Soft-computing models for predicting plastic viscosity and interface yield stress of fresh concrete.

ABSTRACT: Interface yield stress and plastic viscosity of fresh concrete significantly influences its pumping ability. The accurate determination of these properties needs extensive testing on-site which results in time and resource wastage. Thus, to speed up the process of accurately determining these concrete properties, this study tends to use four machine learning (ML) algorithms including Random Forest Regression (RFR), Gene Expression Programming (GEP), K-nearest Neighbor (KNN), Extreme Gradient Boosting (XGB) and a statistical technique Multi Linear Regression (MLR) to develop predictive models for plastic viscosity and interface yield stress of concrete. Out of all employed algorithms, only GEP expressed its output in the form of an empirical equation. The models were developed using data fr

SUBMITTER: Inqiad WB 

PROVIDER: S-EPMC11953388 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature