Research on prediction of compressive strength of fly ash and slag mixed concrete based on machine learning.

ABSTRACT: Every year, a large amount of solid waste such as fly ash and slag is generated worldwide. If these solid wastes are used in concrete mixes to make concrete, it can effectively save resources and protect the environment. The compressive strength of concrete is an essential indicator for testing its quality, and its prediction is affected by many factors. It is difficult to predict its strength accurately. Therefore, based on the current popular machine learning supervised learning algorithms: Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVR), three models established a nonlinear mapping between multi-factor features and target feature concrete compressive strength. Using the three completed training models, we validated the test set with 206 example

SUBMITTER: Wang M 

PROVIDER: S-EPMC9794082 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature