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Multi-objective optimization of dual-purpose outriggers in tall buildings to reduce lateral displacement and differential axial shortening

机译:高层建筑中两用支腿的多目标优化,以减少侧向位移和轴向差值缩短

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Maximum lateral displacement (LAT) and differential axial shortening (DAS) are important criteria to be considered in the design of tall buildings. Outrigger (OR) systems have proven to be efficient for reducing LAT and DAS in tall buildings. We developed a hybrid multi-objective optimization (MOO) method to determine the optimal locations of ORs to minimize LAT and DAS. Because minimizing LAT and DAS are conflicting objectives, multiple Pareto-front solutions are provided by the proposed method. Finite element analysis was used to evaluate the LAT and DAS of arbitrary shaped buildings. The steepest descent method was used to perform a gradient-based line search. To overcome the integrality requirements introduced by the integer design variables, piecewise quadratic interpolation was used to relax the integer variables. The scalarization of two objective functions was performed by using the weighted-sum method. Elite populations identified during scalarized optimization are stored and compared to apply the advantages of evolutional optimization to the MOO problem. It is demonstrated that the Pareto front obtained by the proposed method provides the same results as an exhaustive search.
机译:最大高层位移(LAT)和轴向差动缩短(DAS)是高层建筑设计中要考虑的重要标准。实践证明,支腿(OR)系统可有效降低高层建筑的LAT和DAS。我们开发了一种混合多目标优化(MOO)方法来确定OR的最佳位置,以最小化LAT和DAS。由于最小化LAT和DAS是相互冲突的目标,因此,该方法提供了多个Pareto-front解决方案。有限元分析用于评估任意形状的建筑物的LAT和DAS。最速下降法用于执行基于梯度的线搜索。为了克服整数设计变量引入的完整性要求,使用分段二次插值来松弛整数变量。通过使用加权和方法对两个目标函数进行了标量化。存储并比较在标量优化过程中确定的精英群体,以将进化优化的优势应用于MOO问题。证明了通过所提出的方法获得的帕累托前沿提供了与穷举搜索相同的结果。

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