Structural Analysis of GFRP Elastic Gridshell Structures by Particle Swarm Optimization and Least Square Support Vector Machine Algorithms

Document Type : Original Article

Authors

1 Department of Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

2 State Key Laboratory of Ocean Engineering, Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, Department of Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

Abstract

The gridshell structure is a kind of freeform structure, which is formed by the deformation of a flat grid and the final shape is a double curvature structure. The structural performance of the gridshell is usually obtained by finite element analysis (FEA), which is a time-consuming procedure. This paper aims to present a framework for structural analysis based on the machine learning (ML) model in order to reduce computational time. To this aim, design parameters including the length, width, height, and grid size of the structure are taken into consideration as inputs. The outputs are the member-stresses and the ratio of displacement to self-weight. Therefore, a combination of two algorithms, least-square support vector machine (LSSVM) and particle swarm optimization (PSO), is considered. PSO-LSSVM hybrid model is applied to predict the results of the structural analysis rather than the FEA. The results show that the proposed hybrid approach is an efficient method for obtaining structural performance.

Keywords

Main Subjects


Copyright © 2021 Soheila Kookalani. This is an open access paper distributed under the Creative Commons Attribution License. Journal of Civil Engineering and Materials Application is published by Pendar Pub; Journal p-ISSN 2676-332X; Journal e-ISSN 2588-2880.

