[Objective]Estimating the joint roughness coefficient(JRC)is essential for evaluating the mechanical properties of a rock mass.Due to the limitation of a single statistical parameter for characterizing morphology,JRC values estimation by a single statistical parameter may produce a sufficiently unreliable result.[Methods]To ad-dress the existing challenges in determining JRC values,a model based on Gaussian process regression(GPR)combined with principal component analysis(PCA)was proposed for the quantitative evaluation of JRC.Notably,eight parameters were selected as indicators for the comprehensive expression of the rock joint roughness.To ana-lyse the model's performance,a publicly available dataset of 112 rock joint profiles was used as an example,of which 95 were chosen as training samples and 17 were chosen as validation samples.The reliability of the model was verified by comparing the predicted results with the measured JRC values.[Results]The results show that the derived GPR model demonstrates promising performance(R2=0.972,MSE=0.517)for estimation of JRC values,indicating the high applicability of the model in constructing implicit relationships between multiple statistical pa-rameters and JRC values even under small sample conditions.[Conclusion]In general,the GPR model may pro-vide a new way of estimating JRC values with artificial intelligence.
关键词
岩体结构面/粗糙度/高斯过程回归/统计参数/预测
Key words
rock joints/roughness/Gaussian process regression/statistical parameter/prediction
维普
万方数据