Hua Si, Daoming Shen, Muhammad Nasir Amin, Siyab Ul Arifeen, Muhammad Tahir Qadir, Kaffayatullah Khan
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Statistics, Taylor’s diagrams, <jats:italic>R</jats:italic> <jats:sup>2</jats:sup> values, and comparisons of experimental and theoretical results were used to evaluate the models. Stress testing also showed how different input features affected the model’s outputs. The accuracy of all GEP models was shown to fall within the acceptable range (<jats:italic>R</jats:italic> <jats:sup>2</jats:sup> = 0.952 for CS and <jats:italic>R</jats:italic> <jats:sup>2</jats:sup> = 0.920 for FS), and all MEP prediction models were determined to be exceptionally accurate (<jats:italic>R</jats:italic> <jats:sup>2</jats:sup> = 0.970 for CS and <jats:italic>R</jats:italic> <jats:sup>2</jats:sup> = 0.935 for FS). The statistical testing for error validation also verified that MEP models were more accurate than GEP models. 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引用次数: 0
摘要
本研究探讨了一种新型粘结材料的可能性,即由回收大理石制成的大理石水泥(MC)。研究将评估它与稻壳灰和粉煤灰混合后的性能如何。本研究分析了大理石水泥砂浆(FR-MCM)的抗弯强度,这是一种混合了 MC、粉煤灰和稻壳灰的砂浆。开发了一套能够预测 CS 和 FS(抗折和抗压强度)的机器学习模型。基因表达程序设计(GEP)和多重表达程序设计(MEP)是创建这类模型的关键。统计、泰勒图、R 2 值以及实验和理论结果的比较被用来评估模型。压力测试还显示了不同输入特征对模型输出的影响。所有 GEP 模型的精确度都在可接受范围内(CS 的 R 2 = 0.952,FS 的 R 2 = 0.920),所有 MEP 预测模型的精确度都非常高(CS 的 R 2 = 0.970,FS 的 R 2 = 0.935)。误差验证的统计测试也验证了 MEP 模型比 GEP 模型更准确。根据敏感性分析,固化龄期和稻壳灰对 CS 和 FS 的预测影响最大,其次是粉煤灰和 MC。
Producing sustainable binding materials using marble waste blended with fly ash and rice husk ash for building materials
This study explores the possibilities of a new binding material, i.e., marble cement (MC) made from recycled marble. It will assess how well it performs when mixed with ash from rice husks and fly ash. This research analyzes flexural strength of marble cement mortar (FR-MCM), a mortar that incorporates MC, fly ash, and rice husk ash. A set of machine learning models capable of predicting CS and FS (flexural and compressive strengths) were developed. Gene expression programming (GEP) and multi-expression programming (MEP) are crucial in creating these types of models. Statistics, Taylor’s diagrams, R2 values, and comparisons of experimental and theoretical results were used to evaluate the models. Stress testing also showed how different input features affected the model’s outputs. The accuracy of all GEP models was shown to fall within the acceptable range (R2 = 0.952 for CS and R2 = 0.920 for FS), and all MEP prediction models were determined to be exceptionally accurate (R2 = 0.970 for CS and R2 = 0.935 for FS). The statistical testing for error validation also verified that MEP models were more accurate than GEP models. According to sensitivity analysis, curing age and rice husk ash exerted the most significant influence on the prediction of CS and FS, followed by fly ash and MC.
期刊介绍:
Reviews on Advanced Materials Science is a fully peer-reviewed, open access, electronic journal that publishes significant, original and relevant works in the area of theoretical and experimental studies of advanced materials. The journal provides the readers with free, instant, and permanent access to all content worldwide; and the authors with extensive promotion of published articles, long-time preservation, language-correction services, no space constraints and immediate publication.
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