The Comprehensive Identification of Roof Risk in a Fully Mechanized Working Face Using the Cloud Model

Abstract

Roof accidents seriously affect the safe and efficient mining of the working faces. Therefore, it is necessary to assess and identify the possible and influencing factors on the occurrence of roof risk in a fully mechanized mining workface. In this study, based on the analytic hierarchy process and fuzzy comprehensive evaluation, a comprehensive standard cloud model was established through constructing a quantitative grade interval and calculating the weight of each index to achieve the aim of a roof risk assessment and identification. The accuracy of risk assessment was ensured by using the comprehensive analyses of various aspects, such as cloud digital features, risk assessment cloud image and standard cloud image. This showed that the main influencing factors on the occurrence of roof accidents were roof separation distance, weighting intensity and rib spalling followed by the coal body stress concentration, initial support force and geological conditions. Taking 42,115 fully mechanized working faces in the Yushen coal mining area as an engineering background, this model was adopted to assess and identify the risk of roof accidents through generating comprehensive assessment cloud images and introducing the Dice coefficient to calculate the similarity degree. The results showed that the overall risk of roof accidents in 42,115 working faces was regarded as grade II (general risk) through the overall index of comprehensive risk evaluation and a similarity degree of 0.8606. The impact of roof condition was mainly influenced by the risk of roof accidents, while the support status, personal working status and coal body condition had a limited effect on the risk of roof accidents. The comprehensive standard cloud model proposed in this study had strong visibility and discovered the key parts of risk indexes easily to solve the problems of ambiguity and quantitative identification in traditional roof risk evaluation methods. Therefore, this model was worth promoting, because it laid the foundation for the intelligent identification and early warning system of roof accident risk in a fully mechanized mining workface.

Study on Failure Characteristics and Control Technology of Roadway Surrounding Rock under Repeated Mining in Close-Distance Coal Seam

“…Therefore, multiple coal seams will appear in the same coal measure strata with a small distance between the adjacent multicoal seams. Different from the mining of a single coal seam, the mining activities of a close coal seam group can be influenced by each other's coal seams with the characteristics of mutual disturbance, stress concentration and severe damage to the mining roadway [1][2][3][4]. The roadway in the lower coal seam is significantly affected by the caving gangue generated after upper coal seam mining and the dynamic pressure due to the mining activities of the adjacent working face.…”

Study on Mechanical Characteristics and Failure Modes of Coal–Mudstone Combined Body with Prefabricated Crack

“…Naturally oriented fractures can significantly decrease the mechanical properties (e.g., strength, stiffness and elastic modulus) of the coal mass and rock. Therefore, the mechanical behaviors and failure laws of the coal and rock mass mainly depend on the distribution and development of cracks, which can cause serious engineering disasters, such as rib spalling, roof falling, rock burst and landslides [1][2][3][4][5]. In recent decades, gaining a comprehensive understanding of the mechanical behaviors of coal and rock masses with various faults (e.g., pre-existing cracks or holes) has been attractive [6][7][8][9][10][11][12][13].…”

Research on Dynamic Assessment Method of Earthquake Scene Rescue Performance Based on AHP and Cloud Model

“…Yan Zhou et al [22] used a combination of AHP and cloud model to achieve a more scientific assessment of green building operational performance, and the paper reflects the influence of subjective opinions of building managers in the assessment of green building operational performance, while retaining the uncertainty and ambiguity of each index in operational performance assessment. In addition, the AHP combined with the cloud model was also applied to the roof risk assessment [25] to achieve accurate identification of the accident risk of the roof of the comprehensive mining workings. The successful application of this method in the fields of ecological environment assessment [26], comprehensive risk assessment of computer host safety [27] and slope stability assessment [28,29] proves that the method can assess the fusion of qualitative and quantitative indexes in multiple dimensions and provide a scientific and objective description of the fuzziness and uncertainty of each index.…”