The innovative potential of Generative Pre-trained Transformers (GPTS) for quality inspections in Swedish construction projects
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| Titel: | The innovative potential of Generative Pre-trained Transformers (GPTS) for quality inspections in Swedish construction projects |
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| Autoren: | Kifokeris, Dimosthenis, 1988, Kohvakka, Jan, Koch, Christian, Aslanzadeh, Donia |
| Quelle: | En molntjänst med GPT-baserat projektstöd för kvalitetsäkringi byggprojekt för beställare, projektörer och entreprenörer European Conference on Computing in Construction, EC3 2024, Chania, Greece Proceedings of the European Conference on Computing in Construction. 2024:829-836 |
| Schlagwörter: | cloud service, Quality control, self-checks, Swedish construction projects, generative pre-trained transformer (GPT) |
| Beschreibung: | Approaching quality inspection plans in Swedish construction projects as mere checklists and minimizing the clients’ involvement, can reduce their value. We propose improving this process through a cloud service concept for clients, designers, and contractors, utilizing generative pre-trained transformer (GPT) AI. Methodologically, we synthesize literature insights on GPT uses for construction, and empirical inquiries on developing a quality self-inspection service. We posit that through this service, project knowledge, known quality defects and lessons-learned from previous cases can be better accessed and shared – potentially leading to time savings, suggesting best practices, and improving the collaboration among clients, designers, and contractors. |
| Dateibeschreibung: | electronic |
| Zugangs-URL: | https://research.chalmers.se/publication/542836 https://research.chalmers.se/publication/542361 https://research.chalmers.se/publication/542836/file/542836_Fulltext.pdf |
| Datenbank: | SwePub |
Nájsť tento článok vo Web of Science | Abstract: | Approaching quality inspection plans in Swedish construction projects as mere checklists and minimizing the clients’ involvement, can reduce their value. We propose improving this process through a cloud service concept for clients, designers, and contractors, utilizing generative pre-trained transformer (GPT) AI. Methodologically, we synthesize literature insights on GPT uses for construction, and empirical inquiries on developing a quality self-inspection service. We posit that through this service, project knowledge, known quality defects and lessons-learned from previous cases can be better accessed and shared – potentially leading to time savings, suggesting best practices, and improving the collaboration among clients, designers, and contractors. |
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| ISSN: | 26841150 |
| DOI: | 10.35490/EC3.2024.231 |