Cold oxygen plasma was employed to give hydrophilicity modification to polypropylene (PP) nonwoven fabric (NWF). It was found that, after plasma treatment, PP NWF made from fibers with smooth surfaces can only keep its hydrophilicity for a short time and then shows a quick hydrophobic recovery at room temperature. However, this hydrophilic property can last for a long time in the case of the PP NWF made from fibers with rough surfaces. To prove the contribution of the rough surface to the long-term hydrophilicity, this PP NWF was treated in an organic solvent to smooth the fiber surface. The hydrophilic feature of this PP NWF no longer lasts for a long time after the same plasma treatment. This observation strongly supports our opinion that the fiber surface morphology of PP NWF is a critical factor for long-term hydrophilicity improvement after plasma treatment, which gives a positive solution to overcoming the aging effect of hydrophilicity modification often found in this technique.
Backfill mining is the most environmentally friendly mining method at present, which can effectively control the surface subsidence, improve the recovery rate, and has good social and economic benefits. The purpose of this study is to solve the environmental problems caused by solid waste, combined with the rich geographical advantages of aeolian sand in the Yushenfu mining area of China. The rheological properties of the aeolian sand-fly ash-based filling slurry with different fly ash content are studied by experiments, and the strength development law of the filling body under different age and fly ash content are studied from the macroscopic and microscopic points of view. The rheological experiments showed that the increase of the amount of fly ash has a significant effect on the thixotropy, plastic viscosity, and yield stress of the filling slurry. Additionally, rheological properties of aeolian sand-fly ash-based filling slurry conform to the Bingham model. With the increase of the amount of fly ash, the performance of the filling slurry has been significantly improved. Uniaxial test and scanning electron microscope observation showed that the influence of fly ash on the strength of the filling body was mainly reflected in the late stage of maintenance, but was not obvious in the middle stage. Fly ash particles mainly bear the role of “water reduction” and a physical filling effect, which makes the filling slurry thicker and the internal structure more closely spaced. The volcanic ash reaction of fly ash is lagging behind the hydration reaction of cement; the secondary product of the delayed reaction is filled in the pores of cement hydrates, which can greatly reduce the porosity of the backfill body and increase the later strength of the backfill body. It provides a guarantee for the safe replacement of coal pillars in the working face.
The Grey system theory is a new mathematical method to predict data changes in the poor data integrity. As a branch of Grey system theory, the GM (1, 1) model is widely used because only small sample data and simple calculations are needed in prediction of engineering project. It is a critical problem to effectively predict the performance and corrosion of asphalt pavement of highway construction due to the inadequacy of highway performance monitoring data. The smoothness, rut, and pavement skid resistance are three important indexes to evaluate the performance and corrosion of asphalt pavement. This paper has established the prediction model and derived prediction equation of asphalt pavement performance according to the GM (1, 1) model method and then listed the calculation equation of residual and the gray absolute correlation degree. Based on the experience of constructed Dalian-Guangzhou expressway in China, the vectors “a” and “b” in the prediction equation of smoothness, rut, and pavement skid resistance have been calculated by using the original monitoring data. The field monitoring data are compared with the predictive data for residual and the gray absolute correlation. The results reveal that the predicted data of the smoothness, rut, and skid resistance are mostly consistent with the monitoring data, the biggest residual of the above three indexes is smaller than 8.09%, and the gray absolute correlation degrees all exceed 0.9, which means the accuracy of the predicted equation is excellent. The calculation method based on GM (1, 1) model can effectively predict the changing performance index of asphalt pavement.
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