Investigators from Xi’an Jiaotong University Have Reported New Data on Machine L earning (Review and Comparison of Machine Learning Methods In Developing Optimal Models for Predicting Geotechnical Properties With Consideration of Feature ... )

西安交通大学的研究人员报告了机器学习的新数据(机器学习方法在开发考虑特征的岩土特性预测优化模型中的回顾和比较 ... )

Robotics & Machine Learning Daily News2024，Issue(Dec.3) ：101-101.

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Investigators from Xi’an Jiaotong University Have Reported New Data on Machine L earning (Review and Comparison of Machine Learning Methods In Developing Optimal Models for Predicting Geotechnical Properties With Consideration of Feature ... )

西安交通大学的研究人员报告了机器学习的新数据(机器学习方法在开发考虑特征的岩土特性预测优化模型中的回顾和比较 ... )

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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-关于机器学习的详细数据已经呈现。根据消息来源来自中华人民共和国陕西省，由NewsRx记者报道，研究称：“岩土工程”其中，粘聚力、桩身可打性、岩石强度等性质是最重要的、必不可少的岩土工程设计或分析的输入。传统上，这些特性是从实验室实验中获得的，这些实验有准备好的样品或精心设计的现场实验。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Data detailed on Machine Learning have been presented. According to news originatingfrom Shaanxi, People’s Republic o f China, by NewsRx correspondents, research stated, “Geotechnicalproperties, su ch as cohesion, pile drivability, rock strength, is one of the most important an d indispensableinput for design or analysis of geotechnical/geological engineer ing projects. Conventionally, these propertiesare obtained from laboratory expe riments with well-prepared samples or well-designed experiments in-situ.”

Key words

Shaanxi/People’s Republic of China/Asi a/Cyborgs/Emerging Technologies/Engineering/Machine Learning/Xi’an Jiaotong University

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出版年

2024

Robotics & Machine Learning Daily News

ISSN：

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