NEURAL CONTROLLER FOR THE SELECTION OF RECYCLED COMPONENTS IN POLYMER-GYPSY MORTARS
Grzegorz KŁOSOWSKI
g.klosowski@pollub.plLublin University of Technology, Lublin (Poland)
Tomasz KLEPKA
Department of Technology and Polymer Processing, Lublin University of Technology, Lublin (Poland)
Agnieszka NOWACKA
Department of Technology and Polymer Processing, Lublin University of Technology, Lublin (Poland)
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.
Keywords:
neural networks, gypsum-polymers, rubber regranulateReferences
Aslani, F., Ma, G., Wan, D. L. Y., & Muselin, G. (2018). Development of high-performance selfcompacting concrete using waste recycled concrete aggregates and rubber granules. Journal of Cleaner Production, 182, 553-566. https://doi.org/10.1016/j.jclepro.2018.02.074
DOI: https://doi.org/10.1016/j.jclepro.2018.02.074
Google Scholar
Baricevic, A., Jelcic Rukavina, M., & Pezer, M. (2018). Influence of recycled tire polymer fibers on concrete properties. Cement and Concrete Composites, 91, 29–41.
DOI: https://doi.org/10.1016/j.cemconcomp.2018.04.009
Google Scholar
Benosman, A. S., Taïbi, H., Senhadji, Y., Mouli, M., Belbachir, M., & Bahlouli, M. I. (2017). Plastic Waste Particles in Mortar Composites: Sulfate Resistance and Thermal Coefficients. Progress in Rubber, Plastics and Recycling Technology, 33(3), 171.
DOI: https://doi.org/10.1177/147776061703300304
Google Scholar
Bergström, L., Sturm (née Rosseeva), E. V., Salazar-Alvarez, G., & Cölfen, H. (2015). Mesocrystals in biominerals and colloidal arrays. Acc. Chem. Res., 48, 1391–1402. https://doi.org/10.1021/ar500440b
DOI: https://doi.org/10.1021/ar500440b
Google Scholar
Chłądzyński, S. (2008). Spoiwa gipsowe w budownictwie. Warsawa: Dom wydawniczy Medium.
Google Scholar
Aciu, C. (2013). Possibilities of Recycling Rubber Waste in the Composition of Mortars. ProEnvironment Promediu, 6(15).
Google Scholar
Di Mundo, R., Petrella, A., & Notarnicola, M. (2018). Surface and bulk hydrophobic cement composites by tyre rubber addition. Construction and Building Materials, 172, 176–184. https://doi.org/10.1016/j.conbuildmat.2018.03.233
DOI: https://doi.org/10.1016/j.conbuildmat.2018.03.233
Google Scholar
Forrest, M. (2014). Recycling and re-use of waste rubber. Shropshire: Smithers Rapra.
Google Scholar
Gorninski, J. P., Dal Molin, D.C., & Kazmierczak,C. S.(2007). Strength degradation of polymer concrete in acidic environments. Cem. Concr. Compos., 29(8), 637–645. https://doi.org/10.1016/j.cemconcomp.2007.04.001
DOI: https://doi.org/10.1016/j.cemconcomp.2007.04.001
Google Scholar
Herrero, S., Mayor, P., & Hernandez-Olivarez, F. (2013). Influence of proportion and particle size gradation of rubber from end-of-life tires on mechanical, thermal and acoustic properties of plaster-rubber mortars. Materials & Design, 47, 633–642. https://doi.org/10.1016/j.matdes.2012.12.063
DOI: https://doi.org/10.1016/j.matdes.2012.12.063
Google Scholar
Hooton, R. D. (2015). Current developments and future needs in standards for cementitious materials. Cement and Concrete Research, 78, 165–177. https://doi.org/10.1016/j.cemconres.2015.05.022
DOI: https://doi.org/10.1016/j.cemconres.2015.05.022
Google Scholar
Jafari, K., Tabatabaeian, M., Joshaghani, A., & Ozbakkaloglu, T. (2018). Optimizing the mixturedesign of polymer concrete: An experimental investigation. Construction and Building Materials, 167, 185–196. https://doi.org/10.1016/j.conbuildmat.2018.01.191
DOI: https://doi.org/10.1016/j.conbuildmat.2018.01.191
Google Scholar
Jarosiński, A., Żelazny, S., & Nowak, A. (2007). Warunki otrzymywania spoiwa gipsowego z produktu odpadowego pochodzącego z procesu pozyskiwania koncentratu cynku. Kraków: Czasopismo techniczne 1/Ch-2007 Wydawnictwo Politechniki Krakowskiej.
