Experimental Investigation on the Tensile and Fracture Properties of Burst-Prone Coal under Quasistatic Condition

Gong, Shuang; Wang, Zhen; Zhou, Lei; Wen, Wang

doi:10.1155/2021/5593376

Cited by 2 publications

(2 citation statements)

References 40 publications

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“…Prediction is the core of coal bump prevention and control. Accurate and reliable prediction of high-intensity coal bump disaster is to effectively avoid and control it [2]. The prediction of coal bump has become a research hotspot in the fields of large-scale underground geotechnical engineering and deep coal mineral resources mining.…”

Section: Introductionmentioning

confidence: 99%

Prediction and Evaluation of Coal Mine Coal Bump Based on Improved Deep Neural Network

2021

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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.

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Section: Introductionmentioning

confidence: 99%

Prediction and Evaluation of Coal Mine Coal Bump Based on Improved Deep Neural Network

2021

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“…Under the action of earthquake or blasting and other dynamic load disturbances, the instability of coal pillar dam structure may be caused [6]. At present, the physical and mechanical properties of coal have been widely studied, such as compressive strength [7,8], tensile strength [9][10][11], fracture characteristics [12,13], elastic modulus [14], shear strength [15], and triaxial strength [16]. However, the change of strength characteristics and porosity of coal under the influence of acid-base environment has not been reported.…”

Section: Introductionmentioning

confidence: 99%

Effect of Acid and Alkaline Environment on Dynamic Strength and Porosity Characteristics of Bursting Liability Coal

Fan²,

Liu

et al. 2021

Shock and Vibration

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In order to investigate the influence of acid and alkaline environment on dynamic strength and porosity characteristics of bursting liability coal, scanning electron microscopy (SEM) and X-ray diffraction (XRD) analysis were used to compare the microstructures of coal with different bursting liabilities. A split Hopkinson bar (SHPB) was used to test the dynamic compressive strength and tensile strength of coal samples with different bursting liabilities. The results show that the surface micromorphology and structure characteristics of coal samples with different bursting liabilities are representatives, which can be used as an auxiliary basis to determine the bursting liability of coal seam. The microstructure of coal with strong bursting liability is characterized by mylonitic, fragmentary, and brecciated structure, and the microstructure is diverse and complex. However, the microstructure of no bursting liability coal is single and uniform. Coal with strong bursting liability shows tensile, compressive, and shear cracks produced by tectonic action, and the distribution of cracks is complicated. The development of fissures is greatly affected by the degree of coal metamorphism, organic components, minerals, and other factors. Under acidic and alkaline environments, the decrease amplitude of tensile strength of coal is obviously larger than that in neutral solution, which indicates that under the action of acid-based solution soaking, the easily soluble minerals in coal react with hydrogen ions and hydroxyl ions in solution obviously. Porosity increment in acidic environment is much larger than that in alkaline and neutral environments. The strong bursting liability coal is more sensitive to acidic environment, while the no bursting liability coal is more sensitive to alkaline environment.

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Experimental Investigation on the Tensile and Fracture Properties of Burst-Prone Coal under Quasistatic Condition

Cited by 2 publications

References 40 publications

Prediction and Evaluation of Coal Mine Coal Bump Based on Improved Deep Neural Network

Prediction and Evaluation of Coal Mine Coal Bump Based on Improved Deep Neural Network

Effect of Acid and Alkaline Environment on Dynamic Strength and Porosity Characteristics of Bursting Liability Coal

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