首页|Studies from Guangzhou University Yield New Data on Machine Learning (Prediction of Surface Settlement Caused By Synchronous Grouting During Shield Tunneling In Coarse-grained Soils: a Combined Fem and Machine Learning Approach)
Studies from Guangzhou University Yield New Data on Machine Learning (Prediction of Surface Settlement Caused By Synchronous Grouting During Shield Tunneling In Coarse-grained Soils: a Combined Fem and Machine Learning Approach)
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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.
GuangzhouPeople’s Republic of ChinaA siaAlgorithmsCyborgsEmerging TechnologiesMachine LearningGuangzhou Uni versity
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