A genetic algorithm model coupled with artificial neural network model was developed to find the optimal values of upstream, downstream cutoff lengths, length of floor and length of downstream protection required for a hydraulic structure. These were obtained for a given maximum difference head, depth of impervious layer and degree of anisotropy. The objective function to be minimized was the cost function with relative cost coefficients for the different dimensions obtained. Constraints used were those that satisfy a factor of safety of 2 against uplift pressure failure and 3 against piping failure.
Different cases reaching 1200 were modeled and analyzed using geo-studio modeling, with different values of input variables. The soil wa

In the present work, the behavior of thick-walled cylinder of elasto-plastic material (polymeric material) has been studied analytically. The study is based on modified Von-Mises yield criterion (for non metallic material). The equations of stress distribution are obtained for the cylinder under general cases of elastic expansion, plastic initiation and elastic-plastic expansion.

        A computer program is developed for evaluating the stress distribution. The solution is carried out for worst boundary conditions when the cylinder is subjected to the combination of pressure load, inertia load, and temperature gradient.

        The results are presente

Abstract: Recently, there is increasing interest in using mode-division multipelexing (MDM) technique  to enhace  data rate transmission over multimode fibers.  In this technique, each fiber mode is treated as a separate optical carrier to transfer its own data.  This paper presents a broadband, compact, and low loss three-mode (de)multiplexer designed for C+L band using subwavelength grating (SWG) technology and built-in silicon-on-insulator SOI platform. SWG offers refractive index engineering for wider operating bandwidth and compact devices compared to conventional ones. The designed (de)multiplex deals with three modes (TE0, TE1, and TE2) and has a loss > -1 dB and crosstalk < −15 dB, and its operation c

Drilling fluid properties and formulation play a fundamental role in drilling operations. The Classical water-based muds prepared from only the Syrian clay and water without any additives((Organic and industrial polymers) are generally poor in performance. Moreover, The high quantity of Syrian clay (120 gr / l) used in preparing drilling fluids. It leads to a decrease in the drilling speed and thus an increase in the time required to complete the drilling of the well. As a result, the total cost of drilling the well increased, as a result of an increase in the concentration of the solid part in the drilling fluid. In this context, our study focuses on the investigation of the improvement in drilling mud   Prepa

               In this research, the semiparametric Bayesian method is compared with the classical  method to  estimate reliability function of three  systems :  k-out of-n system, series system, and parallel system. Each system consists of three components, the first one represents the composite parametric in which failure times distributed as exponential, whereas the second and the third components are nonparametric ones in which reliability estimations depend on Kernel method using two methods to estimate bandwidth parameter h method and Kaplan-Meier method. To indicate a better method for system reliability function estimation, it has be

Transmission lines are generally subjected to faults, so it is advantageous to determine these faults as quickly as possible. This study uses an Artificial Neural Network technique to locate a fault as soon as it happens on the Doukan-Erbil of 132kv double Transmission lines network. CYME 7.1-Programming/Simulink utilized simulation to model the suggested network. A multilayer perceptron feed-forward artificial neural network with a back propagation learning algorithm is used for the intelligence locator's training, testing, assessment, and validation. Voltages and currents were applied as inputs during the neural network's training. The pre-fault and post-fault values determined the scaled values. The neural network's p

   Reservoir characterization plays a crucial role in comprehending the distribution of formation properties and fluids within heterogeneous reservoirs. This knowledge is instrumental in constructing an accurate three-dimensional model of the reservoir, facilitating predictions regarding porosity, permeability, and fluid flow distribution. Among the various methods employed for reservoir characterization, the hydraulic flow unit stands out as a widely adopted approach. By effectively subdividing the reservoir into distinct zones, each characterized by unique petrophysical and geological properties, hydraulic flow units enable comprehensive reservoir analysis. The concept of the flow unit is closely tied to the flow zone indicator, a cr