response
algorithms for design of conventional flexible pavements,"
Transportation Research Record no. 1043, TRB, 1985.
[14] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning internal
representation by error propogation," in Rumelhart, D.E. eds, Parallel
Distributed Processing, MIT Press, Cambridge, MA, 1986, pp. 318-362.
[15] H. Adeli and S. L. Hung, Machine learning: neural networks, genetic
algorithms, and fuzzy systems. Wiley, New York, 1995.
[16] S. Haykin. Neural networks: A comprehensive foundation.Prentice-Hall
Inc., NJ, USA, 1999.
[17] H. Adeli, "Neural Networks in civil engineering: 1989-2000,"
Computer-Aided Civil and Infrastructure Engineering, vol. 16, 2001, pp.
126-142.
[18] S. M. Hossain and J. P. Zaniewski, "Characterization of falling weight
deflectometer deflection basin," Transportation Research Record no.
1293, TRB, 1991, pp. 1-11.
[19] B. Xu, S. R. Ranjithan, and Y. R. Kim, "Development of relationships
between FWD deflections and asphalt pavement layer condition
indicators," in Proc., 81st Annual Meeting of the Transportation
Research Board, Washington, D.C., 2001.
[20] E. L. Gervais, G. F. Hayhoe, and N. Garg, "Towards a permanent ACN
solution for 6-wheel landing gear aircraft," in Proc., 2003 ASCE Airfield
Pavement Specialty Conference, Las Vegas, NV, 2003.
[21] G. H. Hayhoe, "LEAF - A new layered elastic computational program
for FAA pavement design and evaluation procedures," in Proc., 2002
Federal Aviation Administration Technology Transfer Conference,
Chicago, IL, 2002.
[22] R. D. McQueen, W. Marsey, and J. M. Arze, "Analysis of
nondestructive data on flexible pavement acquired at the national airport
pavement test facility," in Proc., 2001 Airfield Pavement Specialty
Conference, ASCE, Chicago, IL, 2001.
[1] R. G. Hicks, "Factors influencing the resilient properties of granular
materials," Ph.D. dissertation, Univ. of California, Berkeley, 1970.
[2] M. R. Thompson and Q. L. Robnett, "Resilient properties of subgrade
soils," ASCE Transportation Engineering Journal, vol. 105, no. TE1,
1979.
[3] L. Raad and J. L. Figueroa, "Load response of transportation support
systems," ASCE Transportation Engineering Journal, vol 16, no. TE1,
1980.
[4] H. Ceylan, "Analysis and design of concrete pavement systems using
artificial neural networks," Ph.D. dissertation, Univ. of Illinois at
Urbana-Champaign, December, 2002.
[5] R. W. Meir and G. J. Rix, "Backcalculation of flexible pavement moduli
from dynamic deflection basins using artificial neural networks,"
Transportation Research Record no. 1473, TRB, 1995, pp. 72-81.
[6] N. Gucunski and V. Krstic, "Backcalculation of pavement profiles from
spectral analysis of surface waves test by neural networks using
individual receiver spacing approach," Transportation Research Record
no. 1526, TRB, 1996, pp. 6-13.
[7] L. Khazanovich and J. Roesler, "DIPLOBACK: neural-network based
backcalculation program for composite pavements," Transportation
Research Record no. 1570, TRB, 1997, pp. 143-150.
[8] Y. Kim and R. Y. Kim, "Prediction of layer moduli from falling weight
deflectometer and surface wave measurements using artificial neural
network," Transportation Research Record no. 1570, 1997, pp. 143-150.
[9] R. W. Meier and G. J. Rix, "Backcalculation of flexible pavement
moduli using artificial neural networks," in Proc. 73rd Annual Meeting of
the Transportation Research Board, Washington, D.C., 1993
[10] H. Ceylan, A. Guclu, E. Tutumluer, and M. R. Thompson,
"Backcalculation of full-depth asphalt pavement layer moduli
considering nonlinear stress-dependent subgrade behavior,"
International Journal of Pavement Engineering, vol. 6, no. 3, 2005, pp.
171-182.
