STRUCTURAL DESIGN OF OFFICE BUILDING BY COMPUTATIONAL MORPHOGENESIS(Structures)

Ohmori, Hiroshi; Futai, Hiroyuki; Iwamoto, Toshihiko; Muto, Atsushi; Hasegawa, Yasutoshi

doi:10.3130/aijt.10.77

Cited by 7 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lai et al (2023) applied the multi-material BESO on the wind bracing design of a 574-m arch bridge in Chongqing, China. Ohmori et al (2004) used the EESO method for the wall design of the Akutagawa River Side building in Takatsuki, Japan. In addition to the civil and architectural applications, Airbus (Krog et al, 2009) successfully applied topology optimization on its A380, A400, and A350 plane models, including the design of wing ribs, engine mount frames, and floor crossbeams.…”

Section: Introductionmentioning

confidence: 99%

Topology optimization of shell structures in architectural design

Ma,

Lu,

Lee

et al. 2023

ARIN

View full text Add to dashboard Cite

Free-form architectural design has gained significant interest in modern architectural practice. Due to their visually appealing nature and inherent structural efficiency, free-form shells have become increasingly popular in architectural applications. Recently, topology optimization has been extended to shell structures, aiming to generate shell designs with ultimate structural efficiency. However, despite the huge potential of topology optimization to facilitate new design for shells, its architectural applications remain limited due to complexity and lack of clear procedures. This paper presents four design strategies for optimizing free-form shells targeting architectural applications. First, we propose a topology-optimized ribbed shell system to generate free-form rib layouts possessing improved structure performance. A reusable and recyclable formwork system is developed for their effective and sustainable fabrication. Second, we demonstrate that topology optimization can be combined with funicular form-finding techniques to generate a rich variety of elegant designs, offering new design possibilities. Third, we offer cost-effective design solutions using modular components for free-form shells by combining surface planarization and periodic constraint. Finally, we integrate topology optimization with user-defined patterns on free-form shells to facilitate aesthetic expression, exemplified by the Voronoi pattern. The presented strategies can facilitate the usage of topology optimization in shell designs to achieve high-performance and innovative solutions for architectural applications.

show abstract

Section: Introductionmentioning

confidence: 99%

Topology optimization of shell structures in architectural design

Ma,

Lu,

Lee

et al. 2023

ARIN

View full text Add to dashboard Cite

show abstract

“…BESO improves the robustness of the solution process compared to ESO. For its simple form and effectivity, ESO/BESO has been successfully used for actual engineering, including the Akutagawa office building in Japan [15] and Qatar International Exhibition Center [16]. Yunzhen He et al [17] integrated BESO with genetic algorithms to create diverse and efficient structural designs.…”

Section: Introductionmentioning

confidence: 99%

An Improved Strategy for Genetic Evolutionary Structural Optimization

Cui

Huang

Ding³

2020

Advances in Civil Engineering

View full text Add to dashboard Cite

Genetic evolutionary structural optimization (GESO) method is an integration of the genetic algorithm (GA) and evolutionary structural optimization (ESO). It has proven to be more powerful in searching for global optimal response and requires less computational efforts than ESO or GA. However, GESO breaks down in the Zhou-Rozvany problem. Furthermore, GESO occasionally misses the optimum layout of a structure in the evolution for its characteristic of probabilistic deletion. This paper proposes an improved strategy that has been realized by MATLAB programming. A penalty gene is introduced into the GESO strategy and the performance index (PI) is monitored during the optimization process. Once the PI is less than the preset value which means that the calculation error of some element’s sensitivity is too big or some important elements are mistakenly removed, the penalty gene becomes active to recover those elements and reduce their selection probability in the next iterations. It should be noted that this improvement strategy is different from “freezing,” and the recovered elements could still be removed, if necessary. The improved GESO performs well in the Zhou-Rozvany problem. In other numerical examples, the results indicate that the improved GESO has inherited the computational efficiency of GESO and more importantly increased the optimizing capacity and stability.

show abstract