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Silty-fine sand is widely distributed in the Yellow River floodplain and is frequently utilized in infrastructure construction. It is susceptible to engineering hazards due to its limited bearing capacity, inadequate stability, and high permeability under groundwater seepage. Grouting has emerged as an effective method to solve this problem, with the choice of grouting material playing a pivotal role. To address the urgent requirement for a suitable material to reinforce silty-fine sand, three types of traditional grouting materials are selected for comparison with the self-developed permeable polymer. The performance comparison of different grouting materials encompasses pre-grouting parameters including viscosity and gel time, as well as post-grouting contrasts of strength, water stability, and permeability coefficient of the reinforced silty-fine sand. The results indicate that the permeable polymer possesses low initial viscosity and optimal gel time, facilitating substantial injectability and adjustability. As the grouting pressure and curing period increase, the strength of the silty-fine sand reinforced with different materials demonstrates varying degrees of improvement, and specimens reinforced with the permeable polymer and high permeability epoxy resin display the highest strength at equivalent grouting pressure and curing periods. Over time, the strength of specimens reinforced with different materials exhibits a decline during immersion. Regarding water stability, the specimens reinforced with the permeable polymer show a minimal reduction of 10.87% after 7 days of immersion, exhibiting excellent water stability. Additionally, the permeability coefficients of the specimens decrease with longer curing periods, with the high permeability epoxy resin and permeable polymer exhibiting the lowest coefficients. By comprehensive comparison, the permeable polymer demonstrates the best overall performance and is suitably applicable for reinforcing silty-fine sand in the Yellow River floodplain. • A new self-developed permeable polymer was proposed by comparing with three kinds of traditional grouting materials. • The reinforcement effect of silty-fine sand with the self-developed permeable polymer were carried out. • The strength of silty-fine sand specimens reinforced with different materials ware compared. [ABSTRACT FROM AUTHOR]

• A set of visualized permeable grouting model test devices for sand layer was created. • Anti-seepage reinforcement mechanism of permeable polymer in sand layer is studied. • BP neural network prediction model for grouting effect is constructed. Permeation grouting is one of the important methods of anti-seepage reinforcement when the drainage pipes crosses loose diseases caused by leakage. With the development of grouting materials, permeable polymer slurry is widely used in engineering practice. Due to its low viscosity, fast reaction and micro-expansion, the diffusion and reinforcement law of slurry in loose area of drainage pipeline is more complex. To better understand the effects of water head pressure, grouting pressure, sand particle size and clay content on the anti-seepage and reinforcement effect of permeable polymer grouting in loose area of drainage pipeline, several types of tests, including permeability and uniaxial compressive strength tests, were performed. Then, based on the model test results, a BP neural network prediction model for the anti-seepage reinforcement effect of permeable polymer grouting in loose area of drainage pipeline is constructed, and the research results are applied to the treatment project of loose area of drainage pipeline for verification. The results show that: 1) after grouting, the order of magnitude of permeability coefficient of the consolidated body is reduced to 10-7cm/s, the anti-seepage performance of the sand layer is greatly improved, and the grouting pressure is the main controlling factor affecting the anti-seepage performance of the consolidated body. 2) The compressive strength of the consolidated body is obvious different under different working conditions. The water head pressure is the main controlling factor affecting the strength of the consolidated body. 3) Due to the limitation of test conditions, the relative error between the predicted value of BP neural network model and the actual value is about 20%, which can meet the general prediction needs. [ABSTRACT FROM AUTHOR]