Predicting the evolution of static yield stress with time of blended cement paste through a machine learning approach

Abstract

Predicting the static yield stress (tau S0) is relevant to design innovative concrete technologies. This paper evaluated three machine learning methods to choose the most accurate model to predict the growth of tau S0 of blended cement pastes. Eight input parameters were used, including supplementary cementitious material (SCM) properties, cement reactivity, mixture design parameters, and resting time. Among the evaluated methods, the multilayer perceptron was the most accurate. Results showed that tau S0 during the initial linear growth stage is controlled by the SCM properties. However, the tau S0 during the later exponential growth stage is controlled by the amount of pseudo-contact zones.