[11] Gopalakrishnan, K., "Performance analysis of airport flexible pavement
subjected to new generation aircraft," Ph.D. Dissertation, University of
Illinois at Urbana-Champaign, December, 2004.
[12] G. Rada and M. W. Witczak, "Comprehensive evaluation of laboratory
resilient moduli results for granular material," Transportation Research
Record no. 810, TRB, 1970.
[13] M. R. Thompson and R. P. Elliot, "ILLI-PAVE based response
algorithms for design of conventional flexible pavements,"
Transportation Research Record no. 1043, TRB, 1985.
[14] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning internal
representation by error propogation," in Rumelhart, D.E. eds, Parallel
Distributed Processing, MIT Press, Cambridge, MA, 1986, pp. 318-362.
[15] H. Adeli and S. L. Hung, Machine learning: neural networks, genetic
algorithms, and fuzzy systems. Wiley, New York, 1995.
[16] S. Haykin. Neural networks: A comprehensive foundation.Prentice-Hall
Inc., NJ, USA, 1999.
[17] H. Adeli, "Neural Networks in civil engineering: 1989-2000,"
Computer-Aided Civil and Infrastructure Engineering, vol. 16, 2001, pp.
126-142.
[18] S. M. Hossain and J. P. Zaniewski, "Characterization of falling weight
deflectometer deflection basin," Transportation Research Record no.
1293, TRB, 1991, pp. 1-11.
[19] B. Xu, S. R. Ranjithan, and Y. R. Kim, "Development of relationships
between FWD deflections and asphalt pavement layer condition
indicators," in Proc., 81st Annual Meeting of the Transportation
Research Board, Washington, D.C., 2001.
[20] E. L. Gervais, G. F. Hayhoe, and N. Garg, "Towards a permanent ACN
solution for 6-wheel landing gear aircraft," in Proc., 2003 ASCE Airfield
Pavement Specialty Conference, Las Vegas, NV, 2003.
[21] G. H. Hayhoe, "LEAF - A new layered elastic computational program
for FAA pavement design and evaluation procedures," in Proc., 2002
Federal Aviation Administration Technology Transfer Conference,
Chicago, IL, 2002.
[22] R. D. McQueen, W. Marsey, and J. M. Arze, "Analysis of
nondestructive data on flexible pavement acquired at the national airport
pavement test facility," in Proc., 2001 Airfield Pavement Specialty
Conference, ASCE, Chicago, IL, 2001.
@article{"International Journal of Architectural, Civil and Construction Sciences:64204", author = "Kasthurirangan Gopalakrishnan and Marshall R. Thompson and Anshu Manik", title = "Rapid Finite-Element Based Airport Pavement Moduli Solutions using Neural Networks", abstract = "This paper describes the use of artificial neural
networks (ANN) for predicting non-linear layer moduli of flexible
airfield pavements subjected to new generation aircraft (NGA)
loading, based on the deflection profiles obtained from Heavy
Weight Deflectometer (HWD) test data. The HWD test is one of the
most widely used tests for routinely assessing the structural integrity
of airport pavements in a non-destructive manner. The elastic moduli
of the individual pavement layers backcalculated from the HWD
deflection profiles are effective indicators of layer condition and are
used for estimating the pavement remaining life. HWD tests were
periodically conducted at the Federal Aviation Administration-s
(FAA-s) National Airport Pavement Test Facility (NAPTF) to
monitor the effect of Boeing 777 (B777) and Beoing 747 (B747) test
gear trafficking on the structural condition of flexible pavement
sections. In this study, a multi-layer, feed-forward network which
uses an error-backpropagation algorithm was trained to approximate
the HWD backcalculation function. The synthetic database generated
using an advanced non-linear pavement finite-element program was
used to train the ANN to overcome the limitations associated with
conventional pavement moduli backcalculation. The changes in
ANN-based backcalculated pavement moduli with trafficking were
used to compare the relative severity effects of the aircraft landing
gears on the NAPTF test pavements.", keywords = "Airfield pavements, ANN, backcalculation, newgeneration aircraft", volume = "1", number = "10", pages = "104-9", }
