This paper presents an improved slope stability sensitivity analysis (ISSSA) model that takes anchoring factors into consideration in umbrella-anchored sand and clay slopes under reservoir water level fluctuation. The results of the ISSSA model show that the slope inclination and the layout density of anchors are the main controlling factors for sand slope stability under fluctuation of the water level, while the slope inclination and water head height are the main controlling factors for slope stability in the Cangjiang bridge—Yingpan slope of Yunnan province in China. Moreover, there is an optimum anchorage angle, in the range of 25–45 degrees, which has the greatest influence on slope stability. The fluctuation of the reservoir water level is an important factor that triggers slope instability; in particular, a sudden drop in the surface water level can easily lead to landslides; therefore, corresponding measures should be implemented in a timely manner in order to mitigate landslide disasters.
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Background In recent years, the technology of blocking and grouting of has been widely used in the treatment of groundwater inrush in tunnels in China’s mines. Studies have been carried out on the aggregates pouring under hydrodynamic conditions; however, there is lack of studies of the grout propagation in the pouring aggregate mass, which formed in the earlier stage. This paper presents an experimental investigation of grout propagation in poured aggregates with flowing water, which focused on the second stage, i.e., the grouting stage, after the first stage of aggregates pouring in the salvage of an inundated underground tunnel. Results In this work, a visualized tunnel replica with flowing water was adopted to investigate the main effects of sealing efficiency, water pressure variation, propagation mechanism during grouting in the poured aggregates with different grain sizes and flowing water conditions. A series of experiments with orthogonal array design were carried out, and the propagation of cement/sodium silicate and the change characteristics of fluid pressure in the tunnel were obtained. The results show that the main effects on the sealing efficiency of grouting in poured aggregates with flowing water in a descending order is cement/sodium silicate ratio, relative density of aggregates, particle size distribution of aggregates, and final water pressure difference of both ends of aggregates segment. The cementation form of grouted aggregates has four types; T shape, H shape, O shape and shell shape. The main mode of grout propagation includes three types; permeation filling mode, compaction- permeation mode, and compaction-fracturing mode, which reflects the influence of grain size, density of aggregates and grouting pressure. The variation of water pressure can be divided into four categories; overall ascending type, step ascending type, concave type, and convex type. The curve type mainly depends on water pressure, water flowrate, relative density and grain size of aggregates, gel time, propagation mechanism of grout. Conclusion The flow rate after grouting has decreased by approximately 60% to 86% compared to that before grouting, bulk hydraulic conductivity decreased by more than 80%, reflecting a great improvement of the sealing efficiency in the grouting stage after the pouring stage. The research on grout propagation in different graded aggregate mass has guiding significance for the design and construction of a rapid disaster treatment after groundwater inrush.
The determination of soil parameters in geotechnical engineering and their variations during the construction process have long been a focal point for engineering designers. While the artificial neural network (ANN) has been employed for back analysis of soil parameters, its application to caisson sinking processes remains limited. This study focuses on the Nanjing Longtan Yangtze River Bridge project, specifically the south anchoring of an ultra-large rectangular caisson. A comprehensive analysis of the sinking process was conducted using 400 finite element method (FEM) models to obtain the structural stress and earth pressure at key locations. Multiple combinations of soil parameters were considered, resulting in a diverse set of simulation results. These results were then utilized as training samples to develop a back-propagating artificial neural network (BP ANN), which utilized the structural stress and earth pressure as input sets and the soil parameters as output sets. The BP ANN was individually trained for each stage of the sinking process. Subsequently, the trained ANN was employed to predict the soil parameters under different working conditions based on actual monitoring data from engineering projects. The obtained soil parameter variations were further analyzed, leading to the following conclusions: (1) The soil parameters estimated by the ANN exhibited strong agreement with the original values from the geological survey report, validating their reliability; (2) The surrounding soil during the caisson sinking exhibited three distinct states: a stable state prior to the arrival of the cutting edges, a strengthened state upon the arrival of the cutting edges, and a disturbed state after the passage of the cutting edges; (3) In the stable state, the soil parameters closely resembled the original values, whereas in the strengthened state, the soil strength and stiffness significantly increased, while the Poisson’s ratio decreased. In the disturbed state, the soil strength and stiffness were slightly lower than the original values. This study represents a valuable exploration of back analysis for caisson engineering. The findings provide important insights for similar engineering design and construction projects.
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