Litcius/Paper detail

Multi-source data driven method for assessing the rock mass quality of a NATM tunnel face via hybrid ensemble learning models

Mingliang Zhou, Jiayao Chen, Hongwei Huang, Dongming Zhang, Shuai Zhao, Mahdi Shadabfar

2021International Journal of Rock Mechanics and Mining Sciences59 citationsDOI

Topics & Concepts

Rock mass classificationEnsemble learningNew Austrian Tunnelling methodRandom forestGradient boostingBoosting (machine learning)Rock mass ratingEnsemble forecastingArtificial intelligenceComputer sciencePerceptronMachine learningTree (set theory)Feature (linguistics)Data miningGeologyExcavationArtificial neural networkMathematicsGeotechnical engineeringMathematical analysisLinguisticsPhilosophyRock Mechanics and ModelingTunneling and Rock MechanicsGeotechnical Engineering and Analysis
Multi-source data driven method for assessing the rock mass quality of a NATM tunnel face via hybrid ensemble learning models | Litcius