A prediction model of permanent strain of unbound gravel materials based on performance of single-size gravels under repeated loads

Abstract

Permanent strain is an important consideration for the application of unbound gravel materials used in pavement under the repeated vehicle loads. The existing prediction models of permanent strain involved various influence factors, such as load condition, moisture content, compactness, etc. A new model to predict the permanent strain of unbound gravel mixture was proposed based on the performance of single-size gravels. In this study, the significant plastic stage was designated by the nonlinear and linear analyses during the long-term repeated loads tests. The results showed that the plastic strain had an obvious growth rate in the first 20,000 repetitions which was chosen as the significant plastic stage. Then, the plastic strain of unbound gravel materials was investigated and the calculation models of single-size gravels were established considering the load repetition, load intensity, load frequency, moisture and compactness. The contribution of coarse and fine aggregate in the gravel mixture was obtained based on the theory of maximum density and packing theory. After that, the prediction model of gravel mixture was established using the plastic strain of single-size gravels and the contribution of different aggregates. Compared with the measurements of six gravel mixtures, the proposed model was verified with high precision due to the small differences between the predictions and measurements. With the comparison of the previous models, the proposed model has greater accuracy than that of Wolff-Visser and Paute, and similar as the models of Barksdale and Sweere for the investigated materials. Using this model, the permanent strain can be obtained without additional laboratory tests once the gradation composition and working conditions were determined. This model can predict the permanent strain of the gravel mixture from the gradation composition, which will bring new meaningful findings and strong adaptability.