Prediction model of joint roughness coefficient based on Gaussian process regression
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摘要: 由于岩体结构面几何形貌的复杂性,单一统计参数法估算结构面粗糙度系数(JRC)精度较低。针对目前JRC难获取的问题,选取表征结构面粗糙形态的8种统计参数,结合主成分分析法(PCA)和高斯过程回归(GPR)算法,构建基于多参数融合的JRC预测模型。以公开的112条岩体结构面剖面线数据集(95条训练样本、17条验证样本)为例进行分析研究,利用试验所得JRC对比分析预测效果。结果表明:由高斯过程回归构建的JRC预测模型决定系数高达0.972,均方根误差(MSE)为0.517,反映出高斯过程回归方法对于小样本的复杂多参数JRC值预测的适用性,为今后人工智能在JRC指标预测方面实现合理预测提供思路。Abstract: The joint roughness coefficient (JRC) estimation may produce a sufficiently unreliable result, due to limitation of single statistical parameter method for characterizing morphology. 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. In order to analyze the model’s performance, 112 published rock joint profiles were used as the database, of which 95 were chosen as training database and 17 as validation database. The reliability of the model was verified by comparing the predicted results with the measured data. Results show that the derived GPR model demonstrates promising performance (R^2=0.972, MSE=0.517) for estimating JRC, reflecting the high applicability for multi-parameter JRC prediction even when the number of training dataset is small. In general, the GPR model may provide a new way of thinking about estimating JRC values with artificially intelligent.
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Key words:
- rock joints /
- roughness /
- Gaussian process regression /
- statistical parameter /
- prediction
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