摘要
由一名新闻记者-机器人与机器学习每日新闻的工作人员新闻编辑-调查人员发布了关于人工智能的新报告。据中国人民代表大会永州市消息，NewsRx记者报道，研究表明：“大理石水泥(MC)是一种新型混凝土粘结材料，其强度评估是本次调查的主题。”本报记者引用湖南科技大学的一篇研究报道:“将稻壳灰(RHA)和粉煤灰(FA)与MC进行组合试验,充分挖掘其潜力。机器学习(ML)算法可以帮助配制出更好的MC基混凝土,并建立了预测含FA和RHA的MC基混凝土抗压强度(CS)的ML模型。”采用基因表达式编程(GEP)和多表达式编程(MEP)建立模型,并通过计算R~2值、进行统计检验、建立泰勒图、比较理论和实验数据对模型进行评价,比较MEP和GEP模型的拟合程度和预测性能(R~2=0.96,平均绝对误差=0.646.均方根误差=0.900，Nash-S Utcliffe效率=0.960）。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ar tificial intelligence. According to news originating from Yongzhou, People’s Rep ublic of China, by NewsRx correspondents, research stated, “Marble cement (MC) i s a new binding material for concrete, and the strength assessment of the result ing materials is the subject of this investigation.” Our news journalists obtained a quote from the research from Hunan University of Science and Engineering: “MC was tested in combination with rice husk ash (RHA) and fly ash (FA) to uncover its full potential. Machine learning (ML) algorithm s can help with the formulation of better MC-based concrete. ML models that coul d predict the compressive strength (CS) of MC-based concrete that contained FA a nd RHA were built. Gene expression programming (GEP) and multi-expression progra mming (MEP) were used to build these models. Additionally, models were evaluated by calculating R ~2 values, carrying out statistical tests, creating Taylor’s diagram, and comparin g theoretical and experimental readings. When comparing the MEP and GEP models, MEP yielded a slightly better-fitted model and better prediction performance (R ~2 = 0.96, mean absolute error = 0.646, root mean square error = 0.900, and Nash-S utcliffe efficiency = 0.960).”
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