Backcalculation of full-depth asphalt pavement layer moduli considering nonlinear stress-dependent subgrade behavior

This paper describes the use of artificial neural networks (ANNs) as pavement structural analysis tools for the rapid and accurate prediction of critical responses and deflection profiles of full-depth flexible pavements subjected to typical highway loadings. The ILLI-PAVE finite element program, extensively tested and validated for over three decades, was used as an advanced structural model for solving the critical responses of full-depth flexible pavements. Unlike the linear elastic layered theory commonly used in pavement layer backcalculation, nonlinear stress-dependent subgrade soil response models were used in the ILLI-PAVE program to account for the softening nature of fine-grained subgrade soils under increasing stress states. ANN models then trained with the results from the ILLI-PAVE solutions have been found to be viable alternatives. The trained forward calculation ANN models were capable of predicting asphalt tensile strains and subgrade compressive stresses/strains with low average absolute errors of those obtained directly from the ILLI-PAVE analyses. ANN backcalculation models developed in this study were also capable of successfully predicting the pavement layer moduli from the falling weight deflectometer (FWD) deflection basins and they may be used in the field for rapidly assessing the condition of pavement sections during the FWD testing.

[1]  Jeffery Raphael Roesler,et al.  DIPLOBACK: Neural-Network-Based Backcalculation Program for Composite Pavements , 1997 .

[2]  Marshall R Thompson,et al.  ILLI-PAVE-BASED RESPONSE ALGORITHMS FOR FULL-DEPTH ASPHALT CONCRETE FLEXIBLE PAVEMENTS , 1986 .

[3]  M R Thompson,et al.  ILLI-PAVE based conventional flexible pavement design procedure , 1992 .

[4]  Y. Richard Kim,et al.  Prediction of Layer Moduli from Falling Weight Deflectometer and Surface Wave Measurements Using Artificial Neural Network , 1998 .

[5]  Lutfi Raad,et al.  LOAD RESPONSE OF TRANSPORTATION SUPPORT SYSTEMS , 1980 .

[6]  D. Signorini,et al.  Neural networks , 1995, The Lancet.

[7]  J M Duncan,et al.  FINITE ELEMENT ANALYSES OF PAVEMENTS , 1968 .

[8]  Halil Ceylan.  Analysis and Design of Concrete Pavement Systems Using Artificial Neural Networks , 2002 .

[9]  M R Thompson.  ILLI-PAVE BASE FULL-DEPTH ASPHALT CONCRETE PAVEMENT DESIGN PROCEDURE. SIXTH INTERNATIONAL CONFERENCE, STRUCTURAL DESIGN OF ASPHALT PAVEMENTS, VOLUME I, PROCEEDINGS, UNIVERSITY OF MICHIGAN, JULY 13-17, 1987, ANN ARBOR, MICHIGAN , 1987 .

[10]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[11]  Don R. Alexander,et al.  Using Artificial Neural Networks as a Forward Approach to Backcalculation , 1997 .

[12]  Q. L. Robnett,et al.  Resilient Properties of Subgrade Soils , 1973 .

[13]  Roger W. Meier,et al.  BACKCALCULATION OF FLEXIBLE PAVEMENT MODULI FROM DYNAMIC DEFLECTION BASINS USING ARTIFICIAL NEURAL NETWORKS , 1995 .

[14]  M Thompson,et al.  ILLI-PAVE BASED FLEXIBLE PAVEMENT DESIGN CONCEPTS FOR MULTIPLE WHEEL HEAVY GEAR LOAD AIRCRAFT , 2002 .

[15]  Marshall R. Thompson.  ILLI-PAVE Based NDT Analysis Procedures , 1989 .

[16]  Marshall R Thompson,et al.  ILLI-PAVE-BASED RESPONSE ALGORITHMS FOR DESIGN OF CONVENTIONAL FLEXIBLE PAVEMENTS , 1985 .

[17]  S. Brown,et al.  ANALYSIS OF PAVEMENTS WITH GRANULAR BASES , 1981 .

[18]  Nenad Gucunski,et al.  Backcalculation of Pavement Profiles from Spectral-Analysis-of-Surface-Waves Test by Neural Networks Using Individual Receiver Spacing Approach , 1996 .

[19]  G Y Baladi,et al.  Nondestructive Testing of Pavements and Backcalculation of Moduli , 1989 .

[20]  J. Nazuno.  Haykin, Simon. Neural networks: A comprehensive foundation, Prentice Hall, Inc. Segunda Edición, 1999 , 2000 .