Predicting Compressive Strength and Hydration Products of Calcium Aluminate Cement Using Data-Driven Approach.

ABSTRACT: Calcium aluminate cement (CAC) has been explored as a sustainable alternative to Portland cement, the most widely used type of cement. However, the hydration reaction and mechanical properties of CAC can be influenced by various factors such as water content, Li₂CO₃ content, and age. Due to the complex interactions between the precursors in CAC, traditional analytical models have struggled to predict CAC binders' compressive strength and porosity accurately. To overcome this limitation, this study utilizes machine learning (ML) to predict the properties of CAC. The study begins by using thermodynamic simulations to determine the phase assemblages of CAC at different ages. The XGBoost model is then used to predict the compressive strength, porosity, and hydration produ

SUBMITTER: Ponduru SA 

PROVIDER: S-EPMC9861258 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature