Neural Networks Modeling of Stress Growth in Asphalt Overlays due to Load and Thermal Effects during Reflection Cracking

Although several techniques have been introduced to reduce reflective cracking, one of the primary forms of distress in hot-mix asphalt (HMA) overlays of flexible and rigid pavements, the underlying mechanism and causes of reflective cracking are not yet well under- stood. Fracture mechanics is used to understand the stable and progressive crack growth that often occurs in engineering components under varying applied stress. The stress intensity factor (SIF) is its basis and describes the stress state at the crack tip. This can be used with the appropriate material properties to calculate the rate at which the crack will propagate in a linear elastic manner. Unfortunately, the SIF is difficult to compute or measure, particularly if the crack is situated in a complex three-dimensional (3D) geometry or subjected to a non- simple stress state. In this study, the neural networks (NN) methodology is successfully used to model the SIF as cracks grow upward through a HMA overlay as a result of both load and thermal effects with and without reinforcing interlayers. Nearly 100,000 runs of a finite-element program were conducted to calculate the SIFs at the tip of the reflection crack for a wide variety of crack lengths and pavement structures. The coefficient of determination (R 2 ) of all the developed NN models except one was above 0.99. Owing to the rapid prediction of SIFs using developed NN models, the overall computer run time for a 20-year reflection cracking prediction of a typical overlay was significantly reduced. DOI: 10.1061/(ASCE)MT.1943-5533.0000153. © 2011 American Society of Civil Engineers. CE Database subject headings: Cracking; Neural networks; Predictions; Models; Finite element method; Thermal factors. Author keywords: Reflective cracking; HMA overlay; Neural networks; Prediction model; Fracture mechanics; Finite element.

[1]  I. Al-Qadi,et al.  Development of an overlay design model for reflective cracking with and without steel reinforcing nettings , 2003 .

[2]  P. C. Paris,et al.  The Stress Analysis of Cracks Handbook, Third Edition , 2000 .

[3]  F. Samarotto.  David Neumeyer and Susan Tepping. A Guide to Schenkerian Analysis. Englewood Cliffs, NJ: Prentice-Hall, 1992 , 1993 .

[4]  C L Monismith,et al.  REFLECTION CRACKING: ANALYSES, LABORATORY STUDIES, AND DESIGN CONSIDERATIONS , 1980 .

[5]  Diyar Bozkurt,et al.  Three-Dimensional Finite Element Analysis to Evaluate Reflective Cracking Potential in Asphalt Concrete Overlays , 2002 .

[6]  Lefteri H. Tsoukalas,et al.  Fuzzy and neural approaches in engineering , 1997 .

[7]  Nii O. Attoh-Okine,et al.  Intelligent and Soft Computing in Infrastructure Systems Engineering: Recent Advances , 2009, Intelligent and Soft Computing in Infrastructure Systems Engineering.

[8]  J. Rice,et al.  Elementary engineering fracture mechanics , 1974 .

[9]  Gerardo W. Flintsch,et al.  Soft Computing Applications in Infrastructure Management , 2004 .

[10]  Robert L. Lytton,et al.  DEVELOPMENT AND VALIDATION OF PERFORMANCE PREDICTION MODELS AND SPECIFICATIONS FOR ASPHALT BINDERS AND PAVING MIXES , 1993 .

[11]  Eric W Brooks,et al.  Geosynthetic materials in reflective crack prevention. , 2007 .

[12]  Andreas Loizos,et al.  Verification of falling weight deflectometer backanalysis using a dynamic finite elements simulation , 2005 .

[13]  Hiroshi Tada,et al.  The stress analysis of cracks handbook , 2000 .

[14]  Prithvi S. Kandhal,et al.  Hot Mix Asphalt Materials, Mixture Design and Construction , 1996 .

[15]  Carl L Monismith,et al.  Asphalt Overlay Design Procedures , 1985 .

[16]  André Molenaar,et al.  CAPA: A MODERN TOOL FOR THE ANALYSIS AND DESIGN OF PAVEMENTS , 1993 .

[17]  Robert L. Lytton,et al.  GEOSYNTHETICS IN FLEXIBLE AND RIGID PAVEMENT OVERLAY SYSTEMS TO REDUCE REFLECTION CRACKING , 2002 .

[18]  H. Riedwyl.  Goodness of Fit , 1967 .

[19]  Randy C. Ahlrich.  Evaluation of Asphalt Rubber and Engineering Fabrics as Pavement Interlayers. , 1986 .

[20]  Edward T. Harrigan,et al.  NAtioNAl CooperAtive HigHwAy reseArCH progrAm , 2013 .

[21]  Yang H. Huang,et al.  Pavement Analysis and Design , 1997 .

[22]  Robert L. Lytton,et al.  Prediction of Reflection Cracking in Hot-Mix Asphalt Overlays , 2010 .

[23]  Mark Dougherty,et al.  A REVIEW OF NEURAL NETWORKS APPLIED TO TRANSPORT , 1995 .

[24]  P. C. Paris,et al.  A Critical Analysis of Crack Propagation Laws , 1963 .

[25]  J W Button,et al.  EVALUATION OF FABRICS, FIBERS AND GRIDS IN OVERLAYS. SIXTH INTERNATIONAL CONFERENCE, STRUCTURAL DESIGN OF ASPHALT PAVEMENTS, VOLUME I, PROCEEDINGS, UNIVERSITY OF MICHIGAN, JULY 13-17, 1987, ANN ARBOR, MICHIGAN , 1987 .

[26]  Hojjat Adeli,et al.  Neural Networks in Civil Engineering: 1989–2000 , 2001 .

[27]  Jiwon Kim,et al.  Analysis of Reflective Crack Control System Involving Reinforcing Grid over Base-Isolating Interlayer Mixture , 2002 .

[28]  E R Brown,et al.  HOT MIX ASPHALT MATERIALS, MIXTURE DESIGN AND CONSTRUCTION. SECOND EDITION , 1996 .

[29]  R. Lytton.  Use of geotextiles for reinforcement and strain relief in asphalt concrete , 1989 .

[30]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .