理论与试验研究

基于XGBoost及插值优化的岩石JRC预测研究

  • 汪钊毅 ,
  • 郑飞 ,
  • 李芷 ,
  • 安雪锋 ,
  • 莫承龙
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  • 1.中国地质大学(武汉) 未来技术学院,武汉 430074;
    2.中国地质大学(武汉) 工程学院,武汉 430074
汪钊毅(2000—),男,甘肃兰州人,硕士生,主要从事岩土工程、地下工程等领域的科研工作。E-mail:915793899@qq.com
李芷(1991—),女,山东东营人,博士,讲师,主要从事岩石力学、地下工程风险等领域的研究工作。E-mail:lizhiprchina@hotmail.com

收稿日期: 2025-05-13

  网络出版日期: 2026-03-03

基金资助

国家自然科学基金青年基金项目(42107197,42207217)

Prediction of Rock JRC Based on XGBoost and Interpolation Optimization

  • Wang Zhaoyi ,
  • Zheng Fei ,
  • Li Zhi ,
  • An Xuefeng ,
  • Mo Chenglong
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  • 1. School of Future Technology, China University of Geoscience (Wuhan), Wuhan 430074, P.R. China;
    2. Faculty of Engineering, China University of Geoscience (Wuhan), Wuhan 430074, P.R. China

Received date: 2025-05-13

  Online published: 2026-03-03

摘要

岩石节理粗糙系数(JRC)对岩石节理的力学响应及岩体稳定性有重要影响,可基于节理剖面线几何形态对其进行预测。本研究构建了一种极限梯度提升(XGBoost)和贝叶斯优化(BO)融合的模型(XGBooost-BO),通过岩石节理剖面线的几何特征来预测岩石节理剖面线的JRC系数,并研究了样本数和特征指标对准确性和效率的影响。具体包括:(1)采用了公开的112条岩石结构面剖面线数据,使用了多种插值算法扩充样本数据集,比较了基于不同插值算法扩充数据集的预测效果;(2)使用扩充后的样本数据集(共448条,其中336条训练样本,112条测试样本)进行分析研究,并利用SHAP对模型进行分析,采用了可决系数(R2)、平均绝对百分比误差(EMAPE)、平均绝对误差(EMAE)、均方根误差(ERMSE)作为模型预测性能指标进行评价。结果表明:XGBoost-BO模型在岩石节理JRC系数预测中表现出了良好的性能;通过插值算法对原始样本数据进行扩充后的XGBoost-BO模型预测精度(R2=0.912 1、EMAPE=0.102 3、EMAE=0.755 7、ERMSE=1.265 3)较原样本数据的预测精度(R2=0.862 7、EMAPE=0.189 5、EMAE=1.3418、ERMSE=1.751 3)更好。

本文引用格式

汪钊毅 , 郑飞 , 李芷 , 安雪锋 , 莫承龙 . 基于XGBoost及插值优化的岩石JRC预测研究[J]. 地下空间与工程学报, 2026 , 22(1) : 47 -59 . DOI: 10.20174/j.JUSE.2026.01.06

Abstract

The joint roughness coefficient (JRC) of rock significantly influences the mechanical response and stability of rock joints, and it can be predicted based on the joint profile line geometry. This study developed a model, XGBoost-BO, which integrates eXtreme Gradient Boosting (XGBoost) and Bayesian Optimization (BO) to predict the JRC of rock joint profiles and investigated the impact of sample size and feature indicators on prediction accuracy and efficiency. Specifically, it includes: (1) Publicly available data from 112 rock structural surface profiles were used to expand the sample dataset using various interpolation algorithms, and the effects of expanding the dataset based on different interpolation algorithms were compared for prediction accuracy. (2) Analytical studies were conducted using the expanded sample dataset (448 samples in total, with 336 training samples and 112 test samples), and the model was analyzed using Shapley Additive Explanations (SHAP) for machine learning. R2, Mean Absolute Percentage Error (EMAPE), Mean Absolute Error (EMAE), and Root Mean Square Error (ERMSE) were used as performance metrics to evaluate the model's prediction accuracy. The results indicate that: The XGBoost-BO model performs well in predicting the JRC coefficients of rock joints. The prediction accuracy of the XGBoost-BO model (R2=0.912 1, EMAPE=0.102 3, EMAE=0.755 7, ERMSE=1.265 3), expanded by interpolation algorithm from the original sample data, surpasses that of the original sample data (R2=0.862 7, EMAPE=0.189 5, EMAE=1.341 8, ERMSE=1.751 3).

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