Machine Learning in Predicting Section Drawings Case of Anatolian Seljuk Kümbets

Orkan Zeynel Güzelci

doi:10.52842/conf.ecaade.2022.2.169

Machine Learning in Predicting Section Drawings Case of Anatolian Seljuk Kümbets

Orkan Zeynel Güzelci^*

^*Corresponding author for this work

University of Porto

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

Funerary structures called kümbet emerged as a unique typology during the Anatolian Seljuk period (1077-1307). This study introduces a machine learning (ML) based model to predict sections of kümbets to complete their missing parts. The proposed ML-based model employs the Pix2Pix method, which is a subset of conditional Generative Adversarial Networks (cGAN).The model is trained over a coupled dataset (interior space and exterior shell) of section drawings. Then, the model is validated by predicting overall shape (exterior shell) for a given input (interior space). The outcomes of the validation phase are evaluated objectively by using structural similarity method (SSIM). Initial findings of the implementation show that the proposed ML-based model has the potential to be used as a design decision support tool for further restitution and renovation works.

Original language	English
Title of host publication	eCAADe 2022 - Co-creating the Future
Subtitle of host publication	Inclusion in and through Design
Editors	Burak Pak, Gabriel Wurzer, Rudi Stouffs
Publisher	Education and research in Computer Aided Architectural Design in Europe
Pages	169-176
Number of pages	8
ISBN (Print)	9789491207334
DOIs	https://doi.org/10.52842/conf.ecaade.2022.2.169
Publication status	Published - 2022
Externally published	Yes
Event	40th Conference on Education and Research in Computer Aided Architectural Design in Europe, eCAADe 2022 - Ghent, Belgium Duration: 13 Sept 2022 → 16 Sept 2022

Publication series

Name	Proceedings of the International Conference on Education and Research in Computer Aided Architectural Design in Europe
Volume	2
ISSN (Print)	2684-1843

Conference

Conference	40th Conference on Education and Research in Computer Aided Architectural Design in Europe, eCAADe 2022
Country/Territory	Belgium
City	Ghent
Period	13/09/22 → 16/09/22

Bibliographical note

Publisher Copyright:
© 2022, Education and research in Computer Aided Architectural Design in Europe. All rights reserved.

Funding

This work is supported by national funds through FCT - Fundação para a Ciência e a Tecnologia, I.P., under the project UIDB/00145/2020.

Funders	Funder number
Fundação para a Ciência e a Tecnologia	UIDB/00145/2020

Keywords

Anatolian Seljuk Architecture
Kümbet
Machine Learning
Pix2Pix
Section

Access to Document

10.52842/conf.ecaade.2022.2.169

Cite this

Güzelci, O. Z. (2022). Machine Learning in Predicting Section Drawings Case of Anatolian Seljuk Kümbets. In B. Pak, G. Wurzer, & R. Stouffs (Eds.), eCAADe 2022 - Co-creating the Future: Inclusion in and through Design (pp. 169-176). (Proceedings of the International Conference on Education and Research in Computer Aided Architectural Design in Europe; Vol. 2). Education and research in Computer Aided Architectural Design in Europe. https://doi.org/10.52842/conf.ecaade.2022.2.169

Güzelci, Orkan Zeynel. / Machine Learning in Predicting Section Drawings Case of Anatolian Seljuk Kümbets. eCAADe 2022 - Co-creating the Future: Inclusion in and through Design. editor / Burak Pak ; Gabriel Wurzer ; Rudi Stouffs. Education and research in Computer Aided Architectural Design in Europe, 2022. pp. 169-176 (Proceedings of the International Conference on Education and Research in Computer Aided Architectural Design in Europe).

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title = "Machine Learning in Predicting Section Drawings Case of Anatolian Seljuk K{\"u}mbets",

abstract = "Funerary structures called k{\"u}mbet emerged as a unique typology during the Anatolian Seljuk period (1077-1307). This study introduces a machine learning (ML) based model to predict sections of k{\"u}mbets to complete their missing parts. The proposed ML-based model employs the Pix2Pix method, which is a subset of conditional Generative Adversarial Networks (cGAN).The model is trained over a coupled dataset (interior space and exterior shell) of section drawings. Then, the model is validated by predicting overall shape (exterior shell) for a given input (interior space). The outcomes of the validation phase are evaluated objectively by using structural similarity method (SSIM). Initial findings of the implementation show that the proposed ML-based model has the potential to be used as a design decision support tool for further restitution and renovation works.",

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Güzelci, OZ 2022, Machine Learning in Predicting Section Drawings Case of Anatolian Seljuk Kümbets. in B Pak, G Wurzer & R Stouffs (eds), eCAADe 2022 - Co-creating the Future: Inclusion in and through Design. Proceedings of the International Conference on Education and Research in Computer Aided Architectural Design in Europe, vol. 2, Education and research in Computer Aided Architectural Design in Europe, pp. 169-176, 40th Conference on Education and Research in Computer Aided Architectural Design in Europe, eCAADe 2022, Ghent, Belgium, 13/09/22. https://doi.org/10.52842/conf.ecaade.2022.2.169

Machine Learning in Predicting Section Drawings Case of Anatolian Seljuk Kümbets. / Güzelci, Orkan Zeynel.
eCAADe 2022 - Co-creating the Future: Inclusion in and through Design. ed. / Burak Pak; Gabriel Wurzer; Rudi Stouffs. Education and research in Computer Aided Architectural Design in Europe, 2022. p. 169-176 (Proceedings of the International Conference on Education and Research in Computer Aided Architectural Design in Europe; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Funerary structures called kümbet emerged as a unique typology during the Anatolian Seljuk period (1077-1307). This study introduces a machine learning (ML) based model to predict sections of kümbets to complete their missing parts. The proposed ML-based model employs the Pix2Pix method, which is a subset of conditional Generative Adversarial Networks (cGAN).The model is trained over a coupled dataset (interior space and exterior shell) of section drawings. Then, the model is validated by predicting overall shape (exterior shell) for a given input (interior space). The outcomes of the validation phase are evaluated objectively by using structural similarity method (SSIM). Initial findings of the implementation show that the proposed ML-based model has the potential to be used as a design decision support tool for further restitution and renovation works.

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ER -

Güzelci OZ. Machine Learning in Predicting Section Drawings Case of Anatolian Seljuk Kümbets. In Pak B, Wurzer G, Stouffs R, editors, eCAADe 2022 - Co-creating the Future: Inclusion in and through Design. Education and research in Computer Aided Architectural Design in Europe. 2022. p. 169-176. (Proceedings of the International Conference on Education and Research in Computer Aided Architectural Design in Europe). doi: 10.52842/conf.ecaade.2022.2.169

Machine Learning in Predicting Section Drawings Case of Anatolian Seljuk Kümbets

Abstract

Publication series

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Bibliographical note

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Keywords

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