Multi-Objective Optimization for Nano-Silica-Modified Concrete Based on Explainable Machine Learning
Nano-silica modified concrete (NSC) has been widely applied in engineering practice. However, conventional manual mix proportion design is both time-consuming and costly. In this study, four machine learning models—XGBoost, CatBoost, Random Forest, and AdaBoost—were trained to predict the compressiv...
Enregistré dans:
| Auteurs principaux: | , , , , , |
|---|---|
| Format: | Artigo |
| Langue: | Inglês |
| Publié: |
MDPI AG
2025-09-01
|
| Collection: | Nanomaterials |
| Sujets: | |
| Accès en ligne: | https://www.mdpi.com/2079-4991/15/18/1423 |
| Tags: |
Pas de tags, Soyez le premier à ajouter un tag!
|