Hydration Behavior of Tricalcium Silicate in Seawater Relevant Salt Systems: A Hybrid Study with the Aid of Machine Learning

In order to help in the better application of seawater, the hydration behavior of tricalcium silicate (C 3 S) was studied. C 3 S was mixed with seawater‐relevant solutions (NaCl, MgCl 2 , Na 2 SO 4 ) with varied concentrations. The simplified C 3 S model system was adopted to systematically isolate the roles of individual seawater ions and their interactions. The results showed that seawater‐relevant solutions increased the hydration rate of C 3 S at an early age. The heat flow peak value increased with increasing salt concentration. Among the investigated salts, MgCl 2 exhibits the most obvious effect, indicating its dominant role in the acceleration behavior of seawater. The effect of seawater was more significant than any single salt solution with its typical concentration in seawater. The nucleation rate of hydration products was increased with the addition of salts, which was the main reason for the acceleration effect. A neural network was introduced to help understand the effect of seawater relevant solutions on the simplified model system. The neural network was trained based on the experimental data. The predicted calorimetry results agreed with the experimental data. The average coefficient of determination of the model exceeds 95%, which highlights its potential as a practical tool for predicting hydration behavior from seawater composition.