CLC number: V21
On-line Access:
Received: 2007-12-27
Revision Accepted: 2008-05-04
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Kasthurirangan GOPALAKRISHNAN. Evaluation of accelerated deterioration in NAPTF flexible test pavements[J]. Journal of Zhejiang University Science A, 2008, 9(9): 1157-1166.
@article{title="Evaluation of accelerated deterioration in NAPTF flexible test pavements",
author="Kasthurirangan GOPALAKRISHNAN",
journal="Journal of Zhejiang University Science A",
volume="9",
number="9",
pages="1157-1166",
year="2008",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A0720153"
}
%0 Journal Article
%T Evaluation of accelerated deterioration in NAPTF flexible test pavements
%A Kasthurirangan GOPALAKRISHNAN
%J Journal of Zhejiang University SCIENCE A
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%D 2008
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A0720153
TY - JOUR
T1 - Evaluation of accelerated deterioration in NAPTF flexible test pavements
A1 - Kasthurirangan GOPALAKRISHNAN
J0 - Journal of Zhejiang University Science A
VL - 9
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SP - 1157
EP - 1166
%@ 1673-565X
Y1 - 2008
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A0720153
Abstract: Previous research studies have successfully demonstrated the use of artificial neural network (ANN) models for predicting critical structural responses and layer moduli of highway flexible pavements. The primary objective of this study was to develop an ANN-based approach for backcalculation of pavement moduli based on heavy weight deflectometer (HWD) test data, especially in the analysis of airport flexible pavements subjected to new generation aircraft (NGA). Two medium-strength subgrade flexible test sections, at the National Airport Pavement Test Facility (NAPTF), were modeled using a finite element (FE) based pavement analysis program, which can consider the non-linear stress-dependent behavior of pavement geomaterials. A multi-layer, feed-forward network which uses an error-backpropagation algorithm was trained to approximate the HWD backcalculation function using the FE program generated synthetic database. At the NAPTF, test sections were subjected to Boeing 777 (B777) trafficking on one lane and Boeing 747 (B747) trafficking on the other lane using a test machine. To monitor the effect of traffic and climatic variations on pavement structural responses, HWD tests were conducted on the trafficked lanes and on the untrafficked centerline of test sections as trafficking progressed. The trained ANN models were successfully applied on the actual HWD test data acquired at the NAPTF to predict the asphalt concrete moduli and non-linear subgrade moduli of the medium-strength subgrade flexible test sections.
[1] Ahlvin, R.G., 1991. Origin of Developments for Structural Design of Pavements. Technical Report GL-91-26, Waterways Experiment Station. US Army Corps of Engineers, Vicksburg, Mississippi, USA.
[2] Bush, A.J.III, Baladi, G.Y., 1989. Nondestructive Testing of Pavements and Backcalculation of Moduli. ASTM Special Technical Publication (STP) 1276.
[3] Ceylan, H., 2002. Analysis and Design of Concrete Pavement Systems Using Artificial Neural Networks. Ph.D Thesis, University of Illinois at Urbana-Champaign.
[4] Ceylan, H., Guclu, A., Tutumluer, E., Thompson, M.R., 2005. Backcalculation of full-depth asphalt pavement layer moduli considering nonlinear stress-dependent subgrade behavior. International Journal of Pavement Engineering, 6(3):171-182.
[5] Garg, N., Marsey, W.H., 2002. Comparison Between Falling Weight Deflectometer and Static Deflection Measurements on Flexible Pavement at the National Airport Pavement Facility (NAPTF). Proceedings of the 2002 FAA Airport Technology Conference, Chicago, IL.
[6] Garg, N., Tutumluer, E., Thompson, M.R., 1998. Structural Modeling Concepts for the Design of Airport Pavements for Heavy Aircraft. Proceedings of the Fifth International Conference on the Bearing Capacity of Roads and Airfields, Trondheim, Norway.
[7] Ghuzlan, K.A., 2001. Fatigue Damage Analysis in Asphalt Concrete Mixtures Based Upon Dissipated Energy Concepts. Ph.D Thesis, University of Illinois at Urbana-Champaign, Urbana, IL.
[8] Gomez-Ramirez, F.M., Thompson, M.R., 2002. Characterizing Aircraft Multiple Wheel Load Interaction for Airport Flexible Pavement Design. FAA COE Research Report No.20, University of Illinois at Urbana-Champaign.
