| Title |
Predicting Curtain Wall Works Duration in High-rise Building Projects Using Data Augmentation Techniques and Support Vector Machines |
| Authors |
윤혜순(Yoon, Hye-Soon) ; 백영건(Beak, Young-Gun) ; 박상준(Park, Sang-Jun) ; 장재호(Jang, Jae-Ho) ; 김주형(Kim, Ju-Hyung) |
| DOI |
https://doi.org/10.5659/JAIK.2024.40.12.87 |
| Keywords |
Data Augmentation Technique; Machine Learning; Construction Duration |
| Abstract |
Predicting the duration of high-rise building projects is essential for managing progress and identifying potential delays, especially for
activities on the critical path that can disrupt schedules. Curtain wall installation is one of the key tasks that often cause delays. Although
data-driven approaches show promise for accurate predictions, data scarcity in South Korea limits their application. To address this, exploring
new data augmentation techniques and prediction methods is necessary. This study compares three Monte Carlo simulation (MCS) variants
and the Synthetic Minority Over-Sampling Technique (SMOTE) for data augmentation, using data from 15 real projects. The augmented data
is then analyzed with Support Vector Regression (SVR) using three different kernels. The model's accuracy is assessed using mean square
error (MSE) and by comparing predicted durations with actual construction timelines. Results show that SMOTE combined with SVR linear
yielded the lowest MSE at 0.047, while SMOTE with SVR radial basis function provided the most accurate prediction, with just a one-day
error. These findings suggest that combining data augmentation techniques with machine learning can effectively address data limitations and
improve forecasting of construction duration. |