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GeoMotor: Design with Nature. Recognition of Geometries Using a Convolutional Neural-Network Approach (CNN)

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Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1296)

Abstract

This article conceptualizes a solid regular called GeoMotor capable of moving and directing the sediments of a mountain river and changing its geography. The GeoMotor manages to manipulate the directional growth of sediments in an artificial environment, unveiling emerging architectural structures. For this, an analog simulation of the mountain river flow was performed and provide data to understand the phenomenon. Subsequently, this data was used to train a neural network that recognizes the emerging architectural patterns. As future work, it is planned to improve the models to offer functionalities beyond the orthodox practices of traditional architectonic models.

Keywords

  • GeoMotor
  • CNN
  • Geometry
  • Design
  • Sediments

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  • DOI: 10.1007/978-3-030-63403-2_84
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Fig. 1.

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Acknowledgment

We want to thank Francisco de Paula Santader University for the support, its simulation and manufacturing laboratories SIMU_lab and Fab_lab, and for the special collaboration in this project of civil engineers Rosa M Fuentes and Yarley P Varón and system engineering students Jose Manolo Pinzón Hernández and Juan Camilo Hernández Parra.

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Correspondence to Juan Manuel Villa Carrero .

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Carrero, J.M.V., Cuadros, E.G.P. (2021). GeoMotor: Design with Nature. Recognition of Geometries Using a Convolutional Neural-Network Approach (CNN). In: Cheng, LY. (eds) ICGG 2020 - Proceedings of the 19th International Conference on Geometry and Graphics. ICGG 2021. Advances in Intelligent Systems and Computing, vol 1296. Springer, Cham. https://doi.org/10.1007/978-3-030-63403-2_84

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  • DOI: https://doi.org/10.1007/978-3-030-63403-2_84

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63402-5

  • Online ISBN: 978-3-030-63403-2

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