| Title |
Automated Construction Progress Management Using Computer Vision-based CNN Model and BIM |
| Authors |
Rho, Juhee ; Park, Moonseo ; Lee, Hyun-Soo |
| DOI |
http://dx.doi.org/10.6106/KJCEM.2020.21.5.011 |
| Keywords |
Construction Progress Monitoring; BIM; CNN Model; GIPS; Computer Vision |
| Abstract |
A daily progress monitoring and further schedule management of a construction project have a significant impact on the construction manager’s decision making in schedule change and controlling field operation. However, a current site monitoring method highly relies on the manually recorded daily-log book by the person in charge of the work. For this reason, it is difficult to take a detached view and sometimes human error such as omission of contents may occur. In order to resolve these problems, previous researches have developed automated site monitoring method with the object recognition-based visualization or BIM data creation. Despite of the research results along with the related technology development, there are limitations in application targeting the practical construction projects due to the constraints in the experimental methods that assume the fixed equipment at a specific location. To overcome these limitations, some smart devices carried by the field workers can be employed as a medium for data creation. Specifically, the extracted information from the site picture by object recognition technology of CNN model, and positional information by GIPS are applied to update 4D BIM data. A standard CNN model is developed and BIM data modification experiments are conducted with the collected data to validate the research suggestion. Based on the experimental results, it is confirmed that the methods and performance are applicable to the construction site management and further it is expected to contribute speedy and precise data creation with the application of automated progress monitoring methods. |