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Jiaxin Wang, Hongru Jiang, Shunchuan Wu, Shihuai Zhang, Chaoqun Chu, Xiaoping Zhang, and Yingming Xiao, Intelligent prediction of deep rock strength based on modified three-dimensional Hoek–Brown criterion, Int. J. Miner. Metall. Mater., (2026). https://doi.org/10.1007/s12613-026-3513-7
Jiaxin Wang, Hongru Jiang, Shunchuan Wu, Shihuai Zhang, Chaoqun Chu, Xiaoping Zhang, and Yingming Xiao, Intelligent prediction of deep rock strength based on modified three-dimensional Hoek–Brown criterion, Int. J. Miner. Metall. Mater., (2026). https://doi.org/10.1007/s12613-026-3513-7
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基于修正三维Hoek–Brown准则的深部岩石强度智能预测

摘要: 为研究深部岩石强度,本文提出一种五参数偏函数修正Hoek–Brown (HB)准则,引入智能优化算法确定材料参数,构建一种修正三维 (3D) HB准则(命名为MMCHB准则)。该准则解决了HB准则未考虑中间主应力及未满足光滑性的问题,同时解决了基于常规方法确定材料参数导致单一偏平面包络线形状的缺陷。所提MMCHB准则可退化为三轴压缩和拉伸条件下的HB准则,采用6种完整岩石真三轴试验数据验证所提MMCHB准则,并选取多种同类方法修正的3D HB准则进行对比研究。结果表明,在智能优化算法下所提MMCHB准则对6种岩石强度的预测误差最优,其误差在1.6636%–3.4023%范围内。总体上,MMCHB准则的预测性能优于现有6种修正3D HB准则。基于所提MMCHB准则,开发了一套智能预测系统,为深部岩石强度智能预测和材料参数动态构建提供了新的途径。

 

Intelligent prediction of deep rock strength based on modified three-dimensional Hoek–Brown criterion

Abstract: To study the deep rock strength, this paper proposes a five-parameter deviatoric function to modify the deviatoric function of the Hoek–Brown (HB) criterion, introduces an intelligent optimization algorithm (IOA) to determine the material parameters, thereby constructing a modified three-dimensional (3D) HB criterion, namely MMCHB criterion. The MMCHB criterion avoids the defects of the traditional HB criterion, which neither considers the Intermediate principal stress (IPS) nor meets the smoothness requirement, and overcomes the shortcomings of parameter determination based on conventional methods, which can lead to a single deviatoric plane envelope shape. This modified criterion can be degenerated into the HB criterion under triaxial compression and tension. The proposed criterion is verified using true triaxial test data for six types of intact rock, and the modified 3D HB criteria are selected for comparative study. The results show that the proposed criterion under the IOA has the best prediction error for the six rock types, ranging from 1.6636% to 3.4023%. Overall, the MMCHB criterion outperforms the existing modified 3D HB criteria in prediction. Based on the proposed MMCHB criterion, an intelligent prediction system is developed, which provides a new approach for intelligent prediction of deep rock strength and dynamic construction of rock material parameters.

 

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