1. School of Civil Architecture and Environment, Hubei University of Technology, Wuhan 430068, China 2. Beijing Glory PKPM Technology Limited Company, Beijing 100029, China
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.
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.
Fig.1Optimization framework of scheme design stage
Fig.2Optimization process of curved curtain wall at scheme design stage
Fig.3Optimization framework of deep design stage
Fig.4Optimize process of hyperbolic panels at deep design stage
Fig.5Rendering of Olympic Center in Leshan, Sichuan
Fig.6Schematic model of natatorium curtain wall engineering optimization
Fig.7Connection type of optimization variable and optimization goals in “Octopus” component
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
Tab.1Optimization variables and objective parameter table of optimization solution set
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
Tab.2Comparison table between optimization objective parameters and initial solution of optimization scheme
Fig.8Panel type distribution diagram of initial scheme
Fig.9Panel type distribution diagram of optimization scheme three
Fig.10Connection type of optimization variable and optimization goals in “Galapagos” component
Fig.11“Galapagos” interface of parameter setting
Fig.12“Galapagos” interface of optimization and analysis
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
Tab.3Error values before and after optimization of different schemes
Fig.13Gaussian curvature analysis diagram of existing optimization scheme
Fig.14Gaussian curvature analysis diagram of composite optimization scheme
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
Tab.4Optimization objective parameters of each scheme and total material cost
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