The paper presents an innovative numerical approach to simulate progressive caving of strata above a longwall coal mining panel. A proposed Trigon logic is incorporated within UDEC to successfully capture the progressive caving of strata which is characterized by fracture generation and subsequent propagation. A new damage index, D, is proposed that can quantify regions of both compressive shear and tensile failure within the modelled longwall. Many features of progressive caving are reproduced in the model and found to fit reasonably well with field observations taken from a case study in the Ruhr coalfield. The modelling study reveals that compressive shear failure, rather than tensile failure, is the dominant failure mechanism in the caved strata above the mined-out area. The immediate roof beds act like beams and can collapse in beam bending when vertical stress is dominant or in beam shear fracture when horizontal stress is dominant. The proposed numerical approach can be used to guide the design of longwall panel layout and rock support mechanisms.
This article presents a novel deep learning method for semi-automated detection of historic mining pits using aerial LiDAR data. The recent emergence of national scale remotely sensed datasets has created the potential to greatly increase the rate of analysis and recording of cultural heritage sites. However, the time and resources required to process these datasets in traditional desktop surveys presents a near insurmountable challenge. The use of artificial intelligence to carry out preliminary processing of vast areas could enable experts to prioritize their prospection focus; however, success so far has been hindered by the lack of large training datasets in this field. This study develops an innovative transfer learning approach, utilizing a deep convolutional neural network initially trained on Lunar LiDAR datasets and reapplied here in an archaeological context. Recall rates of 80% and 83% were obtained on the 0.5 m and 0.25 m resolution datasets respectively, with false positive rates maintained below 20%. These results are state of the art and demonstrate that this model is an efficient, effective tool for semi-automated object detection for this type of archaeological objects. Further tests indicated strong potential for detection of other types of archaeological objects when trained accordingly.
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