Performative Structural Design Optimization: Generative Algorithm for a Preliminary Study of a Voided Beam
Abstract
:1. Introduction
1.1. Environmental and Economic Advantages: The Need for Structural Optimization
1.2. Voided Arch-Shaped Box Girder
1.3. Merging MOOPs and Form Finding for a Performative Concrete Shell Structure
2. Introduction to Performative Structural Design Optimization Method
2.1. Computational Design Using Grasshopper
2.2. Finite Element Analysis
3. The Multi-Objective Optimization Problem
Optimization Results—Octopus Solver (HypE Reduction)
4. Form-Finding Using Kangaroo Engine
Kangaroo Solver Results
5. Conclusions
Author Contributions
Funding
Informed Consent Statement
Conflicts of Interest
References
- Simon, H.A. Theories of Bounded Rationality. In Decision and Organization: A Volume in Honor of Jacob Marschak; McGuire, C.B., Radner, R., Eds.; North-Holland Publishing Company: Amsterdam, The Netherlands; London, UK, 1972. [Google Scholar]
- Svoboda, L.; Novák, J.; Kurilla, L.; Zeman, J. A framework for integrated design of algorithmic architectural forms. Adv. Eng. Softw. 2013, 72, 109–118. [Google Scholar] [CrossRef]
- Myers, B.A. Visual programming, programming by example, and program visualization: A taxonomy. Assoc. Comput. Mach. 1986, 17, 59–66. [Google Scholar] [CrossRef]
- Kuhail, M.A.; Farooq, S.; Hammad, R.; Bahja, M. Characterizing visual programming approaches for end-user developers: A systematic review. IEEE Access 2021, 9, 14181–14202. [Google Scholar] [CrossRef]
- Shea, K.; Aish, R.; Gourtovaia, M. Towards integrated performance-driven generative design tools. Autom. Constr. 2005, 14, 253–264. [Google Scholar] [CrossRef]
- Elbeltagi, E.; Wefki, H.; Abdrabou, S.; Dawood, M.; Ramzy, A. Visualized strategy for predicting buildings energy consumption during early design stage using parametric analysis. J. Build. Eng. 2017, 13, 127–136. [Google Scholar] [CrossRef]
- Seghier, T.E.; Lim, Y.W.; Ahmad, M.H.; Samuel, W.O. Building Envelope Thermal Performance Assessment Using Visual Programming and BIM, based on ETTV requirement of Green Mark and GreenRE. Int. J. Built. Environ. Sustain. 2017, 4, 227–235. [Google Scholar] [CrossRef]
- Ho-Nguyen-Tan, T.; Kim, H.G. An efficient method for shape and topology optimization of shell structures. Struct. Multidiscip. Optim. 2022, 65, 119. [Google Scholar] [CrossRef]
- Ho-Nguyen-Tan, T.; Kim, H.G. Level set-based topology optimization for compliance and stress minimization of shell structures using trimmed quadrilateral shell meshes. Comput. Struct. 2022, 259, 106695. [Google Scholar] [CrossRef]
- Aydın, Z.; Ayvaz, Y. Optimum topology and shape design of prestressed concrete bridge girders using a genetic algorithm. Struct. Multidiscip. Optim. 2010, 41, 151–162. [Google Scholar] [CrossRef]
- Sardone, L.; Fiore, A.; Greco, R.; Moccia, C.; Lagaros, N.D.; De Tommasi, D. Algorithm-Aided Structural-Optimization Strategies for the Design of Variable Cross-Section Beams. In Proceedings of the International Fib Symposium on the Conceptual Design of Structures 2021, Attisholz, Switzerland, 16–18 September 2021; Volume 267589, pp. 485–492. [Google Scholar] [CrossRef]
- Lee, S.-J. Optimal Shape Finding of Arch Structures. J. Archit. Inst. Korea 2022, 38, 223–233. [Google Scholar]
- Upadhyay, B.D.; Sonigra, S.S.; Daxini, S.D. Numerical analysis perspective in structural shape optimization: A review post 2000. Adv. Eng. Softw. 2021, 155, 102992. [Google Scholar] [CrossRef]
- Bader, J.; Zitzler, E. HypE: An Algorithm for Fast Hypervolume-Based Many-Objective Optimization. Evol. Comput. 2011, 19, 45–76. [Google Scholar] [CrossRef] [PubMed]
- Ajeel, A.E.; Qaseem, T.A.; Rasheed, S.R. Structural Behavior of Voided Reinforced Concrete Beams Under Combined Moments. Civ. Environ. Res. 2018, 10, 17–24. [Google Scholar]
- Lagaros, N.D. The environmental and economic impact of structural optimization. Struct. Multidiscip. Optim. 2018, 58, 1751–1765. [Google Scholar]
- Ajeel, A.E. Torsion Plus Bending and Shear on Reinforced Concrete Beams. J. Eng. Sustain. Dev. 2016, 20, 277–288. [Google Scholar]
- Yilmaz, D.; Soyluk, K. Comparative analysis of steel arch bridges under near-fault ground motion effects of directivity-pulse and fling-step. J. Struct. Eng. Appl. Mech. 2019, 2, 63–74. [Google Scholar] [CrossRef]
