Characterization of transient groundwater flow through a high arch dam foundation during reservoir impounding

AbstractEven though a large number of large-scale arch dams with height larger than 200 m have been built in the world, the transient groundwater flow behaviors and the seepage control effects in the dam foundations under difficult geological conditions are rarely reported. This paper presents a case study on the transient groundwater flow behaviors in the rock foundation of Jinping I double-curvature arch dam, the world's highest dam of this type to date that has been completed. Taking into account the geological settings at the site, an inverse modeling technique utilizing the time series measurements of both hydraulic head and discharge was adopted to back-calculate the permeability of the foundation rocks, which effectively improves the uniqueness and reliability of the inverse modeling results. The transient seepage flow in the dam foundation during the reservoir impounding was then modeled with a parabolic variational inequality (PVI) method. The distribution of pore water pressure, the amount of leakage, and the performance of the seepage control system in the dam foundation during the entire impounding process were finally illustrated with the numerical results

Subset simulation-based random finite element method for slope reliability analysis and risk assessment

Spatial variability is one of the most significant uncertainties in soil properties that affect the reliability of slope stability. It can be incorporated into slope reliability analysis and risk assessment through random finite element method (RFEM) in a rigorous manner. The great potential of RFEM in reliability analysis and risk assessment of soil slopes has been demonstrated in previous studies. Nevertheless, it often suffers from a common criticism of requiring extensive computational efforts and a lack of efficiency, particularly at small probability levels. This study develops an efficient RFEM that integrates RFEM with an advanced Monte Carlo Simulation method called “Subset Simulation (SS)”. By this means, the computational efficiency of calculating the failure probability and risk is significantly improved. This enhances the applications of RFEM in slope reliability analysis and risk assessment at small probability levels. In addition, the proposed SS-based RFEM also provides insights into the relative contributions of slope failure risk at different probability levels to the overall risk. Finally, the proposed approach is illustrated through a soil slope example. It is shown that the slope failure probability and risk can be evaluated properly using SS-based RFEM.Non UBCUnreviewedThis collection contains the proceedings of ICASP12, the 12th International Conference on Applications of Statistics and Probability in Civil Engineering held in Vancouver, Canada on July 12-15, 2015. Abstracts were peer-reviewed and authors of accepted abstracts were invited to submit full papers. Also full papers were peer reviewed. The editor for this collection is Professor Terje Haukaas, Department of Civil Engineering, UBC Vancouver.FacultyResearche

Numerical Simulation of Hydraulic Characteristics in A Vortex Drop Shaft

A new type of vortex drop shaft without ventilation holes is proposed to resolve the problems associated with insufficient aeration, negative pressure (Unless otherwise specified, the pressure in this text is gauge pressure and time-averaged pressure) on the shaft wall and cavitation erosion. The height of the intake tunnel is adjusted to facilitate aeration and convert the water in the intake tunnel to a non-pressurized flow. The hydraulic characteristics, including the velocity (Unless otherwise specified, the velocity in this text is time-averaged velocity), pressure and aeration concentration, are investigated through model experiment and numerical simulation. The results revealed that the RNG k-ε turbulence model can effectively simulate the flow characteristics of the vortex drop shaft. By changing the inflow conditions, water flowed into the vertical shaft through the intake tunnel with a large amount of air to form a stable mixing cavity. Frictional shearing along the vertical shaft wall and the collisions of rotating water molecules caused the turbulence of the flow to increase; the aeration concentration was sufficient, and the energy dissipation effect was excellent. The cavitation number indicated that the possibility of cavitation erosion was small. The results of this study provide a reference for the analysis of similar spillways

Copula-based approaches for evaluating slope reliability under incomplete probability information

10.1016/j.strusafe.2014.09.007Structural Safety52PA90-9

A Novel Numerical Model for Fluid Flow in 3D Fractured Porous Media Based on an Equivalent Matrix-Fracture Network

An original 3D numerical approach for fluid flow in fractured porous media is proposed. The whole research domain is discretized by the Delaunay tetrahedron based on the concept of node saturation. Tetrahedral blocks are impermeable, and fluid only flows through the interconnected interfaces between blocks. Fractures and the porous matrix are replaced by the triangular interface network, which is the so-called equivalent matrix-fracture network (EMFN). In this way, the three-dimensional seepage problem becomes a two-dimensional problem. The finite element method is used to solve the steady-state flow problem. The big finding is that the ratio of the macroconductivity of the whole interface network to the local conductivity of an interface is linearly related to the cubic root of the number of nodes used for mesh generation. A formula is presented to describe this relationship. With this formula, we can make sure that the EMFN produces the same macroscopic hydraulic conductivity as the intact rock. The approach is applied in a series of numerical tests to demonstrate its efficiency. Effects of the hydraulic aperture of fracture and connectivity of the fracture network on the effective hydraulic conductivity of fractured rock masses are systematically investigated

A new classification of seepage control mechanisms in geotechnical engineering

AbstractSeepage flow through soils, rocks and geotechnical structures has a great influence on their stabilities and performances, and seepage control is a critical technological issue in engineering practices. The physical mechanisms associated with various engineering measures for seepage control are investigated from a new perspective within the framework of continuum mechanics; and an equation-based classification of seepage control mechanisms is proposed according to their roles in the mathematical models for seepage flow, including control mechanisms by coupled processes, initial states, boundary conditions and hydraulic properties. The effects of each mechanism on seepage control are illustrated with examples in hydroelectric engineering and radioactive waste disposal, and hence the reasonability of classification is demonstrated. Advice on performance assessment and optimization design of the seepage control systems in geotechnical engineering is provided, and the suggested procedure would serve as a useful guidance for cost-effective control of seepage flow in various engineering practices

Landslide susceptibility prediction using slope unit-based machine learning models considering the heterogeneity of conditioning factors

To perform landslide susceptibility prediction (LSP), it is important to select appropriate mapping unit and landslide-related conditioning factors. The efficient and automatic multi-scale segmentation (MSS) method proposed by the authors promotes the application of slope units. However, LSP modeling based on these slope units has not been performed. Moreover, the heterogeneity of conditioning factors in slope units is neglected, leading to incomplete input variables of LSP modeling. In this study, the slope units extracted by the MSS method are used to construct LSP modeling, and the heterogeneity of conditioning factors is represented by the internal variations of conditioning factors within slope unit using the descriptive statistics features of mean, standard deviation and range. Thus, slope units-based machine learning models considering internal variations of conditioning factors (variant slope-machine learning) are proposed. The Chongyi County is selected as the case study and is divided into 53,055 slope units. Fifteen original slope unit-based conditioning factors are expanded to 38 slope unit-based conditioning factors through considering their internal variations. Random forest (RF) and multi-layer perceptron (MLP) machine learning models are used to construct variant Slope-RF and Slope-MLP models. Meanwhile, the Slope-RF and Slope-MLP models without considering the internal variations of conditioning factors, and conventional grid units-based machine learning (Grid-RF and MLP) models are built for comparisons through the LSP performance assessments. Results show that the variant Slope-machine learning models have higher LSP performances than Slope-machine learning models; LSP results of variant Slope-machine learning models have stronger directivity and practical application than Grid-machine learning models. It is concluded that slope units extracted by MSS method can be appropriate for LSP modeling, and the heterogeneity of conditioning factors within slope units can more comprehensively reflect the relationships between conditioning factors and landslides. The research results have important reference significance for land use and landslide prevention