| contributor author | Wang, Yufei | |
| contributor author | Sun, Junbo | |
| contributor author | Zou, Zefeng | |
| contributor author | Wang, Xiangyu | |
| contributor author | Li, Shengping | |
| contributor author | Zhao, Hongyu | |
| contributor author | Shang, Jiajie | |
| date accessioned | 2026-08-20T11:46:38Z | |
| date available | 2026-08-20T11:46:38Z | |
| date copyright | 2026/04/19 | |
| date issued | 2026 | |
| identifier other | JMCEE7.MTENG-22159.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4312654 | |
| description abstract | AbstractThis study provides a comprehensive analysis of ultrahigh-performance concrete (UHPC)
compressive strength, focusing on data augmentation, predictive modeling, and model
interpretability. The research utilized 808 experimental data points with 16 ... | |
| publisher | American Society of Civil Engineers | |
| title | Optimized Generative Modeling and Interpretable Machine Learning for Predicting the Compressive Strength of High-Performance Concrete | |
| type | Journal Article | |
| journal volume | 38 | |
| journal issue | 7 | |
| journal title | Journal of Materials in Civil Engineering | |
| identifier doi | 10.1061/JMCEE7.MTENG-22159 | |
| journal fristpage | 04026168-1 | |
| journal lastpage | 04026168-16 | |
| page | 16 | |
| tree | Journal of Materials in Civil Engineering:;2026:;Volume ( 038 ):;issue: 007 | |
| contenttype | Fulltext | |
