With the rapid development of China’s e-commerce and logistics industry, a large number of waste express bags (WEBs) have been produced, which are difficult to recycle. The existing methods of waste express bag disposal often cause severe environmental pollution. It was discovered in this paper that the use of WEBs to modify bitumen could be an environmentally friendly way to recycle WEBs. This study aimed to investigate the feasibility of using WEBs to modify bitumen and promote the performance of WEB-modified bitumen. In order to verify this assumption, a series of basic or rheological experiments were conducted on different dosages of WEB-modified bitumen. The test results and phenomenon showed that the WEBs could be used to modify the binder homogeneously by using a high-speed shearing machine. The basic experiments showed that the WEBs could decrease the penetration and ductility while increasing the softening point and the rotational viscosity. Additionally, the rheological experimental data indicated that the high temperature performance was improved while the low temperature performance stayed the same. The Fourier-transform infrared spectroscopy (FTIR) results demonstrated that modification of the WEB was a physical modification without chemical reaction and the main component of the modifier was polyethylene. The fluorescence microscope (FM) data revealed the micro-structures of different dosages of WEB-modified bitumen. In conclusion, WEB can be a feasible binder modifier.
The correlations between pavement texture and tire pressure with the actual tire-road contact area were first investigated according to the tire-road static contact characteristics; on this basis, the influence mechanisms of speed and pavement texture on the pavement friction coefficient were systematically explored from the angle of tire-road coupling system dynamics via the self-developed dynamic testing system of tire-pavement friction. By integrating the above influence factors, the BP neural network method was applied to the regression of the prediction model for the asphalt pavement friction coefficient. Through the comparison between the model measured value and estimated value, their correlation coefficient R2 reached 0.73, indicating that this model is of satisfactory prediction accuracy and applicable to the antiskid design of asphalt pavement.
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