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An efficient methodology is proposed to find optimal shape of arch dams on the basis of constrained natural frequencies. The optimization is carried out by virtual sub population (VSP) evolutionary algorithm employing real values of design variables. In order to reduce the computational cost of the optimization process, the arch dam natural frequencies are predicted by properly trained back propagation (BP) and wavelet back propagation (WBP) neural networks. The WBP network provides better generalization compared with the standard BP network. The numerical results demonstrate the computational merits of the proposed methodology for optimum design of arch dams.

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In recent years, the importance of economical considerations in the field of dam engineering has motivated many researchers to propose new methods for minimizing the cost of dames and in particular arch dams. This paper presents a method for shape optimization of double curvature arch dams corresponding to minimum construction cost while satisfying different constraints such as natural frequencies, stability and geometrical limitations. For optimization, the charged system search (CSS) and particle swarm optimization (PSO) are employed. To validate the finite element model, a real arch dam is considered as a test example. The results of the present method are compared to those of other optimization algorithms for the selected example from literature.

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In the present paper, nonlinear behavior of mass concrete simulated by smeared crack model is combined with Endurance Time Analysis (ETA) method for studying seismic response of arch dams. ETA is a time-history based dynamic pushover procedure in which special predesigned intensifying acceleration functions are used for analysis and estimation of structural responses in various performance levels by just a single analysis. For this purpose 203m DEZ arch dam is selected as case study and the finite element model of dam-reservoir-foundation system is excited in various performance levels. It was found that ETA provides reasonable responses in comparison with Time-History Analysis (THA) at equivalent target time. In addition, ETAleads to acceptable estimation of crack profiles within dam body and can reduce the total time of analyses, meaningfully. Generally, it was concluded that ETA can be considered as an alternative of THA in nonlinear analysis of arch dams.

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In this paper, optimal design of arch dams is performed under frequency limitations. Colliding Bodies Optimization (CBO), a recently developed meta-heuristic optimization method, which has been successfully applied to several structural problems, is revised and utilized for finding the best feasible shape of arch dams. The formulation of CBO is derived from one-dimensional collisions between bodies, where each agent solution is considered as the massed object or body. The design procedure aims to obtain minimum weight of arch dams subjected to natural frequencies, stability and geometrical limitations. Two arch dam examples from the literature are examined to verify the suitability of the design procedure and to demonstrate the effectiveness and robustness of the CBO in creating optimal design for arch dams. The results of the examples show that CBO is a powerful method for optimal design of arch dams.

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For optimization of real-world arch dams, it is unavoidable to consider two or more conflicting objectives. This paper employs two multi-objective differential evolution algorithms (MoDE) in combination of a parallel working MATLAB-APDL code to obtain a set of Pareto solutions for optimal shape of arch dams. Full dam-reservoir interaction subjected to seismic loading is considered. A benchmark arch dam is then examined as the numerical example. The numerical results are compared to show the performance of the MoDE methods.

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Optimization techniques can be efficiently utilized to achieve an optimal shape for arch dams. This optimal design can consider the conditions of the economy and safety simultaneously. The main aim is to present an applicable and practical model and suggest an algorithm for optimization of concrete arch dams to enhance their seismic performance. To achieve this purpose, a preliminary optimization is accomplished using PSO procedure in the first stage. Capabilities of Ansys Parametric Design Language (APDL) are applied for modeling the Dam-Foundation-Reservoir system. In the second stage with training the neural network, Group Method of Data Handling (GMDH) and replacement of Ansys analyst, optimal results have been achieved with the lowest error and less number of iteration respectively. Then a real world double-arch dam is presented to demonstrate the effectiveness and practicality of the PSO-GMDH. The numerical results reveal that the proposed method called PSO-GMDH provides faster rate and high searching accuracy to achieve the optimal shape of arch concrete dams and the modification and optimization of shape have a quite important role in increasing the safety against dynamic design loads.