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S. Malazdrewicz, L. Sadowski, "Eco-friendly mortars with granite powder and fly ash and their prediction with artificial neural networks", in P. Iványi, J. Logo, B.H.V. Topping, (Editors), "Proceedings of the Sixth International Conference on Soft Computing, Machine Learning and Optimisation in Civil, Structural and Environmental Engineering", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 5, Paper 3.1, 2023, doi:10.4203/ccc.5.3.1

Keywords: mortar, eco-friendly, granite powder, fly ash, cement replacement, machine learning, artificial neural network.

Abstract

This paper focuses on mortar in which cement is partially replaced with siliceous fly ash and granite powder and subjected to abrasion. To optimise the process of testing abrasion resistance, artificial neural network (ANN) was proposed to predict results. Predicting abrasion resistance of mortar, especially with addition of wastes is not widely explored and requires greater academic attention. The possibility of applying machine learning is reducing amount of destructive tests methods in favour of prediction, therefore sustainable approach. This study confirms that it is possible to create eco-friendly mortar with siliceous fly ash and / or granite powder and that these materials do not deteriorate the surface of mortar samples. The most similar results of abrasion resistance to reference samples with only Portland cement were obtained for combination of fly ash and granite powder. The application of ANN to predict abrasion resistance of tested mortars showed very accurate results with the network’s worst quality at 0.967 (with 1.0 as ideal result). The quality of the network relies on properly selected and detailed inputs. This study proved satisfactory results of predicting abrasion resistance based on mortar’s components only.

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Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 5 PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE ON SOFT COMPUTING, MACHINE LEARNING AND OPTIMISATION IN CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING Edited by: P. Iványi, J. Logo and B.H.V. Topping Paper 3.1 Eco-friendly mortars with granite powder and fly ash and their prediction with artificial neural networks S. Malazdrewicz and L. Sadowski Faculty of Civil Engineering, Wroclaw University of Science and Technology, Wroclaw, Poland doi:10.4203/ccc.5.3.1 Full Bibliographic Reference for this paper S. Malazdrewicz, L. Sadowski, "Eco-friendly mortars with granite powder and fly ash and their prediction with artificial neural networks", in P. Iványi, J. Logo, B.H.V. Topping, (Editors), "Proceedings of the Sixth International Conference on Soft Computing, Machine Learning and Optimisation in Civil, Structural and Environmental Engineering", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 5, Paper 3.1, 2023, doi:10.4203/ccc.5.3.1 Keywords: mortar, eco-friendly, granite powder, fly ash, cement replacement, machine learning, artificial neural network. Abstract This paper focuses on mortar in which cement is partially replaced with siliceous fly ash and granite powder and subjected to abrasion. To optimise the process of testing abrasion resistance, artificial neural network (ANN) was proposed to predict results. Predicting abrasion resistance of mortar, especially with addition of wastes is not widely explored and requires greater academic attention. The possibility of applying machine learning is reducing amount of destructive tests methods in favour of prediction, therefore sustainable approach. This study confirms that it is possible to create eco-friendly mortar with siliceous fly ash and / or granite powder and that these materials do not deteriorate the surface of mortar samples. The most similar results of abrasion resistance to reference samples with only Portland cement were obtained for combination of fly ash and granite powder. The application of ANN to predict abrasion resistance of tested mortars showed very accurate results with the network’s worst quality at 0.967 (with 1.0 as ideal result). The quality of the network relies on properly selected and detailed inputs. This study proved satisfactory results of predicting abrasion resistance based on mortar’s components only. download the full-text of this paper (PDF, 9 pages, 305 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
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