Drying shrinkage of alkali-activated binders are recognized as one of the most important properties towards quality assurance of the binders. In this study, results of experimental studies and predictive models developed to determine the drying shrinkage of alkali - activated blast furnace-fly ash mortars are presented and discussed. Different parameters were altered in the experimental study such as the content of GGBFS, FA, activator modulus (Ms), and curing temperature. Their effects on the drying shrinkage of the mortars were then evaluated. Artificial neural network (ANN) and Multiple Linear Regression (MLR) models were built to predict the drying shrinkage at 28 days using the above-mentioned parameters as inputs. The experimental results and ANN model predictions showed strong correlations. The prediction of 28-days drying shrinkage for the alkali-activated GGBFS-FA was more accurate using ANN than MLR.

碱活化粘合剂的干燥收缩率被认为是保证粘合剂质量的最重要特性之一。在这项研究中，介绍和讨论了为确定碱活化高炉粉煤灰砂浆的干燥收缩而开发的实验研究和预测模型的结果。在实验研究中改变了不同的参数，例如 GGBFS 的含量、FA、活化剂模量 (Ms) 和固化温度。然后评估它们对砂浆干燥收缩的影响。建立人工神经网络 (ANN) 和多元线性回归 (MLR) 模型以使用上述参数作为输入来预测 28 天的干燥收缩。实验结果和人工神经网络模型预测显示出很强的相关性。