Predicting the compressive strength of high-performance concrete using an interpretable machine learning model
Yushuai Zhang, Wangjun Ren, Yicun Chen, Yongtao Mi, Jiyong Lei, Licheng Sun · 2024
Read Predicting the compressive strength of high-performance concrete using an interpretable machine learning model by Yushuai Zhang, Wangjun Ren, Yicun Chen, Yongtao Mi, Jiyong Lei, Licheng Sun (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11569169.txt.
Description
Yushuai Zhang and five co-authors present a 2024 way to guess the strength of high-grade concrete with machine learning. Good guesses cut build time and labor cost and help crews check build quality. Past models could be hard to read, so the team built a frame that shows why each guess comes out as it does.
The team added six new input traits to a known concrete data set. They trained four tree-based models and tuned each one with a broad search and checks. Their frame then ranks which traits drive strength most. Builders and lab staff can use the results to mix concrete with more trust.
Catalog record
| CREATOR | Yushuai Zhang, Wangjun Ren, Yicun Chen, Yongtao Mi, Jiyong Lei, Licheng Sun |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 42 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |