| Title |
A Deep Learning Model to Predict BIM Execution Difficulty Based on Bidding Texts in Construction Projects |
| Authors |
김정수(Kim, Jeongsoo) ; 문현석(Moon, Hyounseok) ; 박상미(Park, Sangmi) |
| DOI |
https://doi.org/10.12652/Ksce.2023.43.6.0851 |
| Keywords |
딥러닝; BIM 수행 난이도; 입찰 텍스트; 난이도 분석 Deep learning; BIM execution difficulty; Bidding texts; Difficulty analysis |
| Abstract |
The mandatory use of BIM(Building Information Model) in larger Korean public construction projects necessitates participants to have a comprehensive understanding of the relevant procedures and technologies, especially during the bidding stage. However, most small and medium-sized construction and engineering companies possess limited BIM proficiency and understanding. This hampers their ability to recognize bidding requirements and make informed decisions. To address this challenge, our study introduces a method to gauge the complexity of BIM requirements in bidding documents. This is achieved by integrating a morphological analyzer, which encompasses BIM bidding terminology, with a deep learning model. We investigated the effects of the parameters in our proposed deep learning model and examined its predictive validity. The results revealed an F1-score of 0.83 for the test data, indicating that the model's predictions align closely with the actual BIM performance challenges. |