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Effect of Stress-Dependent Modulus and Poisson's Ratio on Structural Responses in Thin Asphalt Pavements
Most low-volume roads are primarily thin flexible pavements with an unbound base and subgrades. This is especially true for the behavior of unbound pavement materials, which is very nonlinear and stress dependent, even at low traffic stresses. A need therefore exists for more realistic prediction of...
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Published in: | Journal of transportation engineering 2004-05, Vol.130 (3), p.387-394 |
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Main Authors: | , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Most low-volume roads are primarily thin flexible pavements with an unbound base and subgrades. This is especially true for the behavior of unbound pavement materials, which is very nonlinear and stress dependent, even at low traffic stresses. A need therefore exists for more realistic prediction of pavement response for such pavements, based on proper constitutive models and computational methods. For this reason, the nonlinear, stress-dependent finite-element program for pavement analysis was developed. Stress-dependent models for the resilient modulus and Poisson's ratio of unbound pavement materials are incorporated into the finite-element model to predict the resilient behavior within the pavement layers under specified wheel loads. The results of this study show that the developed finite-element model with stress dependency is suitable for calculating a reduced horizontal tension in the bottom half of unbound aggregate base layers. Unlike conventional methods for correcting horizontal tension, compressive stresses can be obtained only by the use of proper constitutive material models and the finite-element approach. It is also noted that the effects of nonlinearity and the varying stress-dependent modulus and Poisson's ratio, especially in the base layers, could be substantial, and the proper selection of material properties is very important to improve the prediction of those behaviors. |
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ISSN: | 0733-947X 1943-5436 |
DOI: | 10.1061/(ASCE)0733-947X(2004)130:3(387) |