Optimum Shape Design of Geometrically Nonlinear Submerged Arches Using the Coral Reefs Optimization with Substrate Layers Algorithm

  1. Pereira, Emiliano
  2. Camacho-Gómez, Carlos
  3. Pérez-Aracil, Jorge
  4. Salcedo-Sanz, Sancho
  5. Hernández-Díaz, Alejandro Mateo
  1. 1 Universidad de La Laguna
    info

    Universidad de La Laguna

    San Cristobal de La Laguna, España

    ROR https://ror.org/01r9z8p25

Revista:
Applied Sciences

ISSN: 2076-3417

Año de publicación: 2021

Volumen: 11

Número: 13

Páginas: 5862

Tipo: Artículo

DOI: 10.3390/APP11135862 GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Applied Sciences

Objetivos de desarrollo sostenible

Resumen

In this paper, a novel procedure for optimal design of geometrically nonlinear submerged arches is proposed. It is based on the Coral Reefs Optimization with Substrate Layers algorithm, a multi-method ensemble evolutionary approach for solving optimization problems. A novel arch shape parameterization is combined with the Coral Reefs Optimization with Substrate Layers algorithm. This new parameterization allows considering geometrical parameters in the design process, in addition to the reduction of the bending moment carried out by the classical design approach. The importance of considering the second-order behaviour of the arch structure is shown by different numerical experiments. Moreover, it is shown that the use of Coral Reefs Optimization with Substrate Layers algorithm leads to nearly-optimal solutions, ensuring the stability of the structure, reducing the maximum absolute bending moment value, and complying with the serviceability structural restrictions.

Referencias bibliográficas

  • 10.1016/0020-7683(81)90019-6
  • (2015)
  • 10.1061/(ASCE)0733-9445(2002)128:2(266)
  • 10.1016/S0141-0296(97)00067-9
  • 10.1016/S0045-7949(03)00204-9
  • 10.1016/j.engstruct.2015.07.008
  • 10.1016/j.tws.2015.04.019
  • 10.1016/j.jcsr.2019.105762
  • 10.1016/j.compstruc.2019.05.001
  • 10.1016/j.compstruc.2012.02.015
  • 10.1016/j.asoc.2017.03.037
  • 10.1016/j.cpc.2016.07.012
  • 10.4028/www.scientific.net/KEM.774.589
  • 10.1007/s10999-019-09443-3
  • 10.1007/s10999-019-09451-3
  • 10.1007/s40069-016-0140-0
  • 10.1081/SME-120020290
  • 10.1002/cmm4.1057
  • 10.1016/j.swevo.2019.100575
  • 10.1016/j.asoc.2018.07.009
  • 10.1016/j.asoc.2017.02.007
  • 10.1016/j.swevo.2018.08.015
  • 10.1016/j.jsv.2017.01.019
  • 10.1016/j.engstruct.2017.12.002
  • 10.1016/j.swevo.2018.03.003
  • 10.3390/en14092443
  • 10.1109/ACCESS.2020.3040479
  • 10.1016/j.physrep.2016.08.001
  • 10.1061/(ASCE)0733-9445(2003)129:8(1087)
  • 10.1061/(ASCE)0733-9445(2005)131:3(399)
  • Karnovsky, (2011)
  • Timoshenko, (2019)
  • Zienckiewicz, (2000)
  • Dinnik, (1946)
  • Inglis, (1951)
  • 10.1007/s00419-016-1132-x
  • Timoshenko, (1963)
  • 10.1007/s00419-014-0825-2
  • 10.1016/j.engstruct.2012.11.037
  • 10.1061/(ASCE)0733-9445(2000)126:5(627)
  • 10.1016/0045-7949(77)90027-X
  • Bathe, (1996)
  • 10.1002/cnm.887
  • https://www.ansys.com/
  • 10.1016/0045-7949(76)90056-0
  • Bathe, (2006)
  • 10.3989/ic.16.146
  • 10.1155/2014/739768
  • 10.1007/s13748-016-0104-2
  • 10.1504/IJBIC.2017.086698
  • 10.1177/003754970107600201
  • 10.1023/A:1008202821328
  • Eiben, (2003)
  • Yang, (2008), Nat. Inspired Metaheuristic Alg., 20, pp. 79
  • 10.1016/j.ins.2016.12.024
  • 10.1016/j.compstruc.2011.08.002
  • 10.1016/j.compstruc.2019.01.006
  • 10.1016/j.cor.2014.10.008
  • (2004)