Seismic design of steel frames using multi-objective optimization

Abstract. In this study a multi-objective optimization problem is solved. The objectives used here include simultaneous minimum construction cost in term of sections weight, minimum structural damage using a damage index, and minimum non-structural damage in term of inter-story drift under the applied ground motions. A high-speed and low-error neural network is trained and employed in the process of optimization to estimate the results of non-linear time history analysis. This approach can be utilized for all steel or concrete frame structures. In this study, the optimal design of a planar eccentric braced steel frame is performed with great detail, using the presented multi-objective algorithm with a discrete population and then a moment resisting frame is solved as a supplementary example. Keywords: seismic design; multi-objective optimization; eccentric braced frame (EBF); moment resisting frame; neural networks; damage index; construction cost 1. Introduction Optimal design of multistory structures is usually performed with two conflicting objectives that are the minimum present construction cost and the maximum performance in future under the probable ground motions. The first objective is related to the structural weight and the second one is related to the minimum damage of the structure. Common single-optimization approaches cannot achieve these goals, and making a new model that can optimize a variety of objectives has been a challenging topic among researchers throughout the world proposing different kinds of methods. In this paper, weight, minimum damage to structure, and minimum non-structural damage in the term of inter-story drift are considered as three main objectives. Different techniques of finding multiple answers employing evolutionary algorithms (EA) have been previously developed. Although the importance of finding multiple answers are quite obvious, however, the recent usages of these methods in multi-objective optimization problems are often based on preference. The first real application of EA in finding multiple answers was presented by David Scaffer in his doctoral thesis (1984). David Goldberg (1989b) presented a 10 line multi-objective evolutionary algorithm (MOEA) by the use of domination concept. Following his

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