Google Scholar
Konar, B., Das, A., Gupta, P. K., & Saha, M. (2011). Physicochemical characteristics of styrenebutadiene latex- modified mortar composite vis-à-vis preferential interactions. J. Macromol. Sci., 48 (9), 757–765. https://doi.org/10.1080/10601325.2011.596072
DOI: https://doi.org/10.1080/10601325.2011.596072
Google Scholar
Kou, S.-C., & Poon, C.-S. (2013). A novel polymer concrete made with recycled glass aggregates, fly ash and metakaolin. Constr Build Mater., 41, 146–151. https://doi.org/10.1016/j.conbuildmat.2012.11.083
DOI: https://doi.org/10.1016/j.conbuildmat.2012.11.083
Google Scholar
Lorrentz, P. (2015). Artificial Neural Systems: Principle and Practice. Bentham Science Publishers. https://doi.org/10.2174/97816810809011150101
DOI: https://doi.org/10.2174/97816810809011150101
Google Scholar
Al Menhosh, A., Wang, Y., Wang, Y., & Augusthus-Nelson, L. (2018). Long term durability properties of concrete modified with metakaolin and polymer admixture. Construction and Building Materials, 172, 41–51. https://doi.org/10.1016/j.conbuildmat.2018.03.215
DOI: https://doi.org/10.1016/j.conbuildmat.2018.03.215
Google Scholar
Osiecka, E. (2005). Materiały budowlane – tworzywa sztuczne. Warszawa: Oficyna Wydawnicza Politechniki Warszawskiej.
Google Scholar
Pedro, D., De Brito, J., & Veiga, R. (2012). Mortars made with fine granulate from shredded tires. Journal of Materials in Civil Engineering, 25(4), 519–529. https://doi.org/10.1061/(ASCE)MT.1943-5533.0000606
DOI: https://doi.org/10.1061/(ASCE)MT.1943-5533.0000606
Google Scholar
Picker, A., Nicoleau, L., Burghard, Z., Bill, J., Zlotnikov, I., Labbez, C., Nonat, A., & Cölfen, H. (2017). Mesocrystalline calcium silicate hydrate: A bioinspired route toward elastic concrete materials. Science Advances, 11(3), 37–49. https://doi.org/10.1126/sciadv.1701216
DOI: https://doi.org/10.1126/sciadv.1701216
Google Scholar
Sahmaran, M., & Li, V. C. (2009). Durability properties of micro-cracked ECC containing high volumes fly ash. Cem. Concr. Res., 39, 1033–1043. https://doi.org/10.1016/j.cemconres.2009.07.009
DOI: https://doi.org/10.1016/j.cemconres.2009.07.009
Google Scholar
Seto, J., Ma, Y., Davis, S. A., Meldrum, F., Gourrier, A., Kim, Y.-Y., Cölfen, H. (2012). Structureproperty relationships of a biological mesocrystal in the adult sea urchin spine. Proceedings of the National Academy of Sciences, 109(10), 3699.
DOI: https://doi.org/10.1073/pnas.1109243109
Google Scholar
Serdar, M., Baricevic, A., Jelcic Rukavina, M., Pezer, M., & Bjegovic, D. (2015). Shrinkage behaviour of fibre reinforced concrete with recycled tyre polymer fibres. Int. J. Polym. Sci., 145918. https://doi.org/10.1155/2015/145918
DOI: https://doi.org/10.1155/2015/145918
Google Scholar
Serna, Á., del Rio, M., Palomo, J. G., & González, M. (2012). Improvement of gypsum plaster strain capacity by the addition of rubber particles from recycled tyres. Construction and Building
Google Scholar
Materials, 35, 633–641. https://doi.org/10.1016/j.conbuildmat.2012.04.093
DOI: https://doi.org/10.1016/j.conbuildmat.2012.04.093
Google Scholar
Sosoi, G., Barbuta, M., Serbanoiu, A. A., Babor, D., & Burlacu, A. (2018). Wastes as aggregate substitution in polymer concrete. Procedia Manufacturing, 22, 347–351. https://doi.org/10.1016/j.promfg.2018.03.052
DOI: https://doi.org/10.1016/j.promfg.2018.03.052
Google Scholar
Tanyildizi, H., & Asilturk, E. (2018). High temperature resistance of polymer-phosphazene concrete for 365 days. Construction and Building Materials, 174, 741–748. https://doi.org/10.1016/j.conbuildmat.2018.04.078
DOI: https://doi.org/10.1016/j.conbuildmat.2018.04.078
Google Scholar
Thomas, P., & Thomas, A. (2011). Multilayer perceptron for simulation models reduction: Application to a sawmill workshop. Engineering Applications of Artificial Intelligence, 24(4), 646-657. https://doi.org/10.1016/j.engappai.2011.01.004
DOI: https://doi.org/10.1016/j.engappai.2011.01.004
Google Scholar
Authors
Tomasz KLEPKADepartment of Technology and Polymer Processing, Lublin University of Technology, Lublin Poland
Authors
Agnieszka NOWACKADepartment of Technology and Polymer Processing, Lublin University of Technology, Lublin Poland
Statistics
Abstract views: 138PDF downloads: 45
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.
