Understanding the Impact of Deep Learning Models on Building Information Modeling Systems: a Study on Generative Artificial Intelligence Tools †
| dc.contributor.author | Yönder,V.M. | |
| dc.date.accessioned | 2024-09-24T15:55:55Z | |
| dc.date.available | 2024-09-24T15:55:55Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | The power of the relationship between building information modeling (BIM) systems and advanced artificial intelligence models holds considerable weight for users of BIM. This relationship allows the generation, analysis, and deduction of insights from substantial construction digital data. This research explores the relationship between generative artificial intelligence (generative AI), deep neural nets, and the BIM systems, including its users. This study examines the correlation between generative artificial intelligence and BIM methodology by conducting a case study. Furthermore, this paper investigates the conceptual and practical use of generative AI components (e.g., text-to-image models, diffusion networks, deep neural networks, large language model, and generative adversarial network) in BIM systems via bibliometric analysis. © 2023 by the author. | en_US |
| dc.identifier.doi | 10.3390/IOCBD2023-15381 | |
| dc.identifier.issn | 2673-4591 | |
| dc.identifier.scopus | 2-s2.0-85197265976 | |
| dc.identifier.uri | https://doi.org/10.3390/IOCBD2023-15381 | |
| dc.identifier.uri | https://hdl.handle.net/11147/14803 | |
| dc.language.iso | en | en_US |
| dc.publisher | Multidisciplinary Digital Publishing Institute (MDPI) | en_US |
| dc.relation.ispartof | Engineering Proceedings | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | bibliometric analysis | en_US |
| dc.subject | BIM designer | en_US |
| dc.subject | deep neural networks | en_US |
| dc.subject | generative artificial intelligence (GenAI/GAI) | en_US |
| dc.subject | text-to-image models | en_US |
| dc.title | Understanding the Impact of Deep Learning Models on Building Information Modeling Systems: a Study on Generative Artificial Intelligence Tools † | en_US |
| dc.type | Article | en_US |
| dspace.entity.type | Publication | |
| gdc.author.institutional | Yönder,V.M. | |
| gdc.author.scopusid | 58612439800 | |
| gdc.bip.impulseclass | C5 | |
| gdc.bip.influenceclass | C5 | |
| gdc.bip.popularityclass | C5 | |
| gdc.coar.access | open access | |
| gdc.coar.type | text::journal::journal article | |
| gdc.collaboration.industrial | false | |
| gdc.description.department | Izmir Institute of Technology | en_US |
| gdc.description.departmenttemp | Yönder V.M., Architecture Faculty, Izmir Institute of Technology, Gülbahçe Campus, Izmir, 35433, Turkey | en_US |
| gdc.description.issue | 1 | en_US |
| gdc.description.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| gdc.description.scopusquality | Q4 | |
| gdc.description.volume | 53 | en_US |
| gdc.description.wosquality | N/A | |
| gdc.identifier.openalex | W4392478894 | |
| gdc.index.type | Scopus | |
| gdc.oaire.accesstype | GOLD | |
| gdc.oaire.diamondjournal | false | |
| gdc.oaire.impulse | 1.0 | |
| gdc.oaire.influence | 2.6459237E-9 | |
| gdc.oaire.isgreen | false | |
| gdc.oaire.keywords | text-to-image models | |
| gdc.oaire.keywords | Engineering machinery, tools, and implements | |
| gdc.oaire.keywords | bibliometric analysis | |
| gdc.oaire.keywords | deep neural networks | |
| gdc.oaire.keywords | BIM designer | |
| gdc.oaire.keywords | generative artificial intelligence (GenAI/GAI) | |
| gdc.oaire.keywords | TA213-215 | |
| gdc.oaire.popularity | 3.5106145E-9 | |
| gdc.oaire.publicfunded | false | |
| gdc.oaire.sciencefields | 0209 industrial biotechnology | |
| gdc.oaire.sciencefields | 0211 other engineering and technologies | |
| gdc.oaire.sciencefields | 02 engineering and technology | |
| gdc.openalex.collaboration | National | |
| gdc.openalex.fwci | 0.42841654 | |
| gdc.openalex.normalizedpercentile | 0.59 | |
| gdc.opencitations.count | 0 | |
| gdc.plumx.crossrefcites | 1 | |
| gdc.plumx.mendeley | 35 | |
| gdc.plumx.newscount | 1 | |
| gdc.plumx.scopuscites | 0 | |
| gdc.scopus.citedcount | 0 | |
| relation.isAuthorOfPublication.latestForDiscovery | 2cddd197-f127-4b3f-83ac-c014a7bdeb4d | |
| relation.isOrgUnitOfPublication.latestForDiscovery | 9af2b05f-28ac-4003-8abe-a4dfe192da5e |
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