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This is a complete list of Product Key for all Autodesk 2015 products. Press Ctrl + F to find the key for your product.This post will continue to be updated and the latest edits should follow IGGTech.

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The high performance of deep learning in computer vision was first demonstrated in classification tasks. Many CNN models can provide good classification accuracy such as Vgg [28], ResNet [29], Xception [30], and so on. Some of them are applied as feature extractors in segmentation models. FCN replaces the fully connected layer in the classification model with deconvolution to upsample the pooled feature map to its original size, pioneered semantic segmentation. The application of deep learning in crack detection can be roughly divided into three types, methods based on classification [31], object detection [32], and semantic segmentation [33, 34]. Xue et al. [35] modified the last few deconvolution modules of FCN to adapt to the needs of crack segmentation. However, this FCN-based method may not be able to guarantee the accuracy of segmentation and maintain the original topological structure of the crack when facing the crack of complex structures.

where M refers to the number of categories, yic refers to the sign function (0 or 1), and pic refers to the predicted probability that the observed sample i belongs to category c. Thus, we can consider that the pixels in the image are learned equally with the cross-entropy loss function, and this kind of equality does not apply to the situation where the sample is extremely uneven. In coal crack CT images, the number of pixels corresponding to the crack is much smaller than that of the background. Taking the dataset we established as an example, the proportion of crack pixels in the whole image is less than 5%. Dice Loss [39] was proposed in 2016, designed to deal with scenarios where positive and negative samples are strongly imbalanced in semantic segmentation. Different from distribution-based cross-entropy loss, the Dice function is based on region and is used to calculate the similarity between two images. The Dice coefficient and Dice loss function can be formulated as follows:

where β is a weight coefficient for adjusting the proportion of CE function. It is a constant in the range [0, 1], and the value of this article is 0.5. The experiment proved that this new loss function effectively improves the accuracy of crack segmentation compared to using the cross-entropy loss function alone.

A comparison of evaluation metrics of all these methods is shown in Table 4. As we can see, since the proportion of cracks in the images is very low, and the judgment error rate of image background pixels is low so that the total pixel accuracy of every method is not very different. However, the performance of different methods can still be judged from the remaining evaluation indicators. PSPNet and FCN may have good performance in semantic segmentation under natural conditions, but they do not perform well on the coal crack CT image dataset. U-net is designed to deal with medical images which have similarities with the images we used, so this model can have a nice performance. As the best performing comparison method, U-net achieved an Acc of 98.5%, mAcc of 94.2, MIoU of 86.5%, and a FWIoU of 97.2% which are 0.1%, 1.2%, 2.9%, and 0.5% lower than proposed method. A histogram comparison of the experimental results is shown in Figure 8.

Due to the extreme complexity of mechanical response of soft surrounding rock (SR) around a tunnel under high geostatic stress conditions, the integration of physical and numerical modeling techniques was adopted. Based on the similarity theory, new composite-similar material was developed, which showed good agreement with the similarity relation and successfully simulated physico-mechanical properties (PMP) of deep buried soft rock. And the 800 mm800 mm200 mm physical model (PM) was conducted, in which the endoscopic camera technique was adopted to track the entire process of failure of the model all the time. The experimental results indicate that the deformation of SR around a underground cavern possessed the characteristics of development by stages and in delay, and the initial damage of SR could induce rapid failure in the later stage, and the whole process could be divided into three stages, including the localized extension of crack(the horizontal load (HL) was in the range of 130 kN to 170 kN, the vertical load (VL) was in the range of 119 kN to 153.8 kN), rapid crack coalescence (the HL was in the range of 170 kN to 210 kN, the VL was in the range of 153.8 kN to 182.5 kN) and residual strength (the HL was greater than 210 kN, the VL was greater than 182.5 kN). Under the high stress conditions, the phenomenon of deformation localization in the SR became serious and different space positions show different deformation characteristics. In order to further explore the deformation localization and progressive failure phenomenon of soft SR around the deeply buried tunnel, applying the analysis software of FLAC3D three-dimensional explicit finite-difference method, based on the composite strain-softening model of Mohr-Coulomb shear failure and tensile failure, the calculation method of large deformation was adopted. Then, the comparative analysis between the PM experiment and numerical simulation of the three centered arch tunnels was implemented and the relationship of deformation localization and progressive failure of SR around a tunnel under high stress conditions was discussed.

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Abstract:Efficient fracturing is the key issue for the exploitation of geothermal energy in a Hot Dry Rock reservoir. By using the laser irradiation cracking method, this study investigates the changes in uniaxial compressive strength, ultrasonic characteristics and crack distributions of granite specimens by applying a laser beam under various irradiation conditions, including different powers, diameters and moving speeds of the laser beam. The results indicate that the uniaxial compressive strength is considerably dependent on the power, diameter and moving speed of the laser beam. The ultrasonic-wave velocity and amplitude of the first wave both increase with a decreased laser power, increased diameter or moving speed of the laser beam. The wave form of irradiated graphite is flattened by laser irradiation comparing with that of the original specimen without laser irradiation. The crack angle and the ratio of the cracked area at both ends are also related to the irradiation parameters. The interior cracks are observed to be well-developed around the bottom of the grooving kerf generated by the laser beam. The results indicate that laser irradiation is a new economical and practical method that can efficiently fracture graphite.Keywords: thermal irradiation; laser cracking; mechanical property; ultrasonic characteristic; crack distribution 2ff7e9595c