| Title |
Material Database Extraction Through Deep Learning From Bill of Quantity |
| Authors |
미아오쉬(Miao, Xu) ; 엄신조(Eom, Shin-Jo) |
| DOI |
https://doi.org/10.5659/JAIK.2023.39.12.297 |
| Keywords |
Bill of quantities; Material Management; Deep Learning; AI; LSTM; FastText |
| Abstract |
In construction projects, optimizing material selection is crucial, as over half of the construction cost is allocated to materials. To achieve
this, an integrated material information system becomes essential. Creating an efficient material list requires significant investment in
manpower and time to register and manage diverse material information. This study introduces a system developed through deep
learning-based intelligent material extraction. The system builds a database of building material information from real projects, utilizing a
classifier trained with standard construction codes using the FastText method and LSTM model. Through experiments on 40 buildings, the
system demonstrated an 86% accuracy rate. The resulting building material information serves as a foundational resource for future
applications such as artificial intelligence-based automation of design economic evaluation and design safety assessment. |