머신러닝 모델과 BIM 객체 기반의 철근 물량 산출방법 비교

초록

Accurately calculating rebar quantities in the early stages of construction projects is important for cost estimation and resource management. This study introduces two automated methods for rebar quantity calculation: one based on Building Information Modeling (BIM) and anotherusing machine learning. Both approaches rely on detailed design data. The BIM-based method restructured rebar quantity formulas to use onlyinformation that can be extracted directly from BIM models. When tested on a case study, it achieved an average error rate of 2.015 percentfor columns and 4.925 percent for typical beams, showing reliable performance during the detailed design phase. The machine learning-basedmethod estimated rebar quantities using the rebar-to-concrete ratio. Approximately 100 samples, including concrete volume, rebar quantities,and construction years, were used for training. The LeakyReLU activation function produced the most accurate results, with an error rate of9.73 percent, which meets AACE standards for detailed estimates. Both methods showed potential for improving accuracy and enablingautomation. However, the study was limited by the size of the dataset and its focus on columns, beams, and slabs. Future research shouldexpand the dataset and apply the methods to a wider range of structural components. These findings provide a strong basis for early-stagerebar quantity estimation and support more informed decision-making in construction planning.

키워드