Salami Babatunde Abiodun, Usman Jamilu, Gbadamosi Afeez, Malami Salim Idris, Abba Sani I
Cardiff School of Management, Cardiff Metropolitan University, CF5 2YB, Cardiff, Wales, United Kingdom.
Interdisciplinary Research Center for Membrane and Water Security, King Fahd University of Petroleum and Minerals, 31261, Dhahran, Saudi Arabia.
Sci Rep. 2024 Dec 28;14(1):30646. doi: 10.1038/s41598-024-58908-9.
With the continuous clamor for a reduction in embodied carbon in cement, rapid solution to climate change, and reduction to resource depletion, studies into substitute binders become crucial. These cementitious binders can potentially lessen our reliance on cement as the only concrete binder while also improving concrete functional properties. Finer particles used in cement microstructure densify the pore structure of concrete and enhance its performance properties. The compressive strength of concrete made from a mixture of ground granulated blast furnace slag (GGBFS), fly ash (FA), and ordinary Portland cement was estimated using kernel regression techniques in this work. The kernel-based method offered was support vector regression (SVR), while robust linear regression (RLR), and multi-linear regression (MLR) were used as regression methods, subsequently, nonlinear average approaches were used to improve the accuracy of the prediction. Eight variables (cement, FA, GGBFS, water, superplasticizer dose [SP], coarse aggregate [CA], fine aggregate [F], age) were employed as input features in 3323 data samples, and their relative value was assessed using linear correlation analysis. Following analysis, three combinations were employed to train the kernel-based models: I (inputs: cement, water, and age|output: CS), II (inputs: cement, water, FA, SP, and age|output: CS), and III (inputs: cement, water, FA, SP, CA, GGBFS, and F|output: CS). The third combination gave the best testing performance with all the proposed models where their R and MSE results after model evaluation for SVR, RLR, and MLR, are [0.984, 0.8776 and 0.8804] and [0.0019, 0.0131 and 0.0128] respectively. The study concludes that SVR with the combination III (SVR-M3) offered the best performance through effectiveness and efficiency in accurately predicting the compressive strength of the blended concrete. The prediction models should be utilized with the input variable ranges used in this work.
随着对降低水泥中隐含碳、快速解决气候变化问题以及减少资源消耗的呼声不断，对替代胶凝材料的研究变得至关重要。这些胶凝材料有可能减少我们对水泥作为唯一混凝土胶凝材料的依赖，同时还能改善混凝土的功能特性。水泥微观结构中使用的细颗粒会使混凝土的孔隙结构致密化并提高其性能。在这项工作中，使用核回归技术估算了由磨细粒化高炉矿渣（GGBFS）、粉煤灰（FA）和普通硅酸盐水泥混合制成的混凝土的抗压强度。提供的基于核的方法是支持向量回归（SVR），而稳健线性回归（RLR）和多元线性回归（MLR）被用作回归方法，随后，使用非线性平均方法来提高预测的准确性。八个变量（水泥、FA、GGBFS、水、高效减水剂用量[SP]、粗骨料[CA]、细骨料[F]、龄期）被用作3323个数据样本的输入特征，并使用线性相关分析评估它们的相对值。经过分析，采用三种组合来训练基于核的模型：I（输入：水泥、水和龄期|输出：抗压强度[CS]），II（输入：水泥、水、FA、SP和龄期|输出：抗压强度[CS]），以及III（输入：水泥、水、FA、SP、CA、GGBFS和F|输出：抗压强度[CS]）。对于所有提出的模型，第三种组合给出了最佳测试性能，在对SVR、RLR和MLR进行模型评估后，它们的R和均方误差（MSE）结果分别为[0.984、0.8776和0.8804]以及[0.0019、0.0131和0.0128]。该研究得出结论，组合III的SVR（SVR-M3）通过有效且高效地准确预测混合混凝土的抗压强度而表现出最佳性能。预测模型应与本工作中使用的输入变量范围一起使用。
