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Uniaxial Compressive Strength Determination of Rocks Using X-ray Computed Tomography and Convolutional Neural Networks
Rock Mechanics and Rock Engineering ( IF 6.2 ) Pub Date : 2021-05-31 , DOI: 10.1007/s00603-021-02503-1
Huan Sun , Weisheng Du , Chi Liu

The uniaxial compressive strength (UCS) is an important parameter for rock mass classification and rock engineering designs. This study proposes a novel method for predicting the UCS of rocks using X-ray computed tomography and convolutional neural networks. First, X-ray CT scanning was conducted on five mudstone specimens. The volume data characteristics of the different density compositions in rock specimens were extracted from the CT slices according to the CT thresholds. Then, the function between the cumulative CT value and the peak strength was established. The given CT image data samples of a certain rock correspond to the scope of the predictive UCS. To approve the accuracy of the UCS prediction of rocks, X-ray CT slices pre-processed with the Laplacian of Gaussian (LOG) algorithm were used to enhance the feature sharpness of the compositions in the rocks. Then, a convolutional neural network (CNN) technique based on the stochastic pooling method was applied to the LOG images of the X-ray slices to estimate the UCS of the rocks. This proposed method shows superior performance for the UCS prediction of rocks and could be widely used in the future of artificial intelligence rock engineering.



中文翻译:

使用 X 射线计算机断层扫描和卷积神经网络测定岩石的单轴抗压强度

单轴抗压强度 (UCS) 是岩体分类和岩石工程设计的重要参数。本研究提出了一种使用 X 射线计算机断层扫描和卷积神经网络预测岩石 UCS 的新方法。首先,对五个泥岩标本进行了 X 射线 CT 扫描。根据CT阈值从CT切片中提取岩石标本中不同密度成分的体积数据特征。然后,建立累积CT值与峰值强度之间的函数。给定的某块岩石的 CT 图像数据样本对应于预测 UCS 的范围。批准 UCS 岩石预测的准确性,使用高斯拉普拉斯算子 (LOG) 算法预处理的 X 射线 CT 切片用于增强岩石中成分的特征清晰度。然后,将基于随机池化方法的卷积神经网络 (CNN) 技术应用于 X 射线切片的 LOG 图像以估计岩石的 UCS。该方法在岩石UCS预测方面表现出优越的性能,可广泛应用于人工智能岩石工程的未来。

更新日期:2021-05-31
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