张鹏, 利铭, 姚海波等. 岩石变形局部化智能识别的DSCM-CNN方法. 力学与实践, 2024, 46(1): 105-115. doi: 10.6052/1000-0879-23-195
引用本文: 张鹏, 利铭, 姚海波等. 岩石变形局部化智能识别的DSCM-CNN方法. 力学与实践, 2024, 46(1): 105-115. doi: 10.6052/1000-0879-23-195
Zhang Peng, Li Ming, Yao Haibo, et al. Intelligent identification of rock deformation localization by DSCM-CNN method. Mechanics in Engineering, 2024, 46(1): 105-115. doi: 10.6052/1000-0879-23-195
Citation: Zhang Peng, Li Ming, Yao Haibo, et al. Intelligent identification of rock deformation localization by DSCM-CNN method. Mechanics in Engineering, 2024, 46(1): 105-115. doi: 10.6052/1000-0879-23-195

岩石变形局部化智能识别的DSCM-CNN方法

INTELLIGENT IDENTIFICATION OF ROCK DEFORMATION LOCALIZATION BY DSCM-CNN METHOD

  • 摘要: 岩石变形局部化的识别对于岩石破坏机理、岩土工程灾害预测预警有着重要的意义。本文将数字散斑相关方法(digital speckle correlation methods,DSCM)与卷积神经网络(convolutional neural networks,CNN)相结合,提出了一种用于岩石变形局部化智能识别的DSCM-CNN模型。通过DSCM获取岩石试件在单轴压缩实验过程中的最大剪应变场云图,根据变形局部化带位置进行标注,完成数据集的构建;利用训练数据集对DSCM-CNN智能识别模型进行训练。通过红砂岩单轴压缩实验对该方法进行验证,结果表明:DSCM-CNN模型可以实现岩石变形局部化带位置的自动识别,子集准确率、精确度、召回率等指标分别达到94.19%,97.21%和96.41%,证明了岩石变形局部化智能识别的DSCM-CNN模型的可行性,为岩石变形局部化智能监测提供了一种新的思路。

     

    Abstract: Identifying rock deformation localization is of great significance for the early warning and prediction of rock damage and geotechnical disaster. In this paper, a DSCM-CNN model is proposed for the intelligent recognition of rock deformation localization by combining digital speckle correlation methods (DSCM) and convolutional neural networks (CNN). The maximum shear strain field nephograms of rock specimens during uniaxial compression tests are obtained by DSCM, and then the nephograms are labelled according to the position of the deformation localization zones to complete construction of the dataset. The proposed DSCM-CNN intelligent identification model is trained by the training dataset. The method is validated by uniaxial compression tests on red sandstone specimens. The proposed DSCM-CNN intelligent identification model can automatically identify the rock deformation localization zones. The subset accuracy, precision and recall are 94.19%, 97.21%, and 96.41% respectively, which proves the feasibility of the DSCM-CNN model. The proposed intelligent identification DSCM-CNN model provides a new idea for intelligent monitoring of rock deformation localization.

     

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