摘要
由机器人与机器学习每日新闻的新闻记者兼工作人员新闻编辑-人工智能的新数据在一份新的报告中呈现。根据NewsRx记者在中华人民共和国上海的新闻报道，研究表明：“本研究提出了应用机器学习(ML)技术预测和分析聚乙烯纤维增强ECC(PE-ECC)力学性能的方法。”本研究的资助者包括中央大学基础研究基金、国家自然科学基金。我们的新闻记者从同济大学的研究中获得了一句话：“首次建立了一个包含不同力学性能的PE-ECC的综合数据库，总共有50个抗压强度，采用灰色关联分析方法对PE-ECC力学性能关键参数的敏感性进行了研究,结果表明:水泥辅助料胶比、水胶比、砂胶比、水泥辅助料胶比、水泥辅助采用三种具有代表性的ML技术对PE-ECC的力学性能进行了预测，并在ML模型的基础上进一步对所选参数对PE-ECC力学性能的影响进行了参数研究。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on artificial intelligence are presented in a new report. According to news reporting from Shanghai, People ’s Republic of China, by NewsRx journalists, research stated, “This study presen ts the implementation of machine learning (ML) techniques for mechanical propert ies prediction and analysis of polyethylene fiber-reinforced ECC (PE-ECC).” Funders for this research include Fundamental Research Funds For The Central Uni versities; National Natural Science Foundation of China. Our news correspondents obtained a quote from the research from Tongji Universit y: “A comprehensive database including different mechanical properties of PE-ECC was first constructed, with total 50 compressive strengths, 123 tensile strengt hs and 123 tensile strain capacities being assembled. Grey relational analysis w as used to investigate the sensitivity of the critical parameters of PE-ECC’s me chanical properties. The evaluation results showed that the supplementary cement itious materials-to-binder ratio, water-to-binder ratio, sand-to-binder ratio, a nd fiber reinforcing index have significant effects on the mechanical properties of PE-ECC. Three representative ML techniques were utilized and demonstrated go od predictive performance. A parametric study was further undertaken to quantify the effects of the selected parameters on the mechanical properties of PE-ECC b ased on the ML models.”
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