{"title":"An ontological assessment proposal for architectural outputs of generative adversarial network","authors":"Can Uzun, Raşit Eren Cangür","doi":"10.1108/ci-03-2023-0053","DOIUrl":null,"url":null,"abstract":"\nPurpose\nThis 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.\n\n\nDesign/methodology/approach\nThe 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.\n\n\nFindings\nThe 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.\n\n\nOriginality/value\nThis 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.\n","PeriodicalId":45580,"journal":{"name":"Construction Innovation-England","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2023-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Construction Innovation-England","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/ci-03-2023-0053","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
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.
目的:本研究提出了一种本体论方法来评估生成对抗网络的架构输出。本文旨在评估生成对抗网络在表示建筑知识方面的性能。设计/方法/方法提出的本体论评估包括五个步骤。这些步骤分别是:创建建筑数据集,为建筑数据集开发本体,使用提出的本体内的标签训练You Only Look Once对象检测,使用数据集中的图像训练StyleGAN算法,最后检测本体标签并计算StyleGAN生成的基于像素的建筑图像的本体关系。作者提出并计算本体论身份和本体论包含度量来评估stylegan生成的本体论标签。本研究以300张飘窗图像作为本体评估实验的建筑数据集。本体论评估通过检查建筑知识表示的有效性,为stylegan生成的建筑图像提供基于语义的查询。此外,这种本体效度同时揭示了建筑元素标签特定的失败率和成功率。原创性/价值本研究通过本体有效性检查,而不是仅仅进行基于像素的相似性检查,有助于生成对抗网络的评估过程;基于语义的查询可以将gan生成的、基于像素的建筑元素引入建筑、工程和建筑行业。