Study on the evolutionary mechanisms driving deformation damage of dry tailing stack earth–rock dam under short-term extreme rainfall conditions

Xie, Chengyu; Chen, Ziwei; Xiong, Guanpeng; Yang, Baolin; Shen, Jinbo

doi:10.1007/s11069-023-06190-9

Cited by 4 publications

(4 citation statements)

References 50 publications

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“…Currently, several prediction methods exist, including differential scanning calorimetry (DSC), thermogravimetric analysis, Russian U index, differential thermal analysis, cross-point temperature, X-Ray Diffractometer, the Olpinski index method, and the Adiabatic calorimetric method [58]. Many researchers have developed prediction models or techniques, such as mathematical models, TNO models, cloud models, adiabatic calorimeters, remote sensing, and GIS technologies, among others [56,59,60]. The detection and trend analysis of several special gas products released during coal oxidation is the most basic prediction technology of mine fires in practice.…”

Section: Abstract Clustering Analysismentioning

confidence: 99%

A Review on Mine Fire Prevention Technology and Theory Based on Bibliometric Analysis

Shi,

Liu,

2023

Sustainability

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Of all mine disasters, fires are very threatening to mine safety and often lead to the most serious consequences. Research on mine fire prevention technology and theory has experienced significant growth and is attracting escalating academic interest and attention. However, dedicated literature reviews on this topic are scarce. For the purpose of uncovering the research characteristics and trends on mine fire prevention technology and theory, this paper employs bibliometric analysis using the Web of Science Core Collection database. This study presents a detailed analysis of relevant articles published between 2010 and 2022. An assessment of the influences of journals, countries, institutions, and authors was conducted through a citation analysis. Furthermore, this paper describes co-authorship networks among different countries, institutions, and authors. Lastly, a review of the mine fire prevention techniques and theories researched during this period was carried out through a keyword clustering analysis. Four main research topics in mine fire prevention research were identified: “mine fire control technology”, “mine fire occurrence mechanism”, “mine fire prediction technology”, and “mine fire monitoring technology”. Additionally, the theory study of spontaneous combustion and its underlying mechanisms may represent a potential focus for future research. These findings contribute to providing a solid foundation for future research endeavors in this field of fire prevention.

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Section: Abstract Clustering Analysismentioning

confidence: 99%

A Review on Mine Fire Prevention Technology and Theory Based on Bibliometric Analysis

Shi,

Liu,

2023

Sustainability

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“…Mineral resources serve as a crucial material foundation for the development of modern society, and their exploitation holds significant importance in economic development [ 1 , [2] , [3] ]. As shallow resources gradually deplete, resource development is transitioning towards deeper levels, making underground mining the primary method of resource extraction.…”

Section: Introductionmentioning

confidence: 99%

Bibliometric analysis and review of mine ventilation literature published between 2010 and 2023

Xue,

Wang,

Xiao

2024

Heliyon

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“…They exhibit characteristics such as abrupt occurrence, complex mechanisms, and numerous influencing factors. Currently, research on mining-induced dynamic disasters has become a key and challenging aspect of underground mining safety efforts [3,4].…”

Section: Introductionmentioning

confidence: 99%

Classification of Microseismic Signals Using Machine Learning

Chen,

Cui,

et al. 2024

Processes

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The classification of microseismic signals represents a fundamental preprocessing step in microseismic monitoring and early warning. A microseismic signal source rock classification method based on a convolutional neural network is proposed. First, the characteristic parameters of the microseismic signals are extracted, and a convolutional neural network is constructed for the analysis of these parameters; then, the mapping relationship model between the characteristic parameters of the microseismic signals and the rock class is established. The feasibility of the proposed method in differentiating acoustic emission signals under different load conditions is verified by using acoustic emission data from laboratory uniaxial compression tests, Brazilian splitting tests, and shear tests. In the three distinct laboratory experiments, the proposed method achieved a source rock classification accuracy of greater than 90% for acoustic emission signals. The proposed and verified method provides a new basis for the preprocessing of microseismic signals.

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Study on the evolutionary mechanisms driving deformation damage of dry tailing stack earth–rock dam under short-term extreme rainfall conditions

Cited by 4 publications

References 50 publications

A Review on Mine Fire Prevention Technology and Theory Based on Bibliometric Analysis

A Review on Mine Fire Prevention Technology and Theory Based on Bibliometric Analysis

Bibliometric analysis and review of mine ventilation literature published between 2010 and 2023

Classification of Microseismic Signals Using Machine Learning

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