Predicting the Geopolymerization Process of Fly-Ash-Based Geopolymer Using Machine Learning

Abstract

The process of geopolymerization affects the freshness and hardening properties of fly ash base polymer. The prediction of geological polymerization parameters, such as DPT, DPH, GPT, and GPH, is very important for the mixing optimization of FA base polymer. In this study, machine learning models such as backpropagation neural network, support vector regression, random forest, K-nearest neighbor, logistic regression, and multiple linear regression were used to predict the above geological polymerization parameters and explain the influence of composition on the geological polymerization of FA base polymer. Results show that RF was the most stable ML model and had the best predictive performance on the test sets of GPT, GPH, DPT, and DPH, with correlation coefficients of 0.88, 0.95, 0.92, and 0.95, respectively. The variable importance and sensitivity were analyzed by SHapley Additive exPlanations. Results indicate that temperature is the most significant input variable affecting the DPT, DPH, and GPH with SHAP values of 0.09, 4.83, and 1.03, respectively. For GPT, the SHAP value of temperature is 6.89, slightly lower than that of LFR (6.95); yet it is a still significantly important input variable. The mole ratio and alkaline solution concentration were also important and negatively contributed to DPT and DPH, respectively. Besides, both GPT and GPH were sensitive to the mass ratio of liquid-to-fly ash which can promote the geopolymerization extent and shorten the geopolymerization time at a small content. The results of this study pave the way for automatic mixture optimization of FA-based geopolymers.

Keywords

• fly ash
• geopolymer
• machine learning
• geopolymerization