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In this paper nonlinear analysis of structures are performed considering material and geometric nonlinearity using force method and energy concepts. For this purpose, the complementary energy of the structure is minimized using ant colony algorithms. Considering the energy term next to the weight of the structure, optimal design of structures is performed. The first part of this paper contains the formulation of the complementary energy of truss and frame structures for the purpose of linear analysis. In the second part material and geometric nonlinearity of structure is considered using Ramberg-Osgood relationships. In the last part optimal simultaneous analysis and design of structure is studied. In each part, the efficiency of the methods is illustrated by means simple examples.

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By minimizing the total potential energy function and deploying the virtual work principle, a higher-order stiffness matrix is achieved. This new tangent stiffness matrix is used to solve the frame with geometric nonlinear behavior. Since authors’ formulation takes into account the higher-order terms of the strain vector, the convergence speed of the solution process will increase. In fact, both linear and nonlinear parts of the frame axial strains are included in the presented formulation. These higher-order terms affect the resulting unbalanced force and also frame tangent stiffness. Moreover, the finite element method, updated Lagrangian description, and arc length scheme are employed in this study. To check the efficiency of the proposed strategy, several numerical examples are solved. The findings indicate that the authors’ technique can accurately trace the structural equilibrium paths having the limit points.

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In this study, the complex behavior of steel encased reinforced concrete (SRC) composite beam–columns in biaxial bending is predicted by multilayer perceptron neural network. For this purpose, the previously proposed nonlinear analysis model, mixed beam-column formulation, is verified with biaxial bending test results. Then a large set of benchmark frames is provided and P-M_x-M_y triaxial interaction curve is obtained for them. The specifications of these frames and their analytical results are defined as inputs and targets of artificial neural network and a relatively accurate estimation model of the nonlinear behavior of these beam-columns is presented. In the end, the results of neural network are compared to some analytical examples of biaxial bending to determine the accuracy of the model.

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Dampers can reduce structural response under dynamic loads. Since dampers are costly, the design of structures equipped with dampers should make their application economically justifiable. Among the effective cost reduction factors is optimal damper placement. Hence, this study intended to find the optimal viscous damper placement using efficient optimization methods. Taking into account the nonlinear behavior of structure, this optimal distribution can be determined through meeting story-wise damping requirements such that the structure provides the minimum dynamic response and becomes economically justified. To compare the effect of different damper placement layouts on structural response and determine the objective function of optimization, the ratio of peak structural displacement to yield displacement was used as the damage index and objective function of optimization. Colliding Bodies' Optimization (CBO) algorithm was used for optimal damper placement. In this study, the 3- and 4-story concrete frames with different damper placement conditions were studied. Results confirmed the efficiency of the proposed method and algorithm in optimal viscous damper placement in each story. It was also discovered that the application of dampers on higher stories partially uniforms height-wise damage distribution and works towards the design goals.

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Most industrial-practical projects deal with nonlinearity phenomena. Therefore, it is vital to implement a nonlinear method to analyze their behavior. The Finite Element Method (FEM) is one of the most powerful and popular numerical methods for either linear or nonlinear analysis. Although this method is absolutely robust, it suffers from some drawbacks. One of them is convergency issues, especially in large deformation problems. Prevalent iterative methods such as the Newton-Raphson algorithm and its various modified versions cannot converge in certain problems including some cases such as snap-back or through-back. There are some appropriate methods to overcome this issue such as the arc-length method. However, these methods are difficult to implement. In this paper, a computational framework is presented based on meta-heuristic algorithms to improve nonlinear finite element analysis, especially in large deformation problems. The proposed method is verified via different benchmark problems solved by commercial software. Finally, the robustness of the proposed algorithm is discussed compared to the classic methods.
 

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Most industrial-practical projects deal with nonlinearity phenomena. Therefore, it is vital to implement a nonlinear method to analyze their behavior. The Finite Element Method (FEM) is one of the most powerful and popular numerical methods for either linear or nonlinear analysis. Although this method is absolutely robust, it suffers from some drawbacks. One of them is convergency issues, especially in large deformation problems. Prevalent iterative methods such as the Newton-Raphson algorithm and its various modified versions cannot converge in certain problems including some cases such as snap-back or through-back. There are some appropriate methods to overcome this issue such as the arc-length method. However, these methods are difficult to implement. In this paper, a computational framework is presented based on meta-heuristic algorithms to improve nonlinear finite element analysis, especially in large deformation problems. The proposed method is verified via different benchmark problems solved by commercial software. Finally, the robustness of the proposed algorithm is discussed compared to the classic methods.