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Optimization of Clay Core Dimensions in Earth Fill Dams Using Particle Swarm Algorithm Original Article, B31 Roshani E, Farsadizadeh D. Journal. Civil Eng. Urban. 2(5): 176-181. 2012. ABSTRACT: The flow rate from earth fill dam body mainly depends on the dimensions and characteristics of the clay core. Thus, finding the optimized dimensions for clay core in non-homogeneous earth fill dam is essential. In order to decrease water loss, the clay core should be expanded this leads to dramatic increment in construction volume and costs. The novelty of this issue could be expressed as an optimization material in which the total cost of lost water and earthwork should be minimized. The method serves us a simpler and an accurate solution for an earth fill dam to be in optimised dimensions. The objective function is consisting of two parts. The first part is to calculate the water seepage volume through the dam core with a combination of finite element method and artificial neural network (ANN) and the second part encounters the costs regarding to the volume of earth works. Finally the best answer from the economical view will be chosen according to the amount of seepage and core volume. Since calculating water seepage through soil using finite element method is time consuming, first a combination of 600 different potential shapes of the core has been modelled by finite element and then the result have been used to train an artificial neural network. Comparing this model with linear and logarithmic regression models proved that ANN evaluates water flow rate with more precession. Output data, including flow rate, entered to the particle swarm optimization (PSO) algorithm and the optimized dimensions were achieved. The proposed model for optimizing the clay core dimension can be applied for non-homogeneous earth fill dams with impervious foundation. Allavian earth fill dam was chosen to show the benefit of using the proposed optimization method in a real world case study. The results indicated that the construction cost could be dramatically less than what has been already spent on the case study. Keywords:Optimization, Clay core, Artificial neural network, Particle swarm optimization منبع: [Hidden Content] دانلود: [Hidden Content],%20B%2031%20176-181.pdf JCEU, B 31 176-181.pdf