- Billington, D.P. Robert Maillart: Builder, Designer, and Artist; Cambridge University Press: Cambridge, UK, 1997. [Google Scholar]
- Ter Maten, R.N. Ultra-High-Performance Concrete in Large Span Shell Structures; Delft University of Technology: Rotterdam, The Netherlands, 2011. [Google Scholar]
- Tamplin, R.; Iuorio, O. Challenges in designing and fabrication of a thin concrete shell. Creativity in Structural Design. In Proceedings of the IASS Symposium 2018, Boston, MA, USA, 16–20 July 2018. [Google Scholar]
- Ekici, B.; Cubukcuoglu, C.; Turrin, M.; Sariyildiz, I.S. Performative computational architecture using swarm and evolutionary optimisation: A review. Build. Environ. 2019, 147, 356–371. [Google Scholar]
- Sariyildiz, S. Performative Computational Design. In Proceedings of the ICONARCH-I: International Congress of Architecture-I, Konya, Turkey, 15–17 November 2012; pp. 313–344. [Google Scholar]
- Sardone, L.; Greco, R.; Fiore, A.; Moccia, C.; De Tommasi, D.; Lagaros, N.D. A preliminary study on a variable section beam through Algorithm-Aided Design: A way to connect architectural shape and structural optimization. Procedia Manuf. 2020, 44, 497–504. [Google Scholar]
- Dapogny, C.; Frey, P.; Omnès, F.; Privat, Y. Geometrical shape optimization in fluid mechanics using FreeFem++. Struct. Multidiscip. Optim. 2018, 58, 2761–2788. [Google Scholar]
- Akbari, M.; Asadi, P.; Besharati-Givi, M.K.; Khodabandehlouie, G. Artificial neural network and optimization. In Advances in Friction-Stir Welding and Processing; Woodhead Publishing: Cambridge, UK, 2014; pp. 543–599. [Google Scholar] [CrossRef]
- Zitzler, E.; Laumanns, M.; Thiele, L. SPEA2: Improving the Strength Pareto Evolutionary Algorithm; TIK-Report; ETH Zurich, Computer Engineering and Networks Laboratory: Zurich, Switzerland, 2001; p. 103. [Google Scholar] [CrossRef]
- Kilian, A.; Ochsendorf, J. Particle-spring systems for structural form finding. J. Int. Assoc. Shell Spat. Struct. 2005, 46, 77–84. [Google Scholar]
- Adriaenssens, S.M.L.; Barnes, M.R. Tensegrity spline beam and grid shell structures. Eng. Struct. 2001, 23, 29–36. [Google Scholar] [CrossRef]
- Piker, D. Kangaroo: Form finding with computational physics. Archit. Des. 2013, 83, 136–167. [Google Scholar] [CrossRef]
Test Case | Displacement (cm) | Mass (Kg) | Sig1-Val [Tensile Stress] (kN/cm2) | Sig2-Val [Compressive Stress] (kN/cm2) |
---|---|---|---|---|
Solid beam | 0.33 | 437,500 | 0 to 1.119294 | −1.033997 to 0 |
1.13 | 191,044 | 0 to 1.429453 | −1.922351 to 0 |
Test Case | Displacement (cm) | Mass (Kg) | Sig1-Valmax [Tensile Stressmax] (kN/cm2) |
---|---|---|---|
Solid beam | 0.33 | 437,500 | 1.119294 |
Test 1 | 0.36 | 123,932.7 | 0.23137 |
Test 2 | 0.51 | 86,963.8 | 0.283788 |
Test 3 | 0.56 | 84,206.1 | 0.336129 |
Test Case | Displacement (%) | Mass (%) | Sig1-Valmax [Tensile Stressmax] (%) |
---|---|---|---|
Test 1 | +9.1 | −71 | −97.9 |
Test 2 | +54 | −80.1 | −74.6 |
Test 3 | +69.7 | −80.8 | −70 |
Test Case | Displacement (cm) | Mass (kg) | Sig1-Valmax [Tensile Stressmax] (kN/cm2) |
---|---|---|---|
Kangaroo results | 0.59 | 93,973.8 | 0.143538 |
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Sardone, L.; Fiore, A.; Manuello, A.; Greco, R. Performative Structural Design Optimization: Generative Algorithm for a Preliminary Study of a Voided Beam. Appl. Sci. 2022, 12, 8663. https://doi.org/10.3390/app12178663
Sardone L, Fiore A, Manuello A, Greco R. Performative Structural Design Optimization: Generative Algorithm for a Preliminary Study of a Voided Beam. Applied Sciences. 2022; 12(17):8663. https://doi.org/10.3390/app12178663
Chicago/Turabian StyleSardone, Laura, Alessandra Fiore, Amedeo Manuello, and Rita Greco. 2022. "Performative Structural Design Optimization: Generative Algorithm for a Preliminary Study of a Voided Beam" Applied Sciences 12, no. 17: 8663. https://doi.org/10.3390/app12178663
APA StyleSardone, L., Fiore, A., Manuello, A., & Greco, R. (2022). Performative Structural Design Optimization: Generative Algorithm for a Preliminary Study of a Voided Beam. Applied Sciences, 12(17), 8663. https://doi.org/10.3390/app12178663