From Mix Design to Strength Prediction: Ensemble Learning Application on the Performance of High-Performance Concrete

Md Arifuzzaman, Abdulrahman Fahad Alfuhaid, Abm Saiful Islam, M. T. Bhuiyan, Mokammel Hossain Tito, Aniq Gul
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Abstract

In the realm of construction, achieving high-performance concrete (HPC) involves incorporating supplementary materials like fly ash and blast furnace slag, along with superplasticizer. The conventional water-to-cement ratio (w/c) concept, established by Abrams in 1918, asserts an inverse relationship between w/c ratio and concrete strength in HPC. However, a critical analysis of experimental data challenges this perspective, revealing that the paste quantity also significantly influences comparable cement strength, introducing complexity to our understanding of HPC and concrete strength dynamics. Furthermore, an exploration of concrete mix models and machine learning algorithms sheds light on variables impacting compressive strength. Surprisingly, blast furnace slag emerges as a predominant contributor, highlighting the significance of water management. Key factors like cement and aggregates play pivotal roles in shaping compressive strength. Notably, the Vote algorithm demonstrates exceptional predictive accuracy with a high correlation coefficient (0.919) and low mean absolute error (4.9166), while RandomForest and AdditiveRegression also exhibit commendable performance, striking a balance between accuracy and efficiency. These insights guide decisions in concrete mix design and machine learning model selection, offering valuable guidance for optimal outcomes across diverse applications in construction.
从混合设计到强度预测:高性能混凝土性能中的集合学习应用
在建筑领域,实现高性能混凝土(HPC)需要加入粉煤灰和高炉矿渣等辅助材料以及超塑化剂。艾布拉姆斯于 1918 年提出的传统水灰比(w/c)概念认为,在高性能混凝土中,水灰比与混凝土强度之间存在反比关系。然而,对实验数据的批判性分析对这一观点提出了挑战,揭示了浆体数量也会显著影响可比水泥强度,从而为我们理解 HPC 和混凝土强度动态引入了复杂性。此外,对混凝土混合模型和机器学习算法的探索揭示了影响抗压强度的变量。令人惊讶的是,高炉矿渣是主要的影响因素,这凸显了水管理的重要性。水泥和集料等关键因素在抗压强度的形成中起着举足轻重的作用。值得注意的是,Vote 算法具有极高的相关系数(0.919)和较低的平均绝对误差(4.9166),显示出卓越的预测准确性,而 RandomForest 和 AdditiveRegression 也表现出值得称道的性能,在准确性和效率之间取得了平衡。这些见解为混凝土混合设计和机器学习模型选择的决策提供了指导,为在建筑领域的各种应用中取得最佳结果提供了宝贵的指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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