Structural Optimization for Masonry Shell Design Using Multi-objective Evolutionary Algorithms

Esra Cevizci, Seckin Kutucu, Mauricio Morales Beltran, Berk Ekici, Fatih Tasgetiren

Research output: Chapter in Book/Conference proceedings/Edited volumeChapterScientificpeer-review

Abstract

In this study, the implementation of evolutionary algorithms to the form-finding problem of masonry shell models is presented using Autoclaved Aerated Concrete material. Regarding the significance of design decisions, the study is focused on the conceptual stage of the design process. In this context, the applied method is addressed as multi-objective real-parameter constrained optimization. For the sake of dealing with the shell design problem, two objective functions are considered: minimization of global displacement and minimization of mass. Two multi-objective evolutionary algorithms, namely, Non-Dominated Sorting Genetic Algorithm II and Real-coded Genetic Algorithm with mutation strategy of Differential Evolution Algorithms are compared in terms of computational and architectural performance. As a result, the solutions generated by these algorithms are found much competitive.
Original languageEnglish
Title of host publicationOptimization in Industry
Subtitle of host publicationPresent Practices and Future Scopes
EditorsS. Datta, J. Davim
Place of PublicationCham, Switzerland
PublisherSpringer
Chapter5
Pages85-119
ISBN (Electronic)978-3-030-01641-8
ISBN (Print)978-3-030-01640-1
DOIs
Publication statusPublished - 2019

Keywords

  • Architectural design
  • Form-finding
  • Parametric design
  • Computational design
  • Optimization
  • Evolutionary algorithms
  • Asymmetric shells structures

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  • Cite this

    Cevizci, E., Kutucu, S., Morales Beltran, M., Ekici, B., & Tasgetiren, F. (2019). Structural Optimization for Masonry Shell Design Using Multi-objective Evolutionary Algorithms. In S. Datta, & J. Davim (Eds.), Optimization in Industry: Present Practices and Future Scopes (pp. 85-119). Springer. https://doi.org/10.1007/978-3-030-01641-8_5