Mechanics based model for predicting structure-induced rolling resistance (SRR) of the tire-pavement system

The structure-induced rolling resistance of pavements, and its impact on vehicle fuel consumption, is investigated in this study. The structural response of pavement causes additional rolling resistance and fuel consumption of vehicles through deformation of pavement and various dissipation mechanisms associated with inelastic material properties and damping. Accurate and computationally efficient models are required to capture these mechanisms and obtain realistic estimates of changes in vehicle fuel consumption. Two mechanistic-based approaches are currently used to calculate vehicle fuel consumption as related to structural rolling resistance: dissipation-induced and deflection-induced methods. The deflection-induced approach is adopted in this study, and realistic representation of pavement–vehicle interactions (PVIs) is incorporated. In addition to considering viscoelastic behavior of asphalt concrete layers, the realistic representation of PVIs in this study includes non-uniform three-dimensional tire contact stresses and dynamic analysis in pavement simulations. The effects of analysis type, tire contact stresses, pavement viscoelastic properties, pavement damping coefficients, vehicle speed, and pavement temperature are then investigated.     

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.

     

Use of artificial neural networks to determine parameters controlling the nanofibers diameter in electrospinning of nylon‐6,6

This study aimed to find out the primary factors influencing the diameter of electrospun nanofibers of nylon‐6,6 using artificial neural networks (ANNs). Four variables, namely, polymer concentration, working distance, injection rate, and applied voltage were considered as input parameters and the nanofibers diameter measured by scanning electron microscopy was taken as the output. The data were modeled and validated against a set of unseen data. The generated model was used to study the interactions occurring between the input variables and their effect on the diameter. Results show that the injection rate and the polymer concentration are major factors affecting the nanofibers diameter with inverse and direct relations with the diameter, respectively, while the working distance and the applied voltage have direct but minor effects on nanofibers diameter. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci 124:1589–1597, 2011