摘要
由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑每日新闻-一项关于机器学习的新研究现在可用。根据新闻报道由NewsRx记者在中华人民共和国三亚发起的研究称，“鉴于建立精确数学模型表征界面粘结强度的困难采用“极梯度提升”(XGBoost)钢管混凝土(CFST)作为计算本文的键行为捕获工具。水灰比与混凝土抗压强度强度作为输入变量，钢的强度等级、长度、厚度和直径管和推广系数"。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – A new study on Machine Learning is now available. According to news reportingoriginating in Sanya, People’s Republic of China, by NewsRx journalists, research stated, “In view of thedifficulty in establishing an accurate mathematical model to characterize the interfacial bond strength inconcrete-filled steel tube (CFST), the ‘eXtreme Gradient Boosting’ (XGBoost) is utilized as a computationaltool for capturing the bond behavior in this paper. The water-to-cement ratio and concrete compressivestrength are tak en as input variables, as well as strength grade, length, thickness and diameter of steeltube and a generalization coefficient.”
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