Hybrid Elevations using GAN Networks

Mohammad, Ali; Beorkrem, Christopher; Ellinger, Jefferson

doi:10.52842/conf.acadia.2019.370

Cited by 6 publications

(2 citation statements)

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“…Chan and Spaeth (2020) used the GAN for reevaluating architectural sketches; Huang and Zheng (2018) used GAN for reconstructing architectural drawings; Mohammad et al (2019) generated elevations; Zhang and Blasetti (2020) focused on 3D style transfer; As et al (2018), Cao and Ji (2021), Rodrigues et al (2021), Campo (2020) and Campo et al (2019) used GAN for generating architectural layouts. These studies use GAN to generate a new variant of architectural images which yields a creative method in architecture.…”

Section: Introductionmentioning

confidence: 99%

An ontological assessment proposal for architectural outputs of generative adversarial network

Uzun,

Cangür

2023

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Purpose This study presents an ontological approach to assess the architectural outputs of generative adversarial networks. This paper aims to assess the performance of the generative adversarial network in representing building knowledge. Design/methodology/approach The proposed ontological assessment consists of five steps. These are, respectively, creating an architectural data set, developing ontology for the architectural data set, training the You Only Look Once object detection with labels within the proposed ontology, training the StyleGAN algorithm with the images in the data set and finally, detecting the ontological labels and calculating the ontological relations of StyleGAN-generated pixel-based architectural images. The authors propose and calculate ontological identity and ontological inclusion metrics to assess the StyleGAN-generated ontological labels. This study uses 300 bay window images as an architectural data set for the ontological assessment experiments. Findings The ontological assessment provides semantic-based queries on StyleGAN-generated architectural images by checking the validity of the building knowledge representation. Moreover, this ontological validity reveals the building element label-specific failure and success rates simultaneously. Originality/value This study contributes to the assessment process of the generative adversarial networks through ontological validity checks rather than only conducting pixel-based similarity checks; semantic-based queries can introduce the GAN-generated, pixel-based building elements into the architecture, engineering and construction industry.

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Section: Introductionmentioning

confidence: 99%

An ontological assessment proposal for architectural outputs of generative adversarial network

Uzun,

Cangür

2023

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“…The labels are made out of a pair of images and their translated image [32]. Based on it, many studies in terms of recognizing and generating architectural elements [6,[33][34][35][36] and general architectural layouts [37][38][39] were presented and demonstrated the potential ability of such networks to learn image features in mapping relationships [40,41]. In terms of building physical performance improvement, researchers have developed several performance mapping models based on artificial neural networks.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach toward Energy-Effective Residential Building Floor Plan Generation

Wan

Zhao

et al. 2022

Sustainability

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The ability of deep learning has been tested to learn graphical features for building-plan generation. However, whether the deeper space allocation strategies can be obtained and thus reduce energy consumption has still not been investigated. In the present study, we aimed to train a neural network by employing a characterized sample set to generate a residential building floor plan (RBFP) for achieving energy reduction effects. The network is based on Pix2Pix, including two sub-models: functional segmentation layout (FSL) generation and building floor plan (BFP) generation. To better characterize the energy efficiency, 98 screened floor plans of Solar Decathlon (SD) entries were labeled as the sample set. The data augmentation method was adopted to improve the performance of the FSL sub-model after the preliminary testing. Three existing residential buildings were used as cases to observe whether the network-generated RBFP gained the effect of decreasing energy consumption with decent space allocation. The results showed that, under the same simulation settings and building exterior profile (BEP) conditions, the function arrangement of the generated scheme was more reasonable compared to the original scheme in each case. The annual total energy consumption was reduced by 13.38%, 12.74%, and 7.47%, respectively. In conclusion, trained by the sample set that characterizes energy efficiency, the RBFP generation network has a positive effect in both optimizing the space allocation and reducing energy consumption. The implemented data augmentation method can significantly improve the network’s training results with a small sample size.

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Exploring in the Latent Space of Design: A Method of Plausible Building Facades Images Generation, Properties Control and Model Explanation Base on StyleGAN2

Meng

2021

Proceedings of the 2021 DigitalFUTURES

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GAN has been widely applied in the research of architectural image generation. However, the quality and controllability of generated images, and the interpretability of model are still potential to be improved. In this paper, by implementing StyleGAN2 model, plausible building façade images could be generated without conditional input. In addition, by applying GANSpace to analysis the latent space, high-level properties could be controlled for both generated images and novel images outside of training set. At last, the generating and controlling process could be visualized with image embedding and PCA projection method, which could achieve unsupervised classification of generated images, and help to understand the correlation between the images and their latent vectors.

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Hybrid Elevations using GAN Networks

Cited by 6 publications

References 0 publications

An ontological assessment proposal for architectural outputs of generative adversarial network

An ontological assessment proposal for architectural outputs of generative adversarial network

A Deep Learning Approach toward Energy-Effective Residential Building Floor Plan Generation

Exploring in the Latent Space of Design: A Method of Plausible Building Facades Images Generation, Properties Control and Model Explanation Base on StyleGAN2

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