Prediction of the R 3 Test-Based Reactivity of Supplementary Cementitious Materials: A Machine Learning Approach Utilizing Physical and Chemical Properties

Prediction of the R 3 Test-Based Reactivity of Supplementary Cementitious Materials: A Machine Learning Approach Utilizing Physical and Chemical Properties

Abstract This study utilized machine learning (ML) models to investigate the effect of physical and chemical properties on the reactivity of various supplementary cementitious materials (SCMs). Six SCMs, including ground granulated blast furnace slag (GGBFS), pulverized coal fly ash (FA), and ground...

Full description

Bibliographic Details
Main Authors: Jinyoung Yoon, Aidarus Yonis, Sungwoo Park, Farshad Rajabipour, Sukhoon Pyo
Format: Article
Language:English
Published: SpringerOpen 2024-11-01
Series:International Journal of Concrete Structures and Materials
Subjects:
Artificial neural network
R 3 test
Modified strength activity index test
Material characterization
Online Access:https://doi.org/10.1186/s40069-024-00717-5

Internet

https://doi.org/10.1186/s40069-024-00717-5

Similar Items