No abstract

Obtaining real-time, objective, and high-precision distribution information of surface cracks in mining areas is the first task for studying the development regularity of surface cracks and evaluating the risk. The complex geological environment in the mining area leads to low accuracy and efficiency of the existing extracting cracks methods from unmanned air vehicle (UAV) images. Therefore, this manuscript proposes a new identification method of surface cracks from UAV images based on machine learning in coal mining areas. First, the acquired UAV image is cut into small sub-images, and divided into four datasets according to the characteristics of background information: Bright Ground, Dark Dround, Withered Vegetation, and Green Vegetation. Then, for each dataset, a training sample is established with cracks and no cracks as labels and the RGB (red, green, and blue) three-band value of the sub-image as feature. Finally, the best machine learning algorithms, dimensionality reduction methods and image processing techniques are obtained through comparative analysis. The results show that using the V-SVM (Support vector machine with V as penalty function) machine learning algorithm, principal component analysis (PCA) to reduce the full features to 95% of the original variance, and image color enhancement by Laplace sharpening, the overall accuracy could reach 88.99%. This proves that the method proposed in this manuscript can achieve high-precision crack extraction from UAV image.

Universal distinct element code (UDEC) is a simulation software based on the discrete element method, widely used in geotechnical mining. However, in the UDEC, when simulating large-scale excavation, the subsidence of the fractured zone is almost equal to the mining height, which makes the deformation value calculated in the study of gob-side entry retention too large. To solve this problem, in this paper, the double-yield constitutive model is applied to the whole caving zone to analyze the deformation and failure characteristics of surrounding rock along gob-side entry retaining by roof cutting. The results of the simulation are in good agreement with the result of drilling peeking (drilling observation by borehole televiewer) and field condition (observation and measurement in the field). Finally, by using this numerical method, the effects of roadway width, temporary support, and coal side support on the failure of the roof and the arc coal side are studied.