Excessive carbon emissions seriously threaten the sustainable development of society and the environment and have attracted the attention of the international community. The Yellow River Basin is an important ecological barrier and economic development zone in China. Studying the influencing factors of carbon emissions in the Yellow River Basin is of great significance to help China achieve carbon peaking. In this study, quadratic assignment procedure regression analysis was used to analyze the factors influencing carbon emissions in the Yellow River Basin from the perspective of regional differences. Accurate carbon emission prediction models can guide the formulation of emission reduction policies. We propose a machine learning prediction model, namely, the long short-term memory network optimized by the sparrow search algorithm, and apply it to carbon emission prediction in the Yellow River Basin. The results show an increasing trend in carbon emissions in the Yellow River Basin, with significant inter-provincial differences. The carbon emission intensity of the Yellow River Basin decreased from 5.187 t/10,000 RMB in 2000 to 1.672 t/10,000 RMB in 2019, showing a gradually decreasing trend. The carbon emissions of Qinghai are less than one-tenth of those in Shandong, the highest carbon emitter. The main factor contributing to carbon emissions in the Yellow River Basin from 2000 to 2010 was GDP per capita; after 2010, the main factor was population. Compared to the single long short-term memory network, the mean absolute percentage error of the proposed model is reduced by 44.38%.
The fractal theory has been applied to understand the transport law for the non-Newton Herschel–Bulkley (H-B) fluid grouting in fractal soil consisting of a bunch of arbitrary cross-sectional tortuous capillaries. The fractal models for the average flow velocity, starting pressure gradient and effective permeability are the functions of the cross-sectional geometric factors of capillaries, minimum and maximum cross-sectional area of capillaries, porosity, fractal dimensions and characteristic parameters of H-B fluid. The proposed models are verified by comparing with the available models, and then the effects of cross-sectional shapes of capillaries and the characteristic parameters of H-B fluid on the average flow velocity, starting pressure gradient and effective permeability are also analyzed. The results show that the present models relate the characteristics of H-B fluid to the microstructure of soil, and both are the most important factors in determining the grouting effect.
Multiple soil layers may be exposed simultaneously on the excavated surface of a large-diameter slurry shield. To study the formation and characteristics of mud filtration cake on the excavation surface during large-diameter slurry shield tunneling, penetration tests of mud slurries in different soils were carried out using a self-made device, and the microstructures of different mud filtration cakes were observed using scanning electron microscopy. The test results showed that there were three categories of filling forms for mud slurries permeating the soils: mud filtration cake, mud cake + permeation zone, and permeation zone; correspondingly, there were three types of filtration loss, which was mainly affected by the specific gravity of mud slurry. Finally, the porosity and the fractal dimension for the pore area of the mud filtration cake were calculated, and it is found that the fractal dimension of pore area is beneficial to classify the type of mud filtration cake.
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