摘要
由一名新闻记者兼机器人与机器学习每日新闻的工作人员新闻编辑-机器学习的新数据在一份新的报告中呈现。根据NewsRx编辑对广州的新闻报道，研究表明：“本文提出了一种预测盾构掘进过程中同步固结引起地表沉降的替代模型法，该方法将有限元模拟与机器学习算法相结合，引入了一种反演地质参数和同步注浆变量的智能优化算法。”从而在不进行数值有限元分析的情况下预测地表沉降。本研究的资助单位包括国家自然科学基金(NSFC)、广州科技计划。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Fresh data on Machine Learning are presented in a new report. According to news reporting out of Guangzhou, People’s Republic of China, by NewsRx editors, research stated, “This paper presents a surrogate mode ling approach for predicting ground surface settlement caused by synchronous gro uting during shield tunneling process. The proposed method combines finite eleme nt simulations with machine learning algorithms and introduces an intelligent op timization algorithm to invert geological parameters and synchronous grouting va riables, thereby predicting ground surface settlement without conducting numerou s finite element analyses.” Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Science and Technology Program of Guangzhou, China.
NETL
NSTL