{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T15:11:47Z","timestamp":1782313907349,"version":"3.54.5"},"reference-count":36,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T00:00:00Z","timestamp":1755216000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union","award":["2023-1-HR01-KA220-HED-000165929"],"award-info":[{"award-number":["2023-1-HR01-KA220-HED-000165929"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Materials"],"abstract":"<jats:p>This study presents the development of a reliable predictive model for evaluating key physical and mechanical properties of cement-based composites modified with granite powder, a waste byproduct from granite rock cutting. The research addresses the need for more sustainable materials in the concrete industry by exploring the potential of granite powder as a supplementary cementitious material (SCM) to partially replace cement and reduce CO2 emissions. The experimental program included standardized testing of samples containing up to 30% granite powder, focusing on compressive strength at 7, 28, and 90 days, bonding strength at 28 days, and packing density of the fresh mixture. A multilayer perceptron (MLP) artificial neural network was employed to predict these properties using four input variables: granite powder content, cement content, sand content, and water content. The network architecture, consisting of two hidden layers with 10 and 15 neurons, respectively, was selected as the most suitable for this purpose. The model achieved high predictive performance, with coefficients of determination (R) exceeding 0.9 and mean absolute percentage errors (MAPE) below 6% for all output variables, demonstrating its robustness and accuracy. The findings confirm that granite powder not only contributes positively to concrete performance over time, but also supports environmental sustainability goals by reducing the carbon footprint associated with cement production. However, the model\u2019s applicability is currently limited to mixtures using granite powder at up to 30% cement replacement. This research highlights the effectiveness of machine learning, specifically neural networks, for solving multi-output problems in concrete technology. The successful implementation of the MLP network in this context may encourage broader adoption of data-driven approaches in the design and optimization of sustainable cementitious composites.<\/jats:p>","DOI":"10.3390\/ma18163838","type":"journal-article","created":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T14:24:28Z","timestamp":1755267868000},"page":"3838","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Identification of Selected Physical and Mechanical Properties of Cement Composites Modified with Granite Powder Using Neural Networks"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8021-943X","authenticated-orcid":false,"given":"Slawomir","family":"Czarnecki","sequence":"first","affiliation":[{"name":"Faculty of Civil Engineering, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2213","DOI":"10.5194\/essd-10-2213-2018","article-title":"Global CO2 emissions from cement production, 1928\u20132018","volume":"10","author":"Andrew","year":"2018","journal-title":"Earth Syst. 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