首页|New Findings on Machine Learning from Tongji University Summarized (Thickness Re gression for Backfill Grouting of Shield Tunnels Based On Gpr Data and Catboost & Bo-tpe: a Full-scale Model Test Study)
New Findings on Machine Learning from Tongji University Summarized (Thickness Re gression for Backfill Grouting of Shield Tunnels Based On Gpr Data and Catboost & Bo-tpe: a Full-scale Model Test Study)
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
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Current study results on Machine Learn ing have been published. According to news reporting originating in Shanghai, Pe ople’s Republic of China, by NewsRx journalists, research stated,“Ground penetr ating radar (GPR) is a vital non-destructive testing (NDT) technology that can b eemployed for detecting the backfill grouting of shield tunnels. To achieve int elligent analysis of GPR dataand overcome the subjectivity of traditional data processing methods, the CatBoost & BO-TPE model wasconstructed fo r regressing the grouting thickness based on GPR waveforms.”
ShanghaiPeople’s Republic of ChinaAs iaCyborgsEmerging TechnologiesMachine LearningTongji University
NETL
NSTL
