首页|Study Findings from Shanghai University Broaden Understanding of Machine Learning [Shield Tunnel (Segment) Uplift Prediction and Control Based on Interpretable Machine Learning]
Study Findings from Shanghai University Broaden Understanding of Machine Learning [Shield Tunnel (Segment) Uplift Prediction and Control Based on Interpretable Machine Learning]
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Mdpi
Investigators publish new report on artificial intelligence. According to news reporting originating from Shanghai, People’s Republic of China, by NewsRx correspondents, research stated, “Shield tunnel segment uplift is a common phenomenon in construction.” Our news editors obtained a quote from the research from Shanghai University: “Excessive and unstable uplift will affect tunnel quality and safety seriously, shorten the tunnel life, and is not conducive to the sustainable management of the tunnel’s entire life cycle. However, segment uplift is affected by many factors, and it is challenging to predict the uplift amount and determine its cause accurately. Existing research mainly focuses on analyzing uplift factors and the uplift trend features for specific projects, which is difficult to apply to actual projects directly. This paper sorts out the influencing factors of segment uplift and designs a spatial-temporal data fusion mechanism for prediction.”
Shanghai UniversityShanghaiPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
Mdpi
