Abstract
Coal bump prediction is one of the key problems in deep coal mining engineering. To predict coal bump disaster accurately and reliably, we propose a depth neural network (DNN) prediction model based on the dropout method and improved Adam algorithm. The coal bump accident examples were counted in order to analyze the influencing factors, characteristics, and causes of this type of accidents. Finally, four indexes of maximum tangential stress of surrounding rock, uniaxial compressive strength of rock, uniaxial tensile strength of rock, and elastic energy of rock are selected to form the prediction index system of coal bump. Based on the research results of rock burst, 305 groups of rock burst engineering case data are collected as the sample data of coal bump prediction, and then, the prediction model based on a dropout and improved Adam-based deep neural network (DA-DNN) is established by using deep learning technology. The DA-DNN model avoids the problem of determining the index weight, is completely data-driven, reduces the influence of human factors, and can realize the learning of complex and subtle deep relationships in incomplete, imprecise, and noisy limited data sets. A coal mine in Shanxi Province is used to predict coal bump with the improved depth learning method. The prediction results verify the effectiveness and correctness of the DA-DNN coal bump prediction model. Finally, it is proved that the model can effectively provide a scientific basis for coal bump prediction of similar projects.
Highlights
Coal bump is a dynamic phenomenon characterized by sudden, rapid, and violent destruction of coal around roadway or mining face due to the instantaneous release of elastic deformation energy [1]
Adoko et al [8] and Wang et al [9] conducted in-depth research; prediction models based on fuzzy mathematics theory are established, respectively, but the determination of index weight in this method depends on subjective factors
Due to the complexity of coal bump mechanism, the diversity of influencing factors, and the defects of various methods, there are still the following deficiencies in practical engineering application: (1) the main ideas of most methods belong to comprehensive evaluation, and the core problem is the determination of the weight of each index
Summary
Coal bump is a dynamic phenomenon characterized by sudden, rapid, and violent destruction of coal (rock) around roadway or mining face due to the instantaneous release of elastic deformation energy [1]. Adoko et al [8] and Wang et al [9] conducted in-depth research; prediction models based on fuzzy mathematics theory are established, respectively, but the determination of index weight in this method depends on subjective factors. Luo and Cao [15] used principal component analysis to calculate the weight matrix and established a weighted distance discrimination model This method is greatly affected by the representativeness and accuracy of the original data. Due to the complexity of coal bump mechanism, the diversity of influencing factors, and the defects of various methods, there are still the following deficiencies in practical engineering application: (1) the main ideas of most methods belong to comprehensive evaluation, and the core problem is the determination of the weight of each index. The research on the application of depth neural network in coal bump prediction is of great significance to expand the coal bump prediction system and improve the ability of prediction
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