Prediction of the drying shrinkage of alkali-activated materials using artificial neural networks

Alkali-activated materials (AAMs) are qualitatively and quantitatively evaluated with an emphasis on the ultimate drying shrinkage. We systematically evaluated AAMs based on the mix design and curing conditions, utilizing a total of 452 AAM mixtures extracted from 44 papers. Finally, a predictive model for the ultimate drying shrinkage of AAMs was constructed using an artificial neural network (ANN) with high accuracy, in which the reactivity of binder, geopolymer paste volume, liquid-to-binder ratio, alkali activator modulus, aggregate volumetric ratio, curing temperature, relative humidity and specimen size were set as inputs. This model shows great generality by compiling various AAM mixtures and is easy-handling without preparation of samples for acquiring specific properties. Moreover, the efficiency of three commonly used models for predicting the drying shrinkage-the Bazant-Baweja model, Gardner and Lockman model, and multi-linear regression model-were evaluated and compared to the proposed ANN model, revealing a better prediction performance of ANN model. This study will advance the understanding of the drying shrinkage behaviors of AAMs and provide practical guidelines for designing AAM mixtures with high durability.

Automated summary

This study systematically evaluated and predicted the ultimate drying shrinkage of Alkali-activated materials (AAMs) using an artificial neural network model. The model showed high accuracy and generality, while other commonly used models for predicting drying shrinkage were also evaluated and compared. The findings contribute to a better understanding of the drying shrinkage behaviors of AAMs and provide practical guidelines for designing AAM mixtures with high durability.