Artificial neural networks test for the prediction of chemical stability of pyroclastic deposits-based AAMs and comparison with conventional mathematical approach (MLR)

The investigation on the reticulation degree of volcanic alkali-activated materials, AAMs, were experimentally determined in terms of chemico-physical properties: weight loss after leaching test in water, ionic conductivity and pH of the leachate and compressive strength. Artificial neural network (ANN) was successfully applied to predict the chemical stability of volcanic alkali-activated materials. Nine input data per each chemico-physical parameter were used to train each ANN. The training series of specific volcanic precursors were tested also for the other one. Excellent correlations between experimental and calculated data of the same precursor type were found reaching values around one. The evidence of strong effect on chemical stability of the alkaline activator SiO₂/Na₂O molar ratio as well as the Si/Al ratio of precursor mixtures on the reticulation degree of ghiara-based formulation with respect to volcanic ash-based materials is presented. It must be noted that such effect was much less pronounced on the compressive strength values, appearing more insensitive the molar ratio of the alkaline activator. The comparison of the ANN results with more conventional multiple linear regression (MLR) testifies the higher prediction performance of the first method. MLRs results, less significant, are useful to confirm the powerful capacity of ANNs to identify the more suitable formulation using a set of experimental AAMs. This study, as few others, on the correlation between chemical stability and compressive strength of AAMs provide a great contribution in the direction of durability and in-life mechanical performance of these class of materials. Graphic abstract: