Özet
The Request for Information (RFI) is a vital communication tool in construction projects, aiding teams in addressing queries and navigating challenges. Unstructured RFIs hinder manual analysis for extracting hidden knowledge. Prior research in RFI analysis employed NLP and text clustering and often relied on a single clustering method. This paper performs a comparative analysis using diverse clustering methods (LDA, NMF, and K-means) and visualization techniques to determine the most suitable methods. The study offers project managers and quality engineers an effective tool for extracting hidden knowledge in RFI.
Orijinal dil | İngilizce |
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Ana bilgisayar yayını başlığı | Proceedings of the 2024 European Conference on Computing in Construction |
Editörler | Marijana Srećković, Mohamad Kassem, Ranjith Soman, Athanasios Chassiakos |
Yayınlayan | European Council on Computing in Construction (EC3) |
Sayfalar | 144-151 |
Sayfa sayısı | 8 |
ISBN (Basılı) | 9789083451305 |
DOI'lar | |
Yayın durumu | Yayınlandı - 2024 |
Etkinlik | European Conference on Computing in Construction, EC3 2024 - Chania, Greece Süre: 14 Tem 2024 → 17 Tem 2024 |
Yayın serisi
Adı | Proceedings of the European Conference on Computing in Construction |
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Hacim | 2024 |
ISSN (Elektronik) | 2684-1150 |
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???event.eventtypes.event.conference??? | European Conference on Computing in Construction, EC3 2024 |
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Ülke/Bölge | Greece |
Şehir | Chania |
Periyot | 14/07/24 → 17/07/24 |
Bibliyografik not
Publisher Copyright:© 2024 European Council on Computing in Construction.