Search Results (2)

Based on a real engineering case, this study employs the MIDAS finite element software to model the reinforced high embankment slope using anti-sliding piles. The accuracy of the finite element method is verified by comparing calculated outcomes with field monitoring data. Expanding on this foundation, an analysis of factors influencing the reinforced high embankment slope is undertaken to scrutinize the impact of diverse elements on the slope and ascertain the optimal reinforcement strategy. The results reveal the following: The principal displacement observed in the high embankment slope is a vertical settlement, which escalates with the backfill height. Notably, the highest settlement does not manifest at the summit of the initial slope; instead, it emerges close to the summits of the subsequent two slopes. However, the maximum horizontal displacement at the slope’s zenith diminishes as the fill height increases—a trend that aligns with both field observations and finite element computations. The examination of the influence of anti-sliding pile reinforcement on the high embankment slope unveils that factors like the length, diameter, spacing, and positioning of the anti-sliding piles exert minor impacts on vertical settlement, while variations in the parameters of the anti-sliding piles significantly affect the slope’s horizontal displacement. When using anti-sliding piles to reinforce multi-level high embankment slopes, factoring in the extent of horizontal displacement variation and potential cost savings, the optimal parameters for the anti-sliding piles are a length of 15 m, a diameter of 1.5 m, and a spacing of 2.5 m, presenting the most effective combination to ensure superior slope stability and support. Full article

The cause mechanism of collapse disasters is complex and there are many influencing factors. Convolutional Neural Network (CNN) has a strong feature extraction ability, which can better simulate the formation of collapse disasters and accurately predict them. Taihe town’s collapse threatens roads, buildings, and people. In this paper, road distance, water distance, normalized vegetation index, platform curvature, profile curvature, slope, slope direction, and geological data are used as input variables. This paper generates collapse susceptibility zoning maps based on the information value method (IV) and CNN, respectively. The results show that the accuracy of the susceptibility assessment of the IV method and the CNN method is 85.1% and 87.4%, and the accuracy of the susceptibility assessment based on the CNN method is higher. The research results can provide some reference for the formulation of disaster prevention and control strategies. Full article