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Performance-Based Design Optimization of Steel Braced Frame Using an Efficient Discrete Algorithm

机译:高效离散算法基于性能的钢支撑框架设计优化

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Performance-based design optimization (PBDO) of steel braced frames (SBF) is a computationally intensive task, especially when nonlinear time history analysis is applied. In this paper, an efficient discrete optimization algorithm is proposed for PBDO of SBF utilizing a deformation-based method. Two difficulties exist in PBDO of SBF, and the first one is that multiple performance constraints are imposed on the structures. To avoid tackling all constraints simultaneously, a strategy is proposed in which the deformation constraints of beams and columns are strictly followed throughout the optimization process and the brace deformation constraints are checked again at the end of the optimization. The second difficulty is that the search for an optimum design is conducted in a discrete design space since the structural elements are usually taken from standard sections. A common practice is to use regression models for the sections, at the expense of removing sections that cannot fit into the regression models from the design space. In this paper all standard sections are preserved by using the cross-sectional area (Area) and moment of inertia (Ix) as the design variables, thus any standard section can be uniquely defined by its Area and Ix. To investigate the effectiveness of the proposed algorithm, three numerical examples are presented. Compared to the results achieved by genetic algorithm (GA) and differential evolution (DE), the proposed algorithm can achieve better or comparable structural designs. Furthermore, the convergence rate of the proposed algorithm is much higher than GA and DE, proving that the proposed algorithm is an efficient optimization method for PBDO of SBF.
机译:基于性能的设计优化(PBDO)的钢支撑帧(SBF)是一种计算密集型任务,尤其是当应用非线性时间历史分析时。本文提出了一种利用基于变形的方法的SBF的PBDO的有效离散优化算法。在SBF的PBD中存在两个困难,第一个是对结构施加多种性能约束。为了避免同时解决所有约束,提出了一种策略,其中严格遵循梁和列的变形约束,在整个优化过程中,在优化结束时再次检查支撑变形约束。第二个难点是,在离散的设计空间中搜索最佳设计,因为结构元件通常从标准部分中取出。常见的做法是为了使用部分的回归模型,以牺牲不能符合设计空间的回归模型的部分。在本文中,通过使用惯性区域(区域)和惯性矩(IX)作为设计变量来保留所有标准部分,因此任何标准部分都可以由其区域和IX独特地定义。为了研究所提出的算法的有效性,提出了三个数值示例。与遗传算法(GA)和差分演进(DE)实现的结果相比,所提出的算法可以实现更好或更具可比的结构设计。此外,所提出的算法的收敛速率远高于GA和DE,证明该算法是SBF的PBDO的有效优化方法。

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