Optimization of Material Composition for Improving Mechanical Properties of Fly Ash-Slag-Based Geopolymers: A Deep Learning Approach

Abstract

Geopolymer is regarded as a novel type of eco-friendly material that may replace cement. To improve the prediction accuracy of mechanical properties of fly ash-slag-based geopolymer (FASGG), as well as optimize material composition and mix design, this study utilizes seven key parameters as variables, and compressive and flexural strengths were as outputs. Deep learning techniques were applied to train and predict 600 sets of experimental data, developing a novel predictive model of MK–CNN–GRU, which integrated Maximal Information Coefficient-K-median algorithm, Convolutional Neural Network, and Gated Recurrent Unit algorithms. Results indicated that the ranking of input parameters which were related with compressive strength was curing age, Ca/Si ratio, fly ash-to-slag ratio, Si/Al ratio, water-to-binder ratio, alkali activator modulus, and alkali equivalent. Three classical models were selected as benchmarks for predicting compressive strength at different curing ages. The MK–CNN–GRU model could fully exploit the internal features of experimental data and learn its variation patterns, resulting in more stable predictive performance. An ablation study of the submodels confirms that MK–CNN–GRU model considers temporal dependencies, long- and short-term features, as well as local dependencies and hierarchical feature representations within the data. Experimental data suggested an exponential relationship between flexural and compressive strengths of FASGG. The predictions for flexural strength indicated that the MK–CNN–GRU model effectively captured variations, demonstrating good generalization ability and applicability. This model enhances the estimation accuracy regarding the mechanical behavior of FASGG, offering a theoretical framework for refining its composition and mix design.