Ultra-high performance concrete design method based on machine learning model and steel slag powder

Abstract

This paper presents a design method for green ultra-high performance concrete (UHPC) through machine learning (ML) models and steel slag (SS) powder. First, different UHPC compressive strength prediction models are developed, which include the linear regression (LR) model, back propagation artificial neural network (BP-ANN) model, genetic algorithm optimized ANN (GABP-ANN) model, random forest (RF) model and support vector machine (SVM) model. The prediction results of different ML models are compared and the SVM model is identified to have higher accuracy (R2 =0.93). Then, a design model for UHPC is proposed by the modified Andreasen & Andersen (MAA) particle packing model and SVM model. The model takes into account the particle size and the interactions of the materials, resulting in UHPC with excellent mechanical properties and a low amount of cement. Finally, green UHPC with low cement dosage and different SS powder replacement rates (substituted cement) are designed. The effects of SS on the mechanical properties, microscopic properties, hydration properties, drying shrinkage properties, toxic leaching properties and environmental properties of UHPC are investigated when the cement admixture in UHPC is low. The results indicate that the mechanical properties of UHPC decrease with the increase of SS substitution rate, but the decrease is limited. The addition of SS powder can not only inhibit the dry shrinkage and reduce the heat of hydration, but also reduce the impact on the environment.