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.

Kłosowski, G., Klepka, T., & Nowacka, A. (2018). Neural controller for the selection of recycled components in polymer-gypsy mortars. Applied Computer Science, 14(2), 48-59. doi:10.23743/acs-2018-12

Kłosowski, Grzegorz, Tomasz Klepka, and Agnieszka Nowacka. "Neural Controller for the Selection of Recycled Components in Polymer-Gypsy Mortars." Applied Computer Science 14, no. 2 (2018): 48-59.

G. Kłosowski, T. Klepka, and A. Nowacka, "Neural controller for the selection of recycled components in polymer-gypsy mortars," Applied Computer Science, vol. 14, no. 2, pp. 48-59, 2018.

Kłosowski G, Klepka T, Nowacka A. Neural controller for the selection of recycled components in polymer-gypsy mortars. Applied Computer Science. 2018;14(2):48-59.