Similar Articles
- Nataliya SHABLIY, Serhii LUPENKO, Nadiia LUTSYK, Oleh YASNIY, Olha MALYSHEVSKA, KEYSTROKE DYNAMICS ANALYSIS USING MACHINE LEARNING METHODS , Applied Computer Science: Vol. 17 No. 4 (2021)
- Yuriy TRYUS, Nataliya ANTIPOVA, Kateryna ZHURAVEL, Grygoriy ZASPA, INFORMATION TECHNOLOGY OF STOCK INDEXES FORECASTING ON THE BASE OF FUZZY NEURAL NETWORKS , Applied Computer Science: Vol. 13 No. 1 (2017)
- Robert KARPIŃSKI, Jakub GAJEWSKI, Jakub SZABELSKI, Dalibor BARTA, APPLICATION OF NEURAL NETWORKS IN PREDICTION OF TENSILE STRENGTH OF ABSORBABLE SUTURES , Applied Computer Science: Vol. 13 No. 4 (2017)
- Monika KULISZ, Aigerim DUISENBEKOVA, Justyna KUJAWSKA, Danira KALDYBAYEVA, Bibigul ISSAYEVA, Piotr LICHOGRAJ, Wojciech CEL, IMPLICATIONS OF NEURAL NETWORK AS A DECISION-MAKING TOOL IN MANAGING KAZAKHSTAN’S AGRICULTURAL ECONOMY , Applied Computer Science: Vol. 19 No. 4 (2023)
- Lukas BAUER, Leon STÜTZ, Markus KLEY, BLACK BOX EFFICIENCY MODELLING OF AN ELECTRIC DRIVE UNIT UTILIZING METHODS OF MACHINE LEARNING , Applied Computer Science: Vol. 17 No. 4 (2021)
- Michał TOMCZYK, Anna PLICHTA, Mariusz MIKULSKI, APPLICATION OF WAVELET – NEURAL METHOD TO DETECT BACKLASH ZONE IN ELECTROMECHANICAL SYSTEMS GENERATING NOISES , Applied Computer Science: Vol. 15 No. 4 (2019)
- Wafaa Mustafa HAMEED, Asan Baker KANBAR, USING GA FOR EVOLVING WEIGHTS IN NEURAL NETWORKS , Applied Computer Science: Vol. 15 No. 3 (2019)
- Monika KULISZ, Justyna KUJAWSKA, Zulfiya AUBAKIROVA, Gulnaz ZHAIRBAEVA, Tomasz WAROWNY, PREDICTION OF THE COMPRESSIVE STRENGTH OF ENVIRONMENTALLY FRIENDLY CONCRETE USING ARTIFICIAL NEURAL NETWORK , Applied Computer Science: Vol. 18 No. 4 (2022)
- Anna MACHROWSKA, Robert KARPIŃSKI, Marcin MACIEJEWSKI, Józef JONAK, Przemysław KRAKOWSKI, APPLICATION OF EEMD-DFA ALGORITHMS AND ANN CLASSIFICATION FOR DETECTION OF KNEE OSTEOARTHRITIS USING VIBROARTHROGRAPHY , Applied Computer Science: Vol. 20 No. 2 (2024)
- Roman GALAGAN, Serhiy ANDREIEV, Nataliia STELMAKH, Yaroslava RAFALSKA, Andrii MOMOT, AUTOMATION OF POLYCYSTIC OVARY SYNDROME DIAGNOSTICS THROUGH MACHINE LEARNING ALGORITHMS IN ULTRASOUND IMAGING , Applied Computer Science: Vol. 20 No. 2 (2024)
You may also start an advanced similarity search for this article.