DOI: 10.1007/978-3-030-29829-6_2. An Interactive Structural Optimization of Space Frame Structures Using Machine Learning

An Interactive Structural Optimization of Space Frame Structures Using Machine Learning

10.1007/978-3-030-29829-6_2

Crossref book-chapter

Springer International Publishing

Impact: Design With All Senses (297)

Bibliography

AksÃ¶z, Z., & Preisinger, C. (2019). An Interactive Structural Optimization of Space Frame Structures Using Machine Learning. Impact: Design With All Senses, 18â31.

Authors 2

Zeynep Aksöz (first)
Clemens Preisinger (additional)

References 16 Referenced 8

Hajela, P., Berke, L.: Neural network-based decomposition in optimal structural synthesis. Comput. Syst. Eng. 2(5–6), 473–481 (1991) (10.1016/0956-0521(91)90050-F) / Comput. Syst. Eng. by P Hajela (1991)
Holland, J.: Genetic algorithms. Sci. Am. 267(1), 66–72 (1992) (10.1038/scientificamerican0792-66) / Sci. Am. by J Holland (1992)
Brown, N., Mueller, C.: Design variable analysis and generation for performance-based parametric modeling in architecture. Int. J. Archit. Comput. 17(1), 36–52 (2018) (10.1177/1478077118799491) / Int. J. Archit. Comput. by N Brown (2018)
Tseranidis, S., Brown, N., Mueller, C.: Data-driven approximation algorithms for rapid performance evaluation and optimization of civil structures. Autom. Constr. 72, 279–293 (2016) (10.1016/j.autcon.2016.02.002) / Autom. Constr. by S Tseranidis (2016)
Swift, R.A., Batil, S.M.: Application of neural networks to preliminary structural design. In: 32nd Structures, Structural Dynamics, and Materials Conference, Baltimore, MD, U.S.A. (1991) (10.2514/6.1991-1038)
Hanna, S.: Inductive machine learning of optimal modular structures: estimating solutions using support vector machines. AI EDAM 21(4), 351–366 (2007) / AI EDAM by S Hanna (2007)
Preisinger, C.: Linking structure and parametric geometry. Archit. Des. 83, 110–113 (2013) / Archit. Des. by C Preisinger (2013)
Mitchell, T.: The Discipline of Machine Learning. School of Computer Science, Carnegie Mellon University Pittsburgh, PA, (2006)
Dreyfus, S.: Artificial neural networks, back propagation and the Kelley-Bryson gradient procedure. J. Guidance Control Dyn. 13, 926–928 (1990) (10.2514/3.25422) / J. Guidance Control Dyn. by S Dreyfus (1990)
Grosse, R.: Lecture 15: Exploding and Vanishing Gradients (2017). http://www.cs.toronto.edu/~rgrosse/courses/csc321_2017/readings/L15ExplodingandVanishingGradients.pdf
Stanley, K.O.: Compositional pattern producing networks: a novel abstraction of development. Genet. Program. Evolvable Mach. 8(2), 131–162 (2007) (10.1007/s10710-007-9028-8) / Genet. Program. Evolvable Mach. by KO Stanley (2007)
Vierlinger, R.: Towards Ai Drawing Agents (2015) (10.1007/978-3-319-24208-8_30)
Wagarachchi, M., Karunananda, A.: Optimization of artificial neural network architecture using neuroplasticity. Int. J. Artif. Intell. 15(1), 112–125 (2017) / Int. J. Artif. Intell. by M Wagarachchi (2017)
Towards Data Science. https://towardsdatascience.com/grid-search-for-model-tuning-3319b259367e . Accessed 05 Apr 2019
MIT Online Lecture Notes. http://www.mit.edu/~6.s085/notes/lecture3.pdf . Accessed 05 Apr 2019
Linear regression. En.wikipedia.org. https://en.wikipedia.org/wiki/Linear_regression . Accessed 07 June 2019

Dates

Type	When
Created	5 years, 11 months ago (Aug. 28, 2019, 5:07 a.m.)
Deposited	5 years, 3 months ago (May 5, 2020, 7:11 a.m.)
Indexed	3 months, 1 week ago (May 8, 2025, 10:43 p.m.)
Issued	5 years, 11 months ago (Aug. 29, 2019)
Published	5 years, 11 months ago (Aug. 29, 2019)
Published Online	5 years, 11 months ago (Aug. 29, 2019)
Published Print	5 years, 7 months ago (Jan. 1, 2020)

Funders 0

None

BibTeX

@inbook{Aks_z_2019, title={An Interactive Structural Optimization of Space Frame Structures Using Machine Learning}, ISBN={9783030298296}, url={http://dx.doi.org/10.1007/978-3-030-29829-6_2}, DOI={10.1007/978-3-030-29829-6_2}, booktitle={Impact: Design With All Senses}, publisher={Springer International Publishing}, author={Aksöz, Zeynep and Preisinger, Clemens}, year={2019}, month=aug, pages={18–31} }

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