Prediction on the freeze-thaw resistance of a one-part geopolymer stabilized soil by using deep learning method

Abstract

The mechanical properties of soil located at cold areas may be deteriorated under freeze-thaw cycle condition. One-part geopolymer (OPG) is a kind of alkaline-activated material by using industrial by-products and solid alkali. Obviously, OPG can replace ordinary portland cement (OPC) as a soil stabilizer in ground improvement, which presents environmental and low-carbon benefits. The assessment of unconfined compressive strength (UCS) is vital for evaluating OPG-stabilized soil durability under freeze-thaw conditions, typically demanding extensive resources. Leveraging artificial intelligence, a predictive model can be developed for this purpose. This study collected a small sample size of 216 data points of the OPG-stabilized soil's freeze-thaw behaviour. Three deep learning (DL) models, Backpropagation Neural Network [BPNN], Convolutional Neural Network [CNN], Gated Recurrent Unit [GRU], were trained on the small dataset to predict freeze-thaw performance efficiently, offering a promising approach to streamline assessment processes. In the DL models, the ratio of fly ash (FA) and ground granulated blast furnace slag (GGBFS), freezing temperature and freeze-thaw cycle were taken as the input variables, and the target output was the UCS of the OPG-stabilized soil. Among all the models, the CNN achieved the highest prediction accuracy with R2 of 0.9966, and followed by the BPNN (R2=0.9893) and the GRU (R2=0.9872). After that, the interpretable machine learning methods (i.e., Shapley Additive Explanation [SHAP] and Partial Dependence Plot [PDP]) were utilized for the developed CNN model to further understand the impact of input variables on the outcome predictions. In addition, the morphological analysis was used to verify the freeze-thaw mechanism of the OPG-stabilized soil derived from the interpretable CNN model. It is revealed that the inclusion of FA in the OPG crucially enhanced the freeze-thaw resistance of the OPG-stabilized soil. However, beyond a certain threshold, the addition of FA negatively impacted the freeze-thaw resistance of OPG-stabilized soil. Freezing temperature was pinpointed as the key factor affecting the properties of the stabilized soil.

Keywords

• Deep learning
• Compressive strength
• One-part geopolymer
• Interpretable machine learning