Czarnecki Slawomir
Faculty of Civil Engineering, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland.
Materials (Basel). 2025 Aug 15;18(16):3838. doi: 10.3390/ma18163838.
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 CO 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's 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.
本研究提出了一种可靠的预测模型，用于评估用花岗岩粉改性的水泥基复合材料的关键物理和力学性能，花岗岩粉是花岗岩切割产生的一种废弃副产品。该研究通过探索花岗岩粉作为辅助胶凝材料（SCM）部分替代水泥并减少碳排放的潜力，满足了混凝土行业对更可持续材料的需求。实验方案包括对含高达30%花岗岩粉的样品进行标准化测试，重点关注7天、28天和90天的抗压强度、28天的粘结强度以及新鲜混合料的堆积密度。使用多层感知器（MLP）人工神经网络，利用四个输入变量（花岗岩粉含量、水泥含量、砂含量和水含量）来预测这些性能。分别由10个和15个神经元组成的两个隐藏层的网络架构被选为最适合此目的的架构。该模型具有较高的预测性能，所有输出变量的决定系数（R）超过0.9，平均绝对百分比误差（MAPE）低于6%，证明了其稳健性和准确性。研究结果证实，花岗岩粉不仅随着时间的推移对混凝土性能有积极贡献，而且通过减少与水泥生产相关的碳足迹，支持了环境可持续性目标。然而，该模型目前的适用性仅限于水泥替代量高达30%的花岗岩粉混合物。本研究突出了机器学习，特别是神经网络，在解决混凝土技术中的多输出问题方面的有效性。在这种情况下，MLP网络的成功实施可能会鼓励在可持续水泥基复合材料的设计和优化中更广泛地采用数据驱动方法。
