| 张望喜,黄星,刘霞.遗传递增演化算法配筋优化设计[J].计算力学学报,2017,34(3):303~311 |
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| 遗传递增演化算法配筋优化设计 |
| Reinforcement layout optimization design using the genetic adding evolutionary structural optimization |
| 投稿时间：2016-01-19 修订日期：2016-03-04 |
| DOI：10.7511/jslx201703007 |
| 中文关键词: 递增演化结构算法(AESO) 遗传递增演化算法(GAESO) 非线性分析 复杂应力构件 配筋优化设计 |
| 英文关键词:adding evolutionary structural optimization (AESO) genetic adding evolutionary structural optimization (GAESO) nonlinear analysis complex-stress members reinforcement layout optimization design |
| 基金项目:国家自然科学基金（51578228）；国家重点研发计划专项（2016YFC0701400）资助项目 |
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| 中文摘要: |
| 递增演化结构算法（AESO）是在初始结构上，逐步增加有效材料，进而得到优化拓扑形状，其运算速度快，但容易陷入局部最优解。为提高寻找全局最优解的能力，把递增演化结构算法（AESO）和遗传算法（GA）相结合，提出遗传递增演化算法（GAESO），在增加有效材料的选择性上引入生物进化遗传理论，并以ANSYS有限元分析软件的非线性分析为平台，采用钢筋混凝土分离式模型，探讨遗传递增演化算法（GAESO）在钢筋混凝土复杂应力构件配筋优化设计上的应用，直观地完成在荷载作用和应力约束条件下简支深梁、简支开洞深梁和开洞剪力墙等钢筋混凝土复杂应力构件的配筋优化设计，所得结果符合受力机理，演化方向正确，钢筋布置明确，与遗传演化优化算法（GESO）所得结果进行比较验证，证实了算法的可行性、速敛性和稳定性，能为钢筋混凝土配筋优化设计提供参考。 |
| 英文摘要: |
| The adding evolutionary structural optimization(AESO)started from an initial base,the effective material was gradually added,and the final optimal structural form emerged finally.It worked quickly but easily fell into a local optimal solution.In order to improve the ability to find the global optimal solution,the evolutionary structural optimization (ESO) is combined with the genetic algorithm (GA) in this paper,the genetic adding evolutionary structural optimization (GAESO) is proposed,using the genetic theory of biological evolution in the selection of adding materials.On the nonlinear analysis platform in ANSYS,GAESO was used in the reinforced-concrete separating model to explore the reinforcement layout optimization design of complex-stressed members.The reinforcement layout optimization design was completed intuitively of some deep beams,deep beams with opening and shear walls with opening under the loads and some stress constraints.The results agree with the loading mechanism,the evolving direction is right and the reinforcement layout is arranged clearly.Comparison with the outcome of the genetic evolutionary structural optimization (GESO),the similarity,feasibility,fast-convergence,and the stability of the algorithm were confirmed.The algorithm in this paper can provide a reference to the design of reinforcement in concrete. |
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