Author
Agirbas, Asli
Publication Date
2024-09
Publication Place
-
Elsevier
Subject
Structural analysis, Deep learning, Instance segmentation, Mask RCNN, Reciprocal frame structures
Type
Periodical
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
0926-5805
Record ID
9de5a97b-1e7d-4e91-84b5-cc86537d7ddf
Library Location
Architecture
Date
2024-09
Sample Text
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
