Efficient surrogate method for predicting pavement response to various tire configurations

A computationally efficient surrogate model was developed based on artificial neural networks (ANN) to investigate the effect of the new generation of wide-base tires on pavement responses. Non-uniform tire contact stress measurements were obtained using a stress-in-motion instrument. The measured 3-D contact stresses were applied on two extreme 3-D flexible pavement finite element models representing low-volume (thin) and high-volume (thick) roads. Eleven critical pavement responses were modeled at two different material properties input levels—detailed and simplified—depending on data availability. The results rendered by the ANN surrogate models were highly accurate with average prediction error less than 5 % and R-square values higher than 0.95. In addition, two sensitivity analyses were performed to investigate the variables effect on pavement responses. It was found that the type of tire (wide-base vs. dual tire assembly) is more influential than the inflation pressure on pavement responses. However, the tire inflation pressure seemed to have a significant effect on near-surface responses. The developed models were incorporated into a tool to assist designers and engineers in investigating the effect of the pavement responses of wide-base versus dual tire assembly under typical loading conditions and pavement structures.

[1]  Hani S. Mitri,et al.  Three-Dimensional Dynamic Analysis of Flexible Conventional Pavement Foundation , 2005 .

[2]  Imad L. Al-Qadi,et al.  Impact Quantification of Wide-Base Tire Loading on Secondary Road Flexible Pavements , 2011 .

[3]  Imad L. Al-Qadi,et al.  Combined Effect of Moving Wheel Loading and Three-Dimensional Contact Stresses on Perpetual Pavement Responses , 2009 .

[4]  Imad L. Al-Qadi,et al.  Analytical Approach for Predicting Three-Dimensional Tire–Pavement Contact Load , 2014 .

[5]  Imad L. Al-Qadi,et al.  Effect of Wide-Base Tires on Nationwide Flexible Pavement Systems: Numerical Modeling , 2016 .

[6]  Imad L. Al-Qadi,et al.  Quantitative Assessment of the Effect of Wide-Base Tires on Pavement Response by Finite Element Analysis , 2016 .

[7]  Halil Ceylan,et al.  Neural Networks Applications in Pavement Engineering: A Recent Survey , 2014 .

[8]  Eleni I. Vlahogianni,et al.  Testing and Comparing Neural Network and Statistical Approaches for Predicting Transportation Time Series , 2013 .

[9]  Mojtaba Ziyadi,et al.  Prediction of accident severity using artificial neural networks , 2011 .

[10]  Kasthurirangan Gopalakrishnan,et al.  Effect of training algorithms on neural networks aided pavement diagnosis , 2010 .

[11]  Eleni I. Vlahogianni,et al.  Statistical methods versus neural networks in transportation research: Differences, similarities and some insights , 2011 .

[12]  Mehmet Saltan,et al.  Backcalculation of pavement layer thickness using data mining , 2012, Neural Computing and Applications.

[13]  Halil Ceylan,et al.  Computationally efficient surrogate response models for mechanistic–empirical pavement analysis and design , 2011 .

[14]  Imad L. Al-Qadi,et al.  Viscoelastic Modeling and Field Validation of Flexible Pavements , 2006 .

[15]  Mehmet Saltan,et al.  Artificial neural networks–based backcalculation of the structural properties of a typical flexible pavement , 2012, Neural Computing and Applications.

[16]  Imad L. Al-Qadi,et al.  Three-Dimensional Finite Element Modeling of Instrumented Airport Runway Pavement Responses , 2013 .

[17]  Yousef Shafahi,et al.  Two-Stage Support Vector Classifier and Recurrent Neural Network Predictor for Pavement Performance Modeling , 2013 .

[18]  Halil Ceylan,et al.  Advanced Approaches to Characterizing Nonlinear Pavement System Responses , 2007 .

[19]  Imad L. Al-Qadi,et al.  Dynamic Analysis and in Situ Validation of Perpetual Pavement Response to Vehicular Loading , 2008 .

[20]  Imad L. Al-Qadi,et al.  Full-depth Pavement Responses under Various Tire Configurations: Accelerated Pavement Testing and Finite Element Modeling , 2009 .

[21]  Munir D. Nazzal,et al.  Optimization of resilient modulus prediction from FWD results using artificial neural network , 2014 .

[22]  D. Hamby.  A comparison of sensitivity analysis techniques. , 1995, Health physics.

[23]  Imad L. Al-Qadi,et al.  The Truth and Myth of Fatigue Cracking Potential in Hot-Mix Asphalt: Numerical Analysis and Validation (With Discussion) , 2008 .

[24]  Imad L. Al-Qadi,et al.  Hyperelastic Modeling of Wide-Base Tire and Prediction of Its Contact Stresses , 2016 .

[25]  Sameh Zaghloul,et al.  USE OF A THREE-DIMENSIONAL, DYNAMIC FINITE ELEMENT PROGRAM FOR ANALYSIS OF FLEXIBLE PAVEMENT , 1993 .

[26]  Jaime A. Hernandez,et al.  Tire–pavement interaction modelling: hyperelastic tire and elastic pavement , 2017 .