摘要
一位新闻记者兼机器人与机器学习的新闻编辑每日新闻-结构工程的新研究是一篇新报道的主题。根据NewsRx编辑在马来西亚吉隆坡的新闻报道，研究表明：“混凝土是一种基本的建筑材料，是原始资源的重要消耗者，包括沙子、砾石、碎石和淡水。它产生了巨大的需求，新闻记者从英国马来西亚研究所的研究中获得了一句话：“此外，处理具有异常非线性行为的极端条件需要在结构分析和设计方法中费力的校准程序。这些方法在实践中也难以执行。为了减少时间和精力，ML可能是一个可行的选择。设计了一组关键词来执行带有过滤器的PubMed搜索引擎，以不搜索2015年以下的研究。此外，使用PRISMA指南，我们重新选择的研究，筛选后，总共总结了42项研究。PRIS MA指南提供了一个结构化的框架，以确保透明度、准确性和可扩展性。系统综述和META分析的方法和结果报告的完整性。综述研究往往缺乏系统和准确地连接文献不同部分的能力。原始研究中一些最棘手的部分包括知识映射、共引用和共现。利用这些数据，我们能够确定哪些地方在研究具体的机器学习应用方面最活跃。在产出和引文方面，影响最大的作者是哪里，哪些文章总体上引用最多。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on structural engineering is the subject of a new report. According to news reporting out of Kuala Lumpur , Malaysia, by NewsRx editors, research stated, “Concrete, a fundamental constru ction material, stands as a significant consumer of virgin resources, including sand, gravel, crushed stone, and fresh water. It exerts an immense demand, accou nting for approximately 1.6 billion metric tons of Portland and modified Portlan d cement annually.”The news journalists obtained a quote from the research from British Malaysian I nstitute: “Moreover, addressing extreme conditions with exceptionally nonlinear behavior necessitates a laborious calibration procedure in structural analysis a nd design methodologies. These methods are also difficult to execute in practice . To reduce time and effort, ML might be a viable option. A set of keywords are designed to perform the search PubMed search engine with filters to not search t he studies below the year 2015. Furthermore, using PRISMA guidelines, studies we re selected and after screening, a total of 42 studies were summarized. The PRIS MA guidelines provide a structured framework to ensure transparency, accuracy, a nd completeness in reporting the methods and results of systematic reviews and m eta-analyses. The ability to methodically and accurately connect disparate parts of the literature is often lacking in review research. Some of the trickiest pa rts of original research include knowledge mapping, co-citation, and co-occurren ce. Using this data, we were able to determine which locations were most active in researching machine learning applications for concrete, where the most influe ntial authors were in terms of both output and citations and which articles garn ered the most citations overall.”
NETL
NSTL