Neural Controller for the Selection of Recycled Components in Polymer-Gypsy Mortars

Abstract

This study presents research on the development of an intelligent controller that allows optimal selection of rubber granules, as an admixture recycling component for polymer-gypsy mortars. Based on the results of actual measurements, neural networks capable of predicting the setting time of gypsum mortar, as well as determining the bending and compressive strength coefficients were trained. A number of simulation experiments were carried out, thanks to which the characteristics of setting times and strength of mortars containing different compositions of recycling additives were determined. Thanks to the obtained results, it was possible to select the rubber admixtures optimally both in terms of the percentage share as well as in relation to the diameter of the granules.

Genetic Algorithm-Pid Controller for Model Order Reduction Pantographcatenary System

“…In that case the gene pool is uniform, crossover has little or no impact on population and each successive generation is surely identical as the first (Haamed & Hameed, 2020). In general, using the integrated absolute error (IAE), or the integral of squared error (ISE), or the integrated of time weighted squared error (ITSE), or the integral of time multiplied through absolute error (ITAE) is regularly employed in manage system due to the fact it can be evaluated analytically in frequency domain (Kłosowski, Klepka & Nowacka, 2018).…”