Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Enhancing Concrete Workability Prediction Through Ensemble Learning Models: Emphasis on Slump and Material Factors
This study advances concrete workability prediction by integrating ensemble learning models like Random Forest (RF), Extreme Gradient Boosting (XGBoost), adaptive boosting (AdaBoost), and gradient boosted regression trees (GBRTs), and XGBoost showing superior accuracy. Using Shapley additive explanations (SHAPs), it identifies key factors such as the S/log(W) ratio and aggregate components affecting concrete performance. Theoretically, this enhances the predictive analytics framework in material science, while practically, it improves construction efficiency by optimizing material use and reducing physical testing. The development of a user-friendly graphical user interface (GUI) makes these models accessible for industry application. Addressing gaps in model accuracy and interpretability, the research contributes to sustainable construction practices and underscores the need for future expansions in data diversity and computational optimization in the construction industry.
Enhancing Concrete Workability Prediction Through Ensemble Learning Models: Emphasis on Slump and Material Factors
This study advances concrete workability prediction by integrating ensemble learning models like Random Forest (RF), Extreme Gradient Boosting (XGBoost), adaptive boosting (AdaBoost), and gradient boosted regression trees (GBRTs), and XGBoost showing superior accuracy. Using Shapley additive explanations (SHAPs), it identifies key factors such as the S/log(W) ratio and aggregate components affecting concrete performance. Theoretically, this enhances the predictive analytics framework in material science, while practically, it improves construction efficiency by optimizing material use and reducing physical testing. The development of a user-friendly graphical user interface (GUI) makes these models accessible for industry application. Addressing gaps in model accuracy and interpretability, the research contributes to sustainable construction practices and underscores the need for future expansions in data diversity and computational optimization in the construction industry.
Enhancing Concrete Workability Prediction Through Ensemble Learning Models: Emphasis on Slump and Material Factors
Jiangsong Jiang (Autor:in) / Chunhong Xin (Autor:in) / Sifei Wu (Autor:in) / Wenbing Chen (Autor:in) / Hui Li (Autor:in) / Zhaolun Ran (Autor:in)
2024
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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Workability Control of No-Slump Concrete
| British Library Conference Proceedings | 1999
The Slump Flow Test for High-Workability Concrete
| Online Contents | 1998
The Slump Flow Test for High-Workability Concrete
| British Library Online Contents | 1998