Skip to main content
SpringerLink
Log in

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

  • Chapter
  • First Online:
Book cover Optimization in Industry

Part of the book series: Management and Industrial Engineering ((MINEN))

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Panozzo, D., Block, P., & Sorkine-Hornung, O. (2013). Designing unreinforced masonry models. ACM Transactions on Graphics (TOG), 32, 91.

    MATH  Google Scholar 

  2. Adriaenssens, S., Block, P., Veenendaal, D., & Williams, C. (2014). Shell structures for architecture: Form finding and optimization. Taylor & Francis.

    Google Scholar 

  3. Block, P., & Lachauer, L. (2011). Closest-fit, compressiononly solutions for free form shells. In IABSE/IASS London Symposium, International Association for Shell Spatial Structures.

    Google Scholar 

  4. Rippmann, M., Lachauer, L., & Block, P. (2012). Interactive vault design. International Journal of Space Structures, 27, 219–230.

    Article  Google Scholar 

  5. Rippmann, M., & Block, P. (2013). Funicular shell design exploration. In ACADIA, Waterloo, Canada.

    Google Scholar 

  6. Narayanan, N., & Ramamurthy, K. (2000). Structure and properties of aerated concrete: A review. Cement & Concrete Composites, 22, 321–329.

    Article  Google Scholar 

  7. Wittmann, F. H., & Balkema, A. (1992) Advances in autoclaved aerated concrete. Citeseer.

    Google Scholar 

  8. Brisch, J. H., Nelson, R. L., & Francis, H. L. (1999) Masonry: Materials, testing, and applications. American Society for Testing Materials.

    Google Scholar 

  9. Aroni, I. S. (1993). Autoclaved aerated concrete-properties, testing and design. CRC Press.

    Google Scholar 

  10. Pacheco-Torgal, F., Lourenc̦o, P. B., Labrincha, J., & Kumar, S. (2014). Eco-efficient masonry bricks and blocks: Design, properties and durability. Woodhead Publishing.

    Google Scholar 

  11. Preisinger, C., & Heimrath, M. (2014). Karamba—A toolkit for parametric structural design. Structural Engineering International, 24, 217–221.

    Article  Google Scholar 

  12. Gulvanessian, H., Calgaro, J.-A., & Holický, M. (2002). Designer’s guide to EN 1990: Eurocode: Basis of structural design. Thomas Telford.

    Google Scholar 

  13. Becker, M., & Golay, P. (1999). Rhino NURBS 3D modeling. New Riders.

    Google Scholar 

  14. McNeel, R. (2009). Rhinoceros 3D (Vol. 15).

    Google Scholar 

  15. McNeel, R. (2013). Grasshopper 3D.

    Google Scholar 

  16. Sariyildiz, I. (2012, November 15–17). Performative computational design. In Keynote speech in: Proceedings of ICONARCH-I: International Congress of Architecture-I, Konya, Turkey.

    Google Scholar 

  17. Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA_II. IEEE Transactions on Evolutionary Computation, 6, 182–197.

    Article  Google Scholar 

  18. Zitzler, E., Laumanns, M., & Thiele, L. (2001) SPEA2: Improving the strength Pareto evolutionary algorithm. In Eurogen, 2001 (pp. 95–100).

    Google Scholar 

  19. Cubukcuoglu, C., Chatzikonstantinou, I., Tasgetiren, M. F., Sariyildiz, I. S., & Pan, Q.-K. (2016). A multi-objective harmony search algorithm for sustainable design of floating settlements. Algorithms, 9, 51.

    Article  MathSciNet  Google Scholar 

  20. Ugurlu, C., Chatzikonstantinou, I., Sariyildiz, S., & Tasgetiren, M. F. (2015). Identification of sustainable designs for floating settlements using computational design techniques. In 2015 IEEE Congress on Evolutionary Computation (CEC) (pp. 2303–2310).

    Google Scholar 

  21. Ugurlu, C., Chatzikonstantinou, I., Sariyildiz, S., & Tasgetiren, M. F. (2015). Evolutionary computation for architectural design of restaurant layouts. In 2015 IEEE Congress on Evolutionary Computation (CEC) (pp. 2279–2286).

    Google Scholar 

  22. Goldberg, D. E. (2006). Genetic algorithms. Pearson Education India.

    Google Scholar 

  23. Brest, J., Greiner, S., Bošković, B., Mernik, M., & Zumer, V. (2006). Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE Transactions on Evolutionary Computation, 10, 646–657.

    Article  Google Scholar 

  24. Bader, J., & Zitzler, E. (2011). HypE: An algorithm for fast hypervolume-based many-objective optimization. Evolutionary Computation, 19, 45–76.

    Article  Google Scholar 

  25. Zitzler, E., & Thiele, L. (1999). Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation, 3, 257–271.

    Article  Google Scholar 

  26. Jiang, S., Ong, Y.-S., Zhang, J., & Feng, L. (2014). Consistencies and contradictions of performance metrics in multiobjective optimization. IEEE Transactions on Cybernetics, 44, 2391–2404.

    Article  Google Scholar 

  27. Delgarm, N., Sajadi, B., Delgarm, S., & Kowsary, F. (2016). A novel approach for the simulation-based optimization of the buildings energy consumption using NSGA-II: Case study in Iran. Energy and Buildings, 127, 552–560.

    Article  Google Scholar 

  28. Delgarm, N., Sajadi, B., Kowsary, F., & Delgarm, S. (2016). Multi-objective optimization of the building energy performance: A simulation-based approach by means of particle swarm optimization (PSO). Applied Energy, 170, 293–303.

    Article  Google Scholar 

  29. Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1, 67–82.

    Article  Google Scholar 

Download references

Acknowledgements

This work has been supported by Yasar University (YU) “Bilimsel Araştırma Projesi” (Scientific Research Project) with the grant number BAP-037. We would like to express our sincere gratitude to the scientific committee of YU.

Author information

Authors and Affiliations

  1. Department of Architecture, Yasar University, Selcuk Yasar Campus, İzmir, Turkey

    Esra Cevizci, Seckin Kutucu, Mauricio Morales-Beltran & Berk Ekici

  2. Structural Design, AE+T, TU Delft, Delft, The Netherlands

    Mauricio Morales-Beltran

  3. Chair of Design Informatics, TU Delft, Delft, The Netherlands

    Berk Ekici

  4. Department of International Logistics Management, Yasar University, Selcuk Yasar Campus, İzmir, Turkey

    M. Fatih Tasgetiren

Authors
  1. Esra Cevizci
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Seckin Kutucu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mauricio Morales-Beltran
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Berk Ekici
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. M. Fatih Tasgetiren
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Seckin Kutucu .

Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, SRM Institute of Science and Technology, Chennai, India

    Shubhabrata Datta

  2. Department of Mechanical Engineering, University of Aveiro, Aveiro, Portugal

    J. Paulo Davim

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Cevizci, E., Kutucu, S., Morales-Beltran, M., Ekici, B., Fatih Tasgetiren, M. (2019). Structural Optimization for Masonry Shell Design Using Multi-objective Evolutionary Algorithms. In: Datta, S., Davim, J. (eds) Optimization in Industry. Management and Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-01641-8_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-01641-8_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01640-1

  • Online ISBN: 978-3-030-01641-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation