Soft coal is characterized by low strength and weak bonds, which play a key role in the occurrence and development of dynamic disasters. A more thorough understanding of the failure mechanics and infrared radiation characteristics of soft coal at various moisture contents is needed. In this study, infrared radiation experiments were conducted for soft coal at various moisture contents. The results indicate that moisture content affects compressive strength and elastic modulus of soft coal with compressive strength and elastic modulus being highest at moderate. Moisture has a substantial influence on the infrared radiation of compressed coal samples. Soft coals with high moisture contents show a smaller fluctuation of the average infrared radiation temperature curve and a stronger direct relationship of temperature and stress in the linear elastic stage. The coal samples with various moisture contents show a warming trend during loading. The increased amplitude of infrared radiation temperature per unit stress shows a linear relationship with moisture content. A large number of high temperature red spots appear along the diagonal line of coal samples with 0% moisture content before fracture, while changes in the infrared thermal image of the coal sample with 4% moisture content were negligible during loading.
To further improve the accuracy of recurrent neural network in predicting the gas concentration in the upper corner of the mine tunnel, this paper proposes a method to construct a gas concentration prediction model based on multiple sequence long and short memory network, considering the spatial correlation between the gas concentration in the return airway and upper corner. The reliability of the model construction is improved by using the white noise test and smoothness test to verify the interpretability of the data in this paper and constructing supervised learning type data for gas concentration prediction model training and testing by means of data set division and data windowing. Through experimental comparison, grid search, and time series decomposition, the model algorithm, training parameters, and experimental results were combined to make an in-depth analysis of the influence of each parameter on the model training and the prediction. A training model of the spatially fused gas concentration prediction model with a network layer of 1 and a number of neurons of 32 as the model structure, Adam as the optimization algorithm, and a learning rate of 0.001 and a batch size of 32 as the training parameters was finally determined. The gas concentration prediction model trained in this paper performed well in the test set with a mean square error (MSE) of 0.0013, and its superiority was verified by comparing it with other models to provide some experience and basis for subsequent studies on gas concentration prediction in the upper corner.
As coal mine production enters the deep mining stage, the impact of coal and rock dynamic hazards is becoming more and more significant. And the coal and rock containing initial damage such as fractures are more susceptible to destabilization damage by disturbance. So, this paper takes coal containing macro-crack with different inclination angles as the research object and uses the RMT-150B rock mechanics system to carry out uniaxial loading rupture tests on the specimens. On this basis, the changes in infrared radiation on the surface are observed using an infrared thermal imaging camera, and it is analyzed and studied according to the stress distribution and energy change of the specimens. The results show that the strain ratio at crack closure after bearing the coal gradually increases with the increase in the macro-crack inclination. When the inclination angle is 0° < α < 90°, there are obvious low-temperature bands on the upper and lower sides after macro-crack closure. The variance of the infrared thermal image of the specimen can reflect its infrared radiation information more effectively and has a good correspondence with the stress–strain curve. With the increase in the specimen macro-crack inclination angle, the linear change of VIRT is more obvious, the rate of change gradually increases, and the inclination angle is the maximum at 90°. The accumulated elastic strain energy U e is the main source of energy for the sudden change in infrared radiation generated during the bursting process that occurs when the specimen is damaged, and U e is linearly and positively correlated with the change in infrared radiation in front of the specimen peak. These will provide some experimental basis and theoretical guidance for the use of infrared radiation precursor characteristics to warn the damaged coal–rock dynamic disaster.
Metrics & MoreArticle RecommendationsA fund is added to the Acknowledgments: the open fund project of state key laboratory of mining response and disaster prevention and control in deep coal mines, fund number: SKLMRDPC20KF06.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.