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Prediction of concrete mixing proportions using deep learning
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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Choi, Ju Hee | - |
| dc.contributor.author | Yang, Hyun Min | - |
| dc.contributor.author | Lee, Han Seung | - |
| dc.date.accessioned | 2025-04-09T02:33:18Z | - |
| dc.date.available | 2025-04-09T02:33:18Z | - |
| dc.date.issued | 2021-10-17 | - |
| dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/124729 | - |
| dc.description.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. 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, and the highest error rate with an error of 12 to 14% for fly and bfs. | - |
| dc.title | Prediction of concrete mixing proportions using deep learning | - |
| dc.type | Conference | - |
| dc.citation.conferenceName | DuraBI 2021 | - |
| dc.citation.conferencePlace | 대한민국 | - |
| dc.citation.conferenceDate | 2021-10-15 ~ 2021-10-17 | - |
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Related Researcher
- Lee, Han Seung
- ERICA 공학대학 (MAJOR IN ARCHITECTURAL ENGINEERING)