Abstract
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.
| Original language | English |
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| Title of host publication | Proceedings of the 2024 European Conference on Computing in Construction |
| Editors | Marijana Srećković, Mohamad Kassem, Ranjith Soman, Athanasios Chassiakos |
| Publisher | European Council on Computing in Construction (EC3) |
| Pages | 144-151 |
| Number of pages | 8 |
| ISBN (Print) | 9789083451305 |
| DOIs | |
| Publication status | Published - 2024 |
| Event | European Conference on Computing in Construction, EC3 2024 - Chania, Greece Duration: 14 Jul 2024 → 17 Jul 2024 |
Publication series
| Name | Proceedings of the European Conference on Computing in Construction |
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| Volume | 2024 |
| ISSN (Electronic) | 2684-1150 |
Conference
| Conference | European Conference on Computing in Construction, EC3 2024 |
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| Country/Territory | Greece |
| City | Chania |
| Period | 14/07/24 → 17/07/24 |
Bibliographical note
Publisher Copyright:© 2024 European Council on Computing in Construction.