基于机器学习的含充填裂隙岩样断裂行为预测方法

doi:10.13722/j.cnki.jrme.2022.0284

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摘要含不连续面的岩样断裂行为难以量化表征和有效精准预测。采用机器学习方法建立预测含胶结充填裂隙岩样I型断裂韧度和裂纹扩展模式的数据驱动模型。首先，通过含充填裂隙半圆盘直切口试件的三点弯试验和离散元数值模拟结果，对比验证批量模拟的可行性，再进行批量随机数值模拟，构建数据集并过滤异常算例。对数据集进行归一化、随机打乱、随机欠采样等预处理，建立4个经典机器学习模型，采用网格搜索与五折交叉验证算法搜索最优超参数，并采用多个建立在总体上的评估指标和反映误差分布的数据图对模型性能进行综合评估，得出多层感知机模型为预测精度最高的机器学习模型；对模型进行敏感性分析，并应用于试验数据的预测，结果表明多层感知机模型的稳定性和预测效果较好。此外，该研究还开发了用户界面程序用于含充填裂隙岩样断裂行为的批量预测，避免传统试验和数值模拟高昂的时间成本，为解决传统岩石力学问题提供新的方法和思路。

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	陈进帆1
	尚德磊1
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	赵志宏1
	陈朝伟4

关键词 ：岩石力学, 断裂韧度, 机器学习, 离散元, 不连续面

Abstract：Fracture behaviors of rock samples with discontinuities is difficult to quantify and predict. In this study，machine learning method is used to predict mode I fracture toughness and crack propagation mode of rock samples with an infilled fracture. Firstly，the feasibility of the method was verified by comparison of results of notched semi-circle bend test and numerical simulation based on discrete element method，and the dataset which filtered the outliers was generated by a lot of random numerical simulations. Multiple techniques were used in the preprocessing of dataset. Then four classical machine learning models were established. The optimal parameters of the models were obtained by grid search and five-fold cross-validation method. Multiple indicators and graphs were used to verify the prediction performances of the models. Comparison between the four models shows that Multilayer Perceptron(MLP) is the optimal machine learning model. The sensitivity of the MLP model was analyzed and the stability is good. The MLP model was applied to the test samples，which has high prediction accuracy. And a user interface program was developed. The data driven model of rock mechanics based on the machine learning method reduces time cost compared with the traditional test and numerical simulation，which provides a new method and thought for solving traditional rock engineering problems.

Key words： rock mechanics fracture toughness machine learning discrete element method discontinuities

引用本文:

陈进帆1，尚德磊1，2，3，赵志宏1，陈朝伟4. 基于机器学习的含充填裂隙岩样断裂行为预测方法[J]. 岩石力学与工程学报, 2023, 42(S1): 3458-3472.
CHEN Jinfan1，SHANG Delei1，2，3，ZHAO Zhihong1，CHEN Zhaowei4. Prediction method of fracture behaviors of rock samples with an infilled fracture based on machine learning. , 2023, 42(S1): 3458-3472.

链接本文:

http://rockmech.whrsm.ac.cn/CN/10.13722/j.cnki.jrme.2022.0284 或 http://rockmech.whrsm.ac.cn/CN/Y2023/V42/IS1/3458

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