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딥러닝을 통한 콘크리트 강도에 대한 배합 방법 예측에 관한 연구Prediction of concrete mixing proportions using deep learning
- Other Titles
- Prediction of concrete mixing proportions using deep learning
- Issue Date
- Nov-2021
- Publisher
- 한국건축시공학회
- Keywords
- 딥러닝; 압축강도; 물시멘트비; 배합비율; deep learning; compressive strength; water cement ratio; mix proportion
- Citation
- 한국건축시공학회 학술발표대회 논문집, v.22, no.2, pp 30 - 31
- Pages
- 2
- Indexed
- OTHER
- Journal Title
- 한국건축시공학회 학술발표대회 논문집
- Volume
- 22
- Number
- 2
- Start Page
- 30
- End Page
- 31
- Abstract
- This study aims to build a deep learning model that can predict the value of concrete mixing properties according to a given concrete strength value. A model was created for a total of 1,291 concrete data, including 8 characteristics related to concrete mixing elements and environment, and the compressive strength of concrete. As the deep learning model, DNN-3L-256N, which showed the best performance on the prior study, was used. The average value for each characteristic of the data set was used as the initial input value. In results, in the case of ‘curing temperature’, which had a narrow range of values in the existing data set, showed the lowest error rate with less than 1% error based on MAE. The highest error rate with an error of 12 to 14% for fly and bfs.
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Related Researcher
- Lee, Han Seung
- ERICA 공학대학 (MAJOR IN ARCHITECTURAL ENGINEERING)