Effect of Location and Angle of Cutoff Wall on Uplift Pressure in Diversion Dam

Some facility for the prevention of piping, reducing exit gradient and seepage amount under hydraulic structures, is construction of cutoff wall and drain. Therefore, this study compares the efficiency of cutoff wall on some design parameters in an assumed diversion dam cross-section. For this purpose, different placements of cutoff wall with various angle of inclination were used in the dam foundation. Results of this study showed that minimum uplift pressure happens when cut off wall is in the heel (upstream) of the dam. With fixing of longitudinal cut off wall placement, inclination of cutoff wall respect to the vertical position, results in reducing of uplift pressure. Effect of inclination of cutoff wall in upstream of the dam; respect to vertical position, in reducing of uplift pressure is very high.     

Trial Embankment Analysis to Predict Smear Zone Characteristics Induced by Prefabricated Vertical Drain Installation

In this study, FLAC finite difference software has been adopted to simulate the performance of the ground improved using prefabricated vertical drains assisted preloading, considering smear zone characteristics. The numerical code has been applied to predict smear zone properties employing a back calculation procedure using the results of several case studies. The construction of a trial embankment is proposed as a reliable method to predict the smear zone characteristics. The proposed back calculation method is applied to estimate the minimum required degree of consolidation and consequently the minimum required preloading time, resulting in a reliable estimation of the smear zone permeability and extent. Three preloading case studies considering both conventional preloading and vacuum assisted preloading have been simulated to verify the numerical code and to conduct the parametric study using the back calculation procedure. According to the results, the properties of the smear zone can be back-calculated reliably, when at least 33 % degree of consolidation due to trial embankment construction is achieved.     

Adaptive Conditioning of Multiple-Point Statistical Facies Simulation to Flow Data with Probability Maps

Multiple-point statistics (MPS) provides a flexible grid-based approach for simulating complex geologic patterns that contain high-order statistical information represented by a conceptual prior geologic model known as a training image (TI). While MPS is quite powerful for describing complex geologic facies connectivity, conditioning the simulation results on flow measurements that have a nonlinear and complex relation with the facies distribution is quite challenging. Here, an adaptive flow-conditioning method is proposed that uses a flow-data feedback mechanism to simulate facies models from a prior TI. The adaptive conditioning is implemented as a stochastic optimization algorithm that involves an initial exploration stage to find the promising regions of the search space, followed by a more focused search of the identified regions in the second stage. To guide the search strategy, a facies probability map that summarizes the common features of the accepted models in previous iterations is constructed to provide conditioning information about facies occurrence in each grid block. The constructed facies probability map is then incorporated as soft data into the single normal equation simulation (snesim) algorithm to generate a new candidate solution for the next iteration. As the optimization iterations progress, the initial facies probability map is gradually updated using the most recently accepted iterate. This conditioning process can be interpreted as a stochastic optimization algorithm with memory where the new models are proposed based on the history of the successful past iterations. The application of this adaptive conditioning approach is extended to the case where multiple training images are proposed as alternative geologic scenarios. The advantages and limitations of the proposed adaptive conditioning scheme are discussed and numerical experiments from fluvial channel formations are used to compare its performance with non-adaptive conditioning techniques.