Prediction of geological characteristics from shield operational parameters by integrating grid search and K-fold cross validation into stacking classification algorithm

This study presents a framework for predicting geological characteristics based on integrating a stacking classification algorithm (SCA) with a grid search (GS) and K-fold cross validation (K-CV). The SCA includes two learner layers: a primary learner's layer and meta-classifier layer. The accu...

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Veröffentlicht in:	Journal of Rock Mechanics and Geotechnical Engineering Jg. 14; H. 4; S. 1292 - 1303
Hauptverfasser:	Yan, Tao, Shen, Shui-Long, Zhou, Annan, Chen, Xiangsheng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.08.2022 MOE Key Laboratory of Intelligent Manufacturing Technology,Department of Civil and Environmental Engineering,College of Engineering,Shantou University,Shantou,Guangdong 515063,China Discipline of Civil and Infrastructure,School of Engineering,Royal Melbourne Institute of Technology(RMIT),Victoria,3001,Australia%MOE Key Laboratory of Intelligent Manufacturing Technology,Department of Civil and Environmental Engineering,College of Engineering,Shantou University,Shantou,Guangdong 515063,China%Discipline of Civil and Infrastructure,School of Engineering,Royal Melbourne Institute of Technology(RMIT),Victoria,3001,Australia%College of Civil and Transportation Engineering,Shenzhen University,Shenzhen,Guangdong 518060,China Elsevier
Schlagworte:	Geological characteristics K-fold cross-validation (K-CV) K-means Stacking classification algorithm (SCA) Geological characteristics K-means Stacking classification algorithm (SCA) K-fold cross-validation (K-CV) K-fold cross-validation(K-CV) Stacking classification algorithm(SCA)
ISSN:	1674-7755
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