Prediction of joint roughness coefficient via hybrid machine learning model combined with principal components analysis  

作　　者：Shijie Xie Hang Lin Tianxing Ma Kang Peng Zhen Sun 

机构地区：[1]School of Resources and Safety Engineering,Central South University,Changsha,410083,China [2]Ocean College,Zhejiang University,Zhoushan,316021,China [3]School of Civil Engineering,Southeast University,Nanjing,210096,China

出　　处：《Journal of Rock Mechanics and Geotechnical Engineering》2025年第4期2291-2306,共16页岩石力学与岩土工程学报(英文)

基　　金：funding from the National Natural Science Foundation of China (Grant No.42277175);the pilot project of cooperation between the Ministry of Natural Resources and Hunan Province“Research and demonstration of key technologies for comprehensive remote sensing identification of geological hazards in typical regions of Hunan Province” (Grant No.2023ZRBSHZ056);the National Key Research and Development Program of China-2023 Key Special Project (Grant No.2023YFC2907400).

摘　　要：Joint roughness coefficient(JRC)is the most commonly used parameter for quantifying surface roughness of rock discontinuities in practice.The system composed of multiple roughness statistical parameters to measure JRC is a nonlinear system with a lot of overlapping information.In this paper,a dataset of eight roughness statistical parameters covering 112 digital joints is established.Then,the principal component analysis method is introduced to extract the significant information,which solves the information overlap problem of roughness characterization.Based on the two principal components of extracted features,the white shark optimizer algorithm was introduced to optimize the extreme gradient boosting model,and a new machine learning(ML)prediction model was established.The prediction accuracy of the new model and the other 17 models was measured using statistical metrics.The results show that the prediction result of the new model is more consistent with the real JRC value,with higher recognition accuracy and generalization ability.

关 键 词：Rock discontinuities Joint roughness coefficient(JRC) Roughness characterization Principal components analysis(PCA) Machine learning 

分 类 号：TU45[建筑科学—岩土工程]