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Showing 4 results for Aisc-Lrfd

A. Kaveh, T. Bakhshpoori , E. Afshari,
Volume 1, Issue 4 (12-2011)

This paper is concerned with the economical comparison between two commonly used configurations for double layer grids and determining their optimum span-depth ratio. Two ranges of spans as small and big sizes with certain bays of equal length in two directions and various types of element grouping are considered for each type of square grids. In order to carry out a precise comparison between different systems, optimum design procedure based on the Cuckoo Search (CS) algorithm is developed. The CS is a meta-heuristic algorithm recently developed that is inspired by the behavior of some Cuckoo species in combination with the Lévy flight behavior of some birds and insects. The design algorithm obtains minimum weight grid through appropriate selection of tube sections available in AISC Load and Resistance Factor Design (LRFD). Strength constraints of AISC-LRFD specification and displacement constraints are imposed on grids. The comparison is aimed at finding the depth at which each of the different configurations shows its advantages. The results are graphically presented from which the optimum depth can easily be estimated for each type, while the influence of element grouping can also be realized at the same time.
S. Kazemzadeh Azad, O. Hasançebi,
Volume 3, Issue 4 (10-2013)

This paper attempts to improve the computational efficiency of the well known particle swarm optimization (PSO) algorithm for tackling discrete sizing optimization problems of steel frame structures. It is generally known that, in structural design optimization applications, PSO entails enormously time-consuming structural analyses to locate an optimum solution. Hence, in the present study it is attempted to lessen the computational effort of the algorithm, using the so called upper bound strategy (UBS), which is a recently proposed strategy for reducing the total number of structural analyses involved in the course of design optimization. In the UBS, the key issue is to identify those candidate solutions which have no chance to improve the search during the optimum design process. After identifying those non-improving solutions, they are directly excluded from the structural analysis stage, diminishing the total computational cost. The performance of the UBS integrated PSO algorithm (UPSO) is evaluated in discrete sizing optimization of a real scale steel frame to AISC-LRFD specifications. The numerical results demonstrate that the UPSO outperforms the original PSO algorithm in terms of the computational efficiency.
S. Kazemzadeh Azad, O. Hasançebi , S. Kazemzadeh Azad,
Volume 4, Issue 2 (6-2014)

Computational cost of metaheuristic based optimum design algorithms grows excessively with structure size. This results in computational inefficiency of modern metaheuristic algorithms in tackling optimum design problems of large scale structural systems. This paper attempts to provide a computationally efficient optimization tool for optimum design of large scale steel frame structures to AISC-LRFD specifications. To this end an upper bound strategy (UBS), which is a recently proposed strategy for reducing the total number of structural analyses in metaheuristic optimization algorithms, is used in conjunction with an exponential variant of the well-known big bang-big crunch optimization algorithm. The performance of the UBS integrated algorithm is investigated in the optimum design of two large-scale steel frame structures with 3860 and 11540 structural members. The obtained numerical results clearly reveal the usefulness of the employed technique in practical optimum design of large-scale structural systems even using regular computers.
E. Pouriyanezhad, H. Rahami, S. M. Mirhosseini,
Volume 10, Issue 2 (4-2020)

In this paper, the discrete method of eigenvectors of covariance matrix has been used to weight minimization of steel frame structures. Eigenvectors of Covariance Matrix (ECM) algorithm is a robust and iterative method for solving optimization problems and is inspired by the CMA-ES method. Both of these methods use covariance matrix in the optimization process, but the covariance matrix calculation and new population generation in these two methods are completely different. At each stage of the ECM algorithm, successful distributions are identified and the covariance matrix of the successful distributions is formed. Subsequently, by the help of the principal component analysis (PCA), the scattering directions of these distributions will be achieved. The new population is generated by the combination of weighted directions that have a successful distribution and using random normal distribution. In the discrete ECM method, in case of succeeding in a certain number of cycles the step size is increased, otherwise the step size is reduced. In order to determine the efficiency of this method, three benchmark steel frames were optimized due to the resistance and displacement criteria specifications of the AISC-LRFD, and the results were compared to other optimization methods. Considerable outputs of this algorithm show that this method can handle the complex problems of optimizing discrete steel frames.

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