Architectural Drawing Recognition A case study for training the learning algorithm with architectural plan and section drawing images

Uzun, Can; Çolakoğlu, Meryem Birgül

doi:10.5151/proceedings-ecaadesigradi2019_171

Cited by 2 publications

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“…There are also some classi cation studies for deep learning of architectural design images, for example, Can et al developed an algorithm for training using a dataset of architectural drawings ( oor plans and pro les) based on tagged pixels 15 . After some iterations, it is able to classify the pixel-based oor plans and sections better.…”

Section: 2literature Reviewmentioning

confidence: 99%

Intelligent auxiliary design of subway station building space based on deep learning

Jiang

zhang

et al. 2023

Preprint

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This paper aims to study the generation of spatial function layout of subway stations assisted by deep learning, and train the point cloud data of subway stations based on the Pointnet + + model in deep learning. The point cloud data of subway stations comes from the subway station data of large and medium-sized cities collected and processed by the author. After training and verification, the following conclusions are drawn: (1) It has been verified that the spatial deep learning of buildings in the form of point clouds is highly feasible, and the point cloud format of the architectural space model is fully compatible under the Pointnet + + model. (2) Verified the effectiveness of Pointnet + + for semantic segmentation and prediction of subway station cloud information. The results show that the predicted data has 60%+MIOU (MeanIoU average intersection and union ratio) and 75%+Acc (Accuracy). This paper uses an interdisciplinary research method to combine deep learning of 3D point cloud data with architectural design, breaking through the current situation of using 2D images as research objects, and avoiding the application of "deep learning" to 2D images. Objects cannot accurately describe the limitations of 3D space, providing architects with more intuitive and diverse design assistance.

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Section: 2literature Reviewmentioning

confidence: 99%

Intelligent auxiliary design of subway station building space based on deep learning

Jiang

zhang

et al. 2023

Preprint

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Prediction and design of mechanical properties of origami-inspired braces based on machine learning

Cai,

Xu,

Chen

et al. 2024

AI Civ. Eng.

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In order to rapidly and accurately evaluate the mechanical properties of a novel origami-inspired tube structure with multiple parameter inputs, this study developed a method of designing origami-inspired braces based on machine learning models. Four geometric parameters, i.e., cross-sectional side length, plate thickness, crease weakening coefficient, and plane angles, were used to establish a mapping relationship with five mechanical parameters, including elastic stiffness, yield load, yield displacement, ultimate load, and ultimate displacement, all of which were calculated from load-displacement curves. Firstly, forward prediction models were trained and compared for single and multiple mechanical outputs. The parameter ranges were extended and refined to improve the predicted results by introducing the intrinsic mechanical relationships. Secondly, certain reverse prediction models were established to obtain the optimized design parameters. Finally, the design method of this study was verified in finite element methods. The design and analysis framework proposed in this study can be used to promote the application of other novel multi-parameter structures.

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Architectural Drawing Recognition A case study for training the learning algorithm with architectural plan and section drawing images

Cited by 2 publications

References 0 publications

Intelligent auxiliary design of subway station building space based on deep learning

Intelligent auxiliary design of subway station building space based on deep learning

Prediction and design of mechanical properties of origami-inspired braces based on machine learning

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