Showing 5 results for Natural Frequency
Saeed Gholizadeh, Seyed Mohammad Seyedpoor,
Volume 1, Issue 1 (3-2011)
Abstract
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.
A. Kaveh, V.r. Mahdavi,
Volume 1, Issue 4 (12-2011)
Abstract
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.
A. Kaveh, K. Biabani Hamedani, F. Barzinpour,
Volume 10, Issue 2 (4-2020)
Abstract
Meta-heuristic algorithms are applied in optimization problems in a variety of fields, including engineering, economics, and computer science. In this paper, seven population-based meta-heuristic algorithms are employed for size and geometry optimization of truss structures. These algorithms consist of the Artificial Bee Colony algorithm, Cyclical Parthenogenesis Algorithm, Cuckoo Search algorithm, Teaching-Learning-Based Optimization algorithm, Vibrating Particles System algorithm, Water Evaporation Optimization, and a hybridized ABC-TLBO algorithm. The Taguchi method is employed to tune the parameters of the meta-heuristics. Optimization aims to minimize the weight of truss structures while satisfying some constraints on their natural frequencies. The capability and robustness of the algorithms is investigated through four well-known benchmark truss structure examples.
Dr V.r. Mahdavi, Prof. A. Kaveh,
Volume 14, Issue 3 (6-2024)
Abstract
In order to evaluate the damage state, value, and position of structural members more accurately, a multi-objective optimization (MO) method is utilized that is based on changes in natural frequency. The multi-objective optimization dynamic-based damage detection method is first introduced. Two objective functions for optimization are then introduced in terms of changing the natural frequencies and mode shapes. The multi-objective optimization problem (MOP) is formulated by using the two objective functions. Three considered MO algorithms consist of Colliding Bodies Optimization (MOCBO), Particle Swarm Optimization (MOPSO), and non-dominated sorting genetic algorithm (NSGA-II) to achieve the best structural damage detection. The proposed methods are then applied to three planar steel frame structures. Compared to the traditional optimization methods utilizing the single-objective optimization (SO) algorithms, the presented methods provide superior results.
Dr. V. Goodarzimehr, Dr. N. Fanaie, Dr. S. Talatahari,
Volume 15, Issue 1 (1-2025)
Abstract
In this study, the Improved Material Generation Algorithm (IMGA) is proposed to optimize the shape and size of structures. The original Material Generation Algorithm (MGA) introduced an optimization model inspired by the high-level and fundamental characteristics of material chemistry, particularly the configuration of compounds and chemical reactions for generating new materials. MGA uses a Gaussian normal distribution to produce new combinations. To enhance MGA for adapting truss structures, a new technique called Random Chaotic (RC) is proposed. RC increases the speed of convergence and helps escape local optima. To validate the proposed method, several truss structures, including a 37-bar truss bridge, a 52-bar dome, a 72-bar truss, a 120-bar dome, and a 200-bar planar structure, are optimized under natural frequency constraints. Optimizing the shape and size of structures under natural frequency constraints is a significant challenge due to its complexity. Choosing the frequency as a constraint prevents resonance in the structure, which can lead to large deformations and structural failure. Reducing the vibration amplitude of the structure decreases tension and deflection. Consequently, the weight of the structure can be minimized while keeping the frequencies within the permissible range. To demonstrate the superiority of IMGA, its results are compared with those of other state-of-the-art metaheuristic methods. The results show that IMGA significantly improves both exploitation and exploration.