[1] Richardson JN, Adriaenssens S, Coelho RF, Bouillard P. Coupled form-finding and grid optimization approach for single layer grid shells. Engineering structures. 2013 Jul 1;52:230-9. [View at Google Scholar]; [View at Publisher].
[2] Bouhaya L, Baverel O, Caron JF. Optimization of gridshell bar orientation using a simplified genetic approach. Structural and Multidisciplinary Optimization. 2014 Nov 1;50(5):839-48. [View at Google Scholar]; [View at Publisher].
[3] Adriaenssens S, Block P, Veenendaal D, Williams C, editors. Shell structures for architecture: form finding and optimization. Routledge; 2014 Mar 21. [View at Google Scholar]; [View at Publisher].
[4] D’Amico B, Kermani A, Zhang H. Form finding and structural analysis of actively bent timber grid shells. Engineering Structures. 2014 Dec 15;81:195-207. [View at Google Scholar]; [View at Publisher].
[5] Dimcic M. Structural Optimization of Grid Shells Based on Genetic Algorithms. Ph.D Thesis. 2011. [View at Google Scholar]; [View at Publisher].
[6] Dini M, Estrada G, Froli M, Baldassini N. Form-finding and buckling optimisation of gridshells using genetic algorithms. InProceedings of IASS Annual Symposia 2013 Sep 27 (Vol. 2013, No. 16, pp. 1-6). International Association for Shell and Spatial Structures (IASS). [View at Google Scholar]; [View at Publisher].
[7] Douthe C, Baverel O, Caron JF. Form-finding of a grid shell in composite materials. Journal of the International Association for Shell and Spatial structures. 2006 Apr 1;47(1):53-62. [View at Google Scholar]; [View at Publisher].
[8] Kookalani S, Cheng B, Xiang S. Shape optimization of GFRP elastic gridshells by the weighted Lagrange ε-twin support vector machine and multi-objective particle swarm optimization algorithm considering structural weight. InStructures 2021 Oct 1 (Vol. 33, pp. 2066-2084). Elsevier. [View at Google Scholar]; [View at Publisher].
[9] Du Peloux L, Baverel O, Caron JF, Tayeb F. From shape to shell: a design tool to materialize freeform shapes using gridshell structures. InDesign Modelling Symposium Berlin 2013 Sep 28. [View at Google Scholar]; [View at Publisher].
[10] Baverel O, Caron JF, Tayeb F, Du Peloux L. Gridshells in composite materials: construction of a 300 m2 forum for the solidays’ festival in Paris. Structural engineering international. 2012 Aug 1;22(3):408-14. [View at Google Scholar]; [View at Publisher].
[11] Bouhaya L. Structural optimization of gridshells. PhD thesis, Universit´e Paris-Est; 2010. [View at Publisher].
[12] Dimcic M, Knippers J. Free-form grid shell design based on genetic algorithms. In: Integration Through Computation - Proceedings of the 31st Annual Conference of the Association for Computer Aided Design in Architecture, ACADIA 2011. 2011. [View at Google Scholar]; [View at Publisher].
[13] Mesnil R, Ochsendorf J, Douthe C. Influence of the pre-stress on the stability of elastic grid shells. InProceedings of IASS Annual Symposia 2013 Sep 27 (Vol. 2013, No. 4, pp. 1-5). International Association for Shell and Spatial Structures (IASS). [View at Google Scholar]; [View at Publisher].
[14] Vapnik VN. The Nature of Statistical Learning Theory. Springer-Verlag. Adaptive and learning Systems for Signal Processing, Communications and Control. 1995. [View at Google Scholar]; [View at Publisher].
[15] Suykens JA, Van Gestel T, De Brabanter J, De Moor B, Vandewalle JP. Least squares support vector machines. World scientific; 2002 Nov 12. [View at Google Scholar]; [View at Publisher].
[16] Ahmadi Maleki M, Emami M. Application of SVM for Investigation of Factors Affecting Compressive Strength and Consistency of Geopolymer Concretes. Journal of civil Engineering and Materials Application. 2019 Jun 1;3(2):101-7. [View at Google Scholar]; [View at Publisher].
[17] Cheng MY, Hoang ND. Risk score inference for bridge maintenance project using evolutionary fuzzy least squares support vector machine. Journal of Computing in Civil Engineering. 2014 May 1;28(3):04014003. [View at Google Scholar]; [View at Publisher].
[18] Chou JS, Ngo NT, Pham AD. Shear strength prediction in reinforced concrete deep beams using nature-inspired metaheuristic support vector regression. Journal of Computing in Civil Engineering. 2016 Jan 1;30(1):04015002. [View at Google Scholar] ; [View at Publisher].
[19] Pal M, Deswal S. Support vector regression based shear strength modelling of deep beams. Computers & Structures. 2011 Jul 1;89(13-14):1430-9. [View at Google Scholar]; [View at Publisher].
[20] Vu DT, Hoang ND. Punching shear capacity estimation of FRP-reinforced concrete slabs using a hybrid machine learning approach. Structure and Infrastructure Engineering. 2016 Sep 1;12(9):1153-61. [View at Google Scholar]; [View at Publisher].
[21] Luo H, Paal SG. Machine learning–based backbone curve model of reinforced concrete columns subjected to cyclic loading reversals. Journal of Computing in Civil Engineering. 2018 Sep 1;32(5):04018042. [View at Google Scholar]; [View at Publisher].
[22] Chou JS, Pham AD. Smart artificial firefly colony algorithm‐based support vector regression for enhanced forecasting in civil engineering. Computer‐Aided Civil and Infrastructure Engineering. 2015 Sep;30(9):715-32. [View at Google Scholar]; [View at Publisher].
[23] Tayeb F, Caron JF, Baverel O, Du Peloux L. Stability and robustness of a 300 m2 composite gridshell structure. Construction and Building Materials. 2013 Dec 1;49:926-38. [View at Google Scholar]; [View at Publisher].
[24] Brown NC, Jusiega V, Mueller CT. Implementing data-driven parametric building design with a flexible toolbox. Automation in Construction. 2020 Oct 1;118:103252. [View at Google Scholar]; [View at Publisher].
[25] Quinn G, Gengnagel C. A review of elastic grid shells, their erection methods and the potential use of pneumatic formwork. Mobile and rapidly assembled structures IV. 2014 Jun 11;136:129. [View at Google Scholar]; [View at Publisher].
[26] Xiang S, Cheng B, Zou L, Kookalani S. An integrated approach of form finding and construction simulation for glass fiber‐reinforced polymer elastic gridshells. The Structural Design of Tall and Special Buildings. 2020 Apr 10;29(5):e1698. [View at Google Scholar]; [View at Publisher].
[27] Xiang S, Cheng B, Kookalani S. An analytic solution for form finding of GFRP elastic gridshells during lifting construction. Composite Structures. 2020 Jul 15;244:112290. [View at Google Scholar]; [View at Publisher].
[28] Xiang S, Cheng B, Kookalani S, Zhao J. An analytic approach to predict the shape and internal forces of barrel vault elastic gridshells during lifting construction. InStructures 2021 Feb 1 (Vol. 29, pp. 628-637). Elsevier. [View at Google Scholar]; [View at Publisher].
[29] Douthe C, Caron JF, Baverel O. Gridshell structures in glass fibre reinforced polymers. Construction and building materials. 2010 Sep 1;24(9):1580-9. [View at Google Scholar]; [View at Publisher].
[30] Suykens JA, Vandewalle J. Least squares support vector machine classifiers. Neural processing letters. 1999 Jun;9(3):293-300. [View at Google Scholar]; [View at Publisher].
[31] Van Gestel T, Suykens JA, Baesens B, Viaene S, Vanthienen J, Dedene G, De Moor B, Vandewalle J. Benchmarking least squares support vector machine classifiers. Machine learning. 2004 Jan;54(1):5-32. [View at Google Scholar]; [View at Publisher].
[32] Roushangar K, Saghebian SM, Mouaze D. Predicting characteristics of dune bedforms using PSO-LSSVM. International Journal of Sediment Research. 2017 Dec 1;32(4):515-26. [View at Google Scholar]; [View at Publisher].
[33] Chamkalani A, Zendehboudi S, Bahadori A, Kharrat R, Chamkalani R, James L, Chatzis I. Integration of LSSVM technique with PSO to determine asphaltene deposition. Journal of Petroleum Science and Engineering. 2014 Dec 1;124:243-53. [View at Google Scholar]; [View at Publisher].
[34] Junior FE, Yen GG. Particle swarm optimization of deep neural networks architectures for image classification. Swarm and Evolutionary Computation. 2019 Sep 1;49:62-74. [View at Google Scholar]; [View at Publisher].
[35] Hajabdollahi H, Ahmadi P, Dincer I. Thermoeconomic optimization of a shell and tube condenser using both genetic algorithm and particle swarm. International journal of refrigeration. 2011 Jun 1;34(4):1066-76. [View at Google Scholar]; [View at Publisher].
[36] Ren Z, Han H, Cui X, Qing H, Ye H. Application of PSO-LSSVM and hybrid programming to fault diagnosis of refrigeration systems. Science and Technology for the Built Environment. 2021 May 28;27(5):592-607. [View at Google Scholar]; [View at Publisher].
[37] ISECS International Colloquium on Computing, Communication, Control, and Management. Advancing Computing, Communication, Control and Management. Luo Q, editor. Berlin: Springer; 2010. [View at Google Scholar]; [View at Publisher].
 
Volume 5, Issue 3
September 2021
Pages 139-150
  • Receive Date: 15 July 2021
  • Revise Date: 01 September 2021
  • Accept Date: 04 September 2021
  • First Publish Date: 30 September 2021