Semi-automated creation of reciprocal frame structures using deep learning

Name: Semi-automated creation of reciprocal frame structures using deep learning
Author: Agirbas, Asli

İsim	Semi-automated creation of reciprocal frame structures using deep learning
Yazar	Agirbas, Asli
Basım Tarihi:	2024-09
Basım Yeri	- Elsevier
Konu	Structural analysis, Deep learning, Instance segmentation, Mask RCNN, Reciprocal frame structures
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	0926-5805
Kayıt Numarası	9de5a97b-1e7d-4e91-84b5-cc86537d7ddf
Lokasyon	Architecture
Tarih	2024-09
Örnek Metin	Systems that can transform two-dimensional (2D) sketches into 3D models while performing structural analyses are necessary for architectural sketches. To address this challenge, this paper focuses on how deep-learning algorithms can aid in this transformation process. It presents a model that uses the instance-segmentation technique with Mask RCNN to detect and distinguish two types of short beams of reciprocal frame structures (RFs) in 2D sketches and uses this information in the systematic creation of a 3D model of RFs to conduct their structural analysis. The results indicate that the model is capable of clustering beam types in 2D sketches via masking and classifying, eliminating irrelevant background objects, creating parametric RFs using masking information, and performing structural analysis. The model, which helps optimise and ease the design process, can be used by architects or engineers. This paper will inspire future work on the creation of integrated modelling systems.
DOI	10.1016/j.autcon.2024.105515
Cilt	165

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Semi-automated creation of reciprocal frame structures using deep learning

Yazar Agirbas, Asli

Basım Tarihi 2024-09

Basım Yeri - Elsevier

Konu Structural analysis, Deep learning, Instance segmentation, Mask RCNN, Reciprocal frame structures

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 0926-5805

Kayıt Numarası 9de5a97b-1e7d-4e91-84b5-cc86537d7ddf

Lokasyon Architecture

Tarih 2024-09

Örnek Metin Systems that can transform two-dimensional (2D) sketches into 3D models while performing structural analyses are necessary for architectural sketches. To address this challenge, this paper focuses on how deep-learning algorithms can aid in this transformation process. It presents a model that uses the instance-segmentation technique with Mask RCNN to detect and distinguish two types of short beams of reciprocal frame structures (RFs) in 2D sketches and uses this information in the systematic creation of a 3D model of RFs to conduct their structural analysis. The results indicate that the model is capable of clustering beam types in 2D sketches via masking and classifying, eliminating irrelevant background objects, creating parametric RFs using masking information, and performing structural analysis. The model, which helps optimise and ease the design process, can be used by architects or engineers. This paper will inspire future work on the creation of integrated modelling systems.

DOI 10.1016/j.autcon.2024.105515

Cilt 165