红砂岩断裂机理智能识别研究

刘子达; 李地元; 朱泉企; 张晨曦; 马金银; 赵君杰

doi:10.1007/s12613-023-2668-8

Volume 30 Issue 11

Nov. 2023

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文章导航 > 矿物冶金与材料学报（英文） > 2023 > 30(11): 2134-2146

Zida Liu, Diyuan Li, Quanqi Zhu, Chenxi Zhang, Jinyin Ma, and Junjie Zhao, Intelligent method to experimentally identify the fracture mechanism of red sandstone, Int. J. Miner. Metall. Mater., 30(2023), No. 11, pp. 2134-2146. https://doi.org/10.1007/s12613-023-2668-8

Cite this article as:

Zida Liu, Diyuan Li, Quanqi Zhu, Chenxi Zhang, Jinyin Ma, and Junjie Zhao, Intelligent method to experimentally identify the fracture mechanism of red sandstone, Int. J. Miner. Metall. Mater., 30(2023), No. 11, pp. 2134-2146. https://doi.org/10.1007/s12613-023-2668-8

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研究论文

红砂岩断裂机理智能识别研究

中南大学资源与安全工程学院, 长沙 410083, 中国

通讯作者:
李地元 E-mail: diyuan.li@csu.edu.cn

文章亮点

(1) 获得了红砂岩在直接拉伸和变角剪试验下的拉伸及剪切断口细观形貌图像。

(2) 开发了深度学习模型，量化识别了红砂岩的细观拉伸和剪切断口特征。

(3) 开发的深度学习模型成功识别了红砂岩在单轴压缩和巴西劈裂下的断裂面破坏机理。

中文摘要

拉伸断裂和剪切断裂是岩石的主要破坏机理，深入分析这两类断裂模式有助于揭示岩石的破坏机理。扫描电子显微镜（SEM）已被广泛用于分析岩石的细观拉伸和剪切断裂机理。为了量化拉剪断裂机理，本研究提出了一种基于SEM图像和深度学习的方法来定量识别红砂岩中的拉伸和剪切断口。首先，对红砂岩进行直接拉伸和变角剪试验，获得了红砂岩的拉剪断口破坏面，并对其进行了SEM观察。其次，将获得的SEM图像用于开发深度学习模型（AlexNet，VGG13和SqueezeNet）。模型评估表明，VGG13为最佳模型，其测试准确率为0.985。而后，采用积分梯度算法对VGG13所学习到的红砂岩拉剪断口特征进行分析。最后，利用VGG13对红砂岩在单轴压缩和巴西劈裂试验后产生的岩石碎片破坏面上的拉剪断口分布和占比进行了识别，识别结果证明了该方法的可行性，表明该方法可以揭示岩石破坏机理。

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Research Article

Intelligent method to experimentally identify the fracture mechanism of red sandstone

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Abstract

Abstract

Tensile and shear fractures are significant mechanisms for rock failure. Understanding the fractures that occur in rock can reveal rock failure mechanisms. Scanning electron microscopy (SEM) has been widely used to analyze tensile and shear fractures of rock on a mesoscopic scale. To quantify tensile and shear fractures, this study proposed an innovative method composed of SEM images and deep learning techniques to identify tensile and shear fractures in red sandstone. First, direct tensile and preset angle shear tests were performed for red sandstone to produce representative tensile and shear fracture surfaces, which were then observed by SEM. Second, these obtained SEM images were applied to develop deep learning models (AlexNet, VGG13, and SqueezeNet). Model evaluation showed that VGG13 was the best model, with a testing accuracy of 0.985. Third, the features of tensile and shear fractures of red sandstone learned by VGG13 were analyzed by the integrated gradient algorithm. VGG13 was then implemented to identify the distribution and proportion of tensile and shear fractures on the failure surfaces of rock fragments caused by uniaxial compression and Brazilian splitting tests. Results demonstrated the model feasibility and suggested that the proposed method can reveal rock failure mechanisms.
- tensile and shear fractures;
- rock failure;
- deep learning;
- scanning electron microscopy;
- red sandstone

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红砂岩断裂机理智能识别研究

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Intelligent method to experimentally identify the fracture mechanism of red sandstone

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