| Title |
Advanced CBS (Cost Breakdown Structure) Code Search Technology Applying NLP (Natural Language Processing) of Artificial Intelligence |
| Authors |
김한도(Kim, HanDo) ; 남정용(Nam, JeongYong) |
| DOI |
https://doi.org/10.12652/Ksce.2024.44.5.0719 |
| Keywords |
5D BIM; WBS; CBS; 자연어처리; TF-IDF 5D BIM; WBS; CBS; NLP; TF-IDF |
| Abstract |
For efficient construction management, linking BIM with schedule and cost is essential, but there are limits to the application of 5D BIM due to the difficulty in disassembling thousands of WBS and CBS. To solve this problem, a standardized WBS-CBS set is configured in advance, and when a new construction project occurs, the CBS in the BOQ is automatically linked to the WBS when a text most similar to it is found among the standard CBS (Public Procurement Service standard construction code) of the already linked set. A method was used to compare the text similarity of CBS more efficiently using artificial intelligence natural language processing techniques. Firstly, we created a civil term dictionary (CTD) that organized the words used in civil projects and assigned numerical values, tokenized the text of all CBS into words defined in the dictionary, converted them into TF-IDF vectors, and determined them by cosine similarity. Additionally, the search success rate increased to nearly 70 % by considering CBS' hierarchical structure and changing keywords. The threshold value for judging similarity was 0.62 (1: perfect match, 0: no match). |