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浙江大学学报(工学版)
浙江大学学报(工学版)  2022, Vol. 56 Issue (10): 2049-2056    DOI: 10.3785/j.issn.1008-973X.2022.10.016
土木工程、交通工程、海洋工程     
成本导向下基于遗传算法的曲面幕墙设计优化
邹贻权1(),黄浩洲1(),夏绪勇2,王鑫1
1. 湖北工业大学 土木建筑与环境学院，湖北 武汉 430068
2. 北京构力科技有限公司，北京 100029
Design optimization of curved curtain wall based on genetic algorithm under cost orientation
Yi-quan ZOU1(),Hao-zhou HUANG1(),Xu-yong XIA2,Xin WANG1
1. School of Civil Architecture and Environment, Hubei University of Technology, Wuhan 430068, China
2. Beijing Glory PKPM Technology Limited Company, Beijing 100029, China
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摘要：

针对目前建筑越来越多采用曲面幕墙而带来的高额建造成本问题，提出方案设计阶段优化与深化设计阶段优化相结合的两阶段复合优化方案，对曲面幕墙工程项目进行成本优化. 通过Rhino建立曲面幕墙参数化模型，利用Grasshopper逻辑运算器编写优化方案，实现控制参数生成的优化方案. 在方案设计阶段中，使用多目标遗传算法进行网格划分方案优化. 综合考虑面板类型占比与龙骨综合长度的平衡解集，选择满足工程需求的最优方案解，在深化设计阶段中使用单目标遗传算法，将双曲型面板优化为单曲型面板，降低双曲型面板数量的占比. 通过实际工程应用的分析可知，该复合优化方案在原始方案基础上的优化率为23.19%，现有只在单一阶段进行优化的方案优化率为16.20%，复合优化方案的优化率在现有的优化方案的基础上增加了6.99%. 研究结果表明，利用该研究方案，能够有效地降低曲面幕墙工程项目成本.
关键词： 曲面幕墙;  成本优化;  多目标遗传算法;  单目标遗传算法;  复合优化方案    
Abstract:

A two-stage composite optimization scheme combining optimization at the scheme design stage and optimization at the deepening design stage was proposed to optimize the costs of curved curtain wall projects aiming at the problem of high construction costs caused by the increasing use of curved curtain walls in current buildings. A parametric model of curved curtain wall was established by Rhino, and the optimization scheme was written by using Grasshopper logic operator to realize the optimization scheme generated by control parameters. The optimization of the mesh division scheme was conducted by using multi-objective genetic algorithm in the scheme design stage. The equilibrium solution set of panel type share and keel integrated length was comprehensively considered to select the optimal solution to meet the engineering requirements. The single-objective genetic algorithm was used to optimize the hyperbolic panels into single-curved panels and reduce the proportion of the number of hyperbolic panels in the deepening design stage. The analysis of the actual engineering application showed that the optimization rate of this composite optimization scheme was 23.19% based on the original scheme. The optimization rate of the existing scheme which was only optimized in a single stage was 16.20%, and the optimization rate of the composite optimization scheme was increased by 6.99% based on the existing optimization scheme. The research results show that the use of this research scheme can effectively reduce the cost of curved curtain wall projects.
Key words: curved curtain wall    cost optimization    multi-objective genetic algorithm    single-objective genetic algorithm    compound optimization scheme
收稿日期: 2021-11-05 出版日期: 2022-10-25
CLC:  TU 2  
基金资助: “科技助力经济2020”重点专项资助项目（2020ZLSH08）
作者简介: 邹贻权（1973—），男，教授，硕导，从事数字化设计与建造的研究. orcid.org/0000-0001-5855-0038. E-mail： zouyq@mail.hbut.edu.cn
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引用本文:

邹贻权,黄浩洲,夏绪勇,王鑫. 成本导向下基于遗传算法的曲面幕墙设计优化[J]. 浙江大学学报(工学版), 2022, 56(10): 2049-2056.

Yi-quan ZOU,Hao-zhou HUANG,Xu-yong XIA,Xin WANG. Design optimization of curved curtain wall based on genetic algorithm under cost orientation. Journal of ZheJiang University (Engineering Science), 2022, 56(10): 2049-2056.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2022.10.016    或    https://www.zjujournals.com/eng/CN/Y2022/V56/I10/2049

图 1  方案设计阶段的优化框架
图 2  方案设计阶段的曲面幕墙优化流程
图 3  深化设计阶段的优化框架
图 4  深化设计阶段的双曲型面板优化流程
图 5  四川省乐山市奥林匹克中心效果图
图 6  游泳馆幕墙工程优化的示意图模型
图 7  “Octopus”组件优化变量与优化目标的连接方式
方案 αU/mm αV/mm βU/mm βV/mm θU/(°) θV/(°) N1 P1/% N2 P2/% N3 P3/% Lkc/mm
初始方案 0 0 2000 1000 90 0 27 5.17 364 69.73 131 25.10 1750.65
方案1 287 639 2017 1058 84 2 25 5.18 349 72.26 109 22.56 1674.30
方案2 1689 273 2084 1093 89 ?5 29 5.93 325 66.46 135 27.61 1587.31
方案3 1235 456 2075 1035 89 ?2 26 5.62 326 70.41 111 23.97 1625.78
表 1  优化方案解集的优化变量与优化目标参数表
方案 Vn1/% Vp1/% Vn2/% Vp2/% Vn3/% Vp3/% Vkc/%
方案1 ?7.41 +1.00 ?4.12 +2.53 ?16.79 ?2.54 ?4.36
方案2 +7.41 +0.76 ?10.71 ?3.27 +3.05 +2.51 ?9.33
方案3 ?3.70 +0.45 ?10.43 +0.68 ?15.27 ?1.13 ?7.13
表 2  优化方案解集的优化目标参数与初始方案对比表
图 8  初始方案的面板类型分布图
图 9  优化方案3的面板类型分布图
图 10  “Galapagos”组件优化变量与优化目标的连接方式
图 11  “Galapagos”参数设置的界面
图 12  “Galapagos”优化与分析的界面
方案 t1/mm t2/mm γS/% γL/%
现有的优化方案 7.48 4.16 0.424 0.199
复合优化方案 5.88 3.39 0.387 0.171
表 3  不同方案优化前、后的误差
图 13  现有的优化方案的高斯曲率分析图
图 14  复合优化方案的高斯曲率分析图
方案 N1 N2 N3 Lkc/mm C/万元
初始方案 27 364 131 1750.65 66.42
现有的优化方案 27 495 0 1750.65 55.65
复合优化方案 26 437 0 1625.78 51.01
表 4  各方案的优化目标参数及材料总成本
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