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Sand layer sampling is important in water conservancy and hydropower exploration, and the accurate identification of sand layers is the premise for foundation design and treatment. In this study, a new method for identifying sand layers based on key drilling parameters was proposed. Simulated formation laboratory experiments using a self-developed measurement-while-drilling system were first conducted to study the relationships between four drilling parameters and sand layers. Quantitative criteria based on key drilling parameters for sand layer identification were then proposed, followed by validation through three engineering projects. The results showed that bit pressure and drilling speed are sensitive to formation changes and can be used for sand layer identification. Specifically, in sand layers, the variation ratio of drilling speed is more than 150% and the variation ratio of bit pressure is less than 200%. Applications of the proposed method in Na water conservancy project, Sancha water conservancy project, and Tuodan reservoir project show satisfying accuracy, indicating that it can be used as an effective tool for sand layer identification in various engineering projects. [ABSTRACT FROM AUTHOR]

Variations of reservoir water level and seasonal precipitation have reactivated or accelerated many reservoir landslides in the Three Gorges Reservoir area since impoundment in 2003. Updated daily monitoring data since 2017 reveals details about the step-like pattern of annual movement of the Baijiabao landslide, a large creeping landslide with a maximal cumulative surface displacement of 0.2 m over this 2-year period. The spatiotemporal deformation characteristics show that mass movement was greatest in 2017 and boundary cracks exhibit more frequent steps and are more sensitive to hydraulic factors than surface displacement. Acceleration periods are triggered when the reservoir water level falls below 153 m above mean sea level, with most annual movement occurring before the reservoir rises back to that critical level. The rate of daily surface movement is controlled by the daily variation of reservoir water level and by cumulative rainfall during the previous 1 to 7 days. The movement responses vary from different years and movement periods, but the one-factor critical-level model can effectively predict surface movement. The correlation between surface displacement rate and reservoir water level also comprehensively indicates the association between landslide movement and hydraulic factors including rainfall and variation of reservoir water level. Low reservoir level and its drawdown, and heavy or continuous rainfall, increase hydraulic gradients and change stress conditions, which destabilize the Baijiabao landslide and accelerate its surface movement. [ABSTRACT FROM AUTHOR]

Stabilizing piles are commonly used to effectively reinforce landslides and unstable slopes. However, the reinforcement mechanism of stabilizing piles, especially portal double-row (PDR) piles, remains poorly understood. The deformation characteristics and stabilizing mechanism of PDR piles were examined through physical model tests and numerical modeling by considering various embedded depths and interbedded weak and hard bedrock combinations. First, physical model tests were performed to study the effects of interbedded bedrock, orthogonal joints in the upper hard stratum, and embedded depths on the stabilizing mechanism. Second, six groups of numerical models based on the physical models were developed using the distinct element method. The results show that the bending moment and horizontal displacement of PDR piles increase in the presence of orthogonal joints and decrease with increasing embedded depth. The effects of crossbeam and hard bedrock percentage on the PDR pile deformation mechanism were then assessed. The results show that the crossbeam significantly affects the stabilizing PDR pile mechanism, while the effect of hard bedrock percentage is relevant to the embedded depth of PDR piles. The study provides some theoretical information for landslide prevention in areas with interbedded weak and hard bedrock. [ABSTRACT FROM AUTHOR]

The Green-Ampt (GA) model is one of the most widely used analytical methods of slope stability under rainfall. However, it may overestimate the soil water content above the wetting front. In this study, a novel approach to evaluate the time-dependent slope stability during intense rainfall based on a modified GA model is presented, and is known as the stratified Green-Ampt (SGA) model. By considering the stratified soil water content above the wetting front, the soil above the wetting front can be divided into saturated and transitional layers, and the SGA model is used to analyze the infiltration process of intense rainfall into slopes. Thereafter, safety factors (Fs) of infinite and finite slopes are derived using the SGA model. In the analysis of an infinite slope, the conventional limit equilibrium method is adopted to calculate the safety factor; as for a finite slope, the residual thrust method is introduced to obtain the safety factor with sliding mass divided into multiple soil slices. The performance of the SGA model is illustrated in two cases: an infinite slope and the Majiagou landslide as a finite slope. The results indicate that compared to the GA model, the calculated wetting front based upon the SGA model moves faster, and the wetting front depth shows a positive correlation with the slope surface angle and rainfall intensity. The evolution of the safety factor above the sliding surface can be divided into three phases, while the evolution of the safety factor above the wetting front can be divided into two phases. The critical time of the slope reaching a less stable state (safety factor is 1.05) or unstable state (safety factor is 1.00) decreases exponentially with an increase in rainfall intensity. In addition, the rainfall has a significant influence on the design of stabilizing piles for the Majiagou landslide. The presented SGA model appears to be accurate to investigate slope stability during intense rainfall events. [ABSTRACT FROM AUTHOR]