[9] Gopalakrishnan, K., 2004. Performance Analysis of Airport Flexible Pavements Subjected to New Generation Aircraft. Ph.D Thesis, University of Illinois at Urbana-Champaign.
[10] Hayhoe, G.H., 2002. LEAF—a New Layered Elastic Computational Program for FAA Pavement Design and Evaluation Procedures. Proceedings of the 2002 FAA Airport Technology Transfer Conference, Chicago, IL.
[11] Hayhoe, G.F., Garg, N., 2003. Posttraffic Testing on Medium-strength Subgrade Flexible Pavements at the National Airport Pavement Test Facility. Proceedings of the 2003 ASCE Airfield Pavement Specialty Conference, Las Vegas, NV.
[12] Hicks, R.G., 1970. Factors Influencing the Resilient Properties of Granular Materials. Ph.D Thesis, University of California, Berkeley.
[13] Hossain, S.M., Zaniewski, J.P., 1991. Characterization of Falling Weight Deflectometer Deflection Basin. Transportation Research Record 1293, Transportation Research Board, Washington, DC.
[14] McQueen, R.D., Marsey, W., Arze, J.M., 2001. Analysis of Nondestructive Data on Flexible Pavement Acquired at the National Airport Pavement Test Facility. Proceedings of the 2001 Airfield Pavement Specialty Conference, ASCE, Chicago, IL.
[15] Meier, R.W., Rix, G.J., 1995. Backcalculation of flexible pavement moduli from dynamic deflection basins using artificial neural networks. Transportation Research Record, 1473:72-81.
[16] Meier, R.W., Alexander, D.R., Freeman, R.B., 1997. Using artificial neural networks as a forward approach to backcalculation. Transportation Research Record, 1570:126-133.
[17] NCHRP (National Co-operative Highway Research Program), 1990. Calibrated Mechanistic Structural Analysis Procedures for Pavements. Final Report, National Research Council, Washington, DC, p.1-26.
[18] Pekcan, O., Tutumluer, E., Thompson, M.R., 2006. Nonde-structive Flexible Pavement Evaluation Using ILLI-PAVE Based Artificial Neural Network Models. Proceedings of GeoCongress 2006 Conference, Atlanta, GA.
[19] Raad, L., Figueroa, J.L., 1980. Load response of transportation support systems. The Journal of Transportation Engineering ASCE, 16(TE1):111-128.
[20] Rada, G., Witczak, M.W., 1981. Comprehensive Evaluation of Laboratory Resilient Moduli Results for Granular Material. Transportation Research Record 810, Transportation Research Board, Washington, DC.
[21] Rumelhart, D.E., Hinton, G.E., Williams, R.J., 1986. Learning Internal Representation by Error Propogation. Parallel Distributed Processing. MIT Press, Cambridge, MA.
[22] Sharp, K.G., Johnson-Clarke, J.R., 1997. Australian Experience in the Accelerated Testing of Pavements. Proceedings of the Thirteenth International Road Federation (IRF) World Meeting, Toronto, Canada.
[23] Tayabji, S.D., Lukanen, E.O., 2000. Nondestructive Testing of Pavements and Backcalculation of Moduli. ASTM Special Technical Publication (STP) 1375.
[24] Thompson, M.R., Robnett, Q.L., 1979. Resilient properties of subgrade soils. The Journal of Transportation Engineering ASCE, 105(1):71-89.
[25] Thompson, M.R., Elliot, R.P., 1985. ILLI-PAVE Based Response Algorithms for Design of Conventional Flexible Pavements. Transportation Research Record 1043, Transportation Research Board, Washington, DC.
[26] Thompson, M.R., Garg, N., 1999. Wheel Load Interaction: Critical Airport Pavement Responses. FAA COE Research Report No.7, University of Illinois at Urbana-Champaign.
[27] Thompson, M.R., Tutumluer, E., Bejarano, M., 1998. Granular Material and Soil Moduli Review of the Literature. FAA COE Research Report No.1, University of Illinois at Urbana-Champaign.
[28] Xu, B., Ranjithan, S.R., Kim, Y.R., 2001. Development of Relationships Between FWD Deflections and Asphalt Pavement Layer Condition Indicators. 81st Annual Meeting of the TRB, Washington, DC.
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