The nonlinear dynamical behaviors of artificial neural network (ANN) and their application to science and engineering were summarized. The mechanism of two kinds of dynamical processes, i.e. weight dynamics and activa...The nonlinear dynamical behaviors of artificial neural network (ANN) and their application to science and engineering were summarized. The mechanism of two kinds of dynamical processes, i.e. weight dynamics and activation dynamics in neural networks, and the stability of computing in structural analysis and design were stated briefly. It was successfully applied to nonlinear neural network to evaluate the stability of underground stope structure in a gold mine. With the application of BP network, it is proven that the neuro-com- puting is a practical and advanced tool for solving large-scale underground rock engineering problems.展开更多
In recent years,the mining depth of steeply inclined coal seams in the Urumqi mining area has gradually increased.Local deformation of mining coal-rock results in frequent rockbursts.This has become a critical issue t...In recent years,the mining depth of steeply inclined coal seams in the Urumqi mining area has gradually increased.Local deformation of mining coal-rock results in frequent rockbursts.This has become a critical issue that affects the safe mining of deep,steeply inclined coal seams.In this work,we adopt a perspective centered on localized deformation in coal-rock mining and systematically combine theoretical analyses and extensive data mining of voluminous microseismic data.We describe a mechanical model for the urgently inclined mining of both the sandwiched rock pillar and the roof,explaining the mechanical response behavior of key disaster-prone zones within the deep working face,affected by the dynamics of deep mining.By exploring the spatial correlation inherent in extensive microseismic data,we delineate the“time-space”response relationship that governs the dynamic failure of coal-rock during the progression of the sharply inclined working face.The results disclose that(1)the distinctive coal-rock occurrence structure characterized by a“sandwiched rock pillar-B6 roof”constitutes the origin of rockburst in the southern mining area of the Wudong Coal Mine,with both elements presenting different degrees of deformation localization with increasing mining depth.(2)As mining depth increases,the bending deformation and energy accumulation within the rock pillar and roof show nonlinear acceleration.The localized deformation of deep,steeply inclined coal-rock engenders the spatial superposition of squeezing and prying effects in both the strike and dip directions,increasing the energy distribution disparity and stress asymmetry of the“sandwiched rock pillar-B3+6 coal seam-B6 roof”configuration.This makes worse the propensity for frequent dynamic disasters in the working face.(3)The developed high-energy distortion zone“inner-outer”control technology effectively reduces high stress concentration and energy distortion in the surrounding rock.After implementation,the average apparent resistivity in the rock pillar and B6 roof substantially increased by 430%and 300%,respectively,thus guaranteeing the safe and efficient development of steeply inclined coal seams.展开更多
The engineering and geological characteristics of a steep slope consisting of coal gangue, rock and soil medium in Huating coal mine have been comprehensively investigated. Owing to humid weather, heavy rainfall, vege...The engineering and geological characteristics of a steep slope consisting of coal gangue, rock and soil medium in Huating coal mine have been comprehensively investigated. Owing to humid weather, heavy rainfall, vegetation and porous characteristics of the soil and rock mass, the steep slope will be destabilized and induce mud-rock flow or derive hazard easily. Firstly, based on the classical slope reinforcement theory, some regularity between the shear and displacement in the destabilized zone of the slope with or without root strength contribution is presented. Then, based on the experimental and statistical analysis of root strength, hydrological characteristics and stability status, etc., some possible biotechnical techniques for reinforcement of the steep slope have been suggested. These methods are important for quantitative analysis of destabilization of the slope and design of the biotechnical reinforcement.展开更多
Coal-rock interface identification technology was pivotal in automatically adjusting the shearer's cutting drum during coal mining.However,it also served as a technical bottleneck hindering the advancement of inte...Coal-rock interface identification technology was pivotal in automatically adjusting the shearer's cutting drum during coal mining.However,it also served as a technical bottleneck hindering the advancement of intelligent coal mining.This study aimed to address the poor accuracy of current coal-rock identification technology on comprehensive working faces,coupled with the limited availability of coal-rock datasets.The loss function of the SegFormer model was enhanced,the model's hyperparameters and learning rate were adjusted,and an automatic recognition method was proposed for coal-rock interfaces based on FL-SegFormer.Additionally,an experimental platform was constructed to simulate the dusty environment during coal-rock cutting by the shearer,enabling the collection of coal-rock test image datasets.The morphology-based algorithms were employed to expand the coal-rock image datasets through image rotation,color dithering,and Gaussian noise injection so as to augment the diversity and applicability of the datasets.As a result,a coal-rock image dataset comprising 8424 samples was generated.The findings demonstrated that the FL-SegFormer model achieved a Mean Intersection over Union(MIoU)and mean pixel accuracy(MPA)of 97.72%and 98.83%,respectively.The FLSegFormer model outperformed other models in terms of recognition accuracy,as evidenced by an MloU exceeding 95.70% of the original image.Furthermore,the FL-SegFormer model using original coal-rock images was validated from No.15205 working face of the Yulin test mine in northern Shaanxi.The calculated average error was only 1.77%,and the model operated at a rate of 46.96 frames per second,meeting the practical application and deployment requirements in underground settings.These results provided a theoretical foundation for achieving automatic and efficient mining with coal mining machines and the intelligent development of coal mines.展开更多
This paper proposes a detection method based on an improved Mask Region-based Convolutional Neural Network(Mask R-CNN)model for crack recognition in shallow-buried compressed air energy storage(CAES)cavern linings,ena...This paper proposes a detection method based on an improved Mask Region-based Convolutional Neural Network(Mask R-CNN)model for crack recognition in shallow-buried compressed air energy storage(CAES)cavern linings,enabling a comprehensive safety assessment of gas storage caverns.Flexible concrete samples are prepared to simulate the crack characteristics of the sealing lining,providing data support for the recognition module.The Convolutional Block Attention Module is introduced into the ResNet-50 backbone to adaptively adjust feature map weights and enhance feature extraction.Additionally,the mask segmentation loss function is optimized by combining Binary Cross-Entropy loss and Dice loss to improve crack region recognition.Experimental results show that the improved Mask R-CNN model achieves a mean average precision of 89.3%,a 17.2%improvement over the original model,and an intersection over union of 88.41%.Compared to RCNN,Faster R-CNN,YOLOv5,and SSD,the improved model shows superior performance with higher average precision(AP)50:95,AP50,and AP75 values in crack recognition tasks.The proposed method effectively identifies cracks in the flexible concrete sealing lining of shallow-buried CAES caverns,contributing significantly to the prevention of gas storage leaks and providing a valuable approach for the comprehensive safety assessment of CAES gas storage caverns.展开更多
Aiming at the technical problems of regional rock burst control and disaster reduction,the indoor comparative tests of three kinds of variables are designed,involving water content,borehole diameter and borehole filli...Aiming at the technical problems of regional rock burst control and disaster reduction,the indoor comparative tests of three kinds of variables are designed,involving water content,borehole diameter and borehole filling materials.This research analyzed the characteristics of the whole process of energy evolution of rock impacted by different regulation methods,and revealed the differences and applicable conditions of different regulation methods in reducing the impact mechanism.The results show that different control methods can effectively change the mechanical parameters of the target object.There are significant stage differences in the energy evolution of impact rocks.By constructing the energy conversion efficiency model,the study further elaborated on the water injection softening mechanism of"release first and then weaken",the drilling pressure relief mechanism of"guide first and then release"and the filling strengthening regulation mechanism of"release first and then absorb".The study of the optimal application conditions of different control measures provides an important basis for the regulation and disaster reduction of rock burst.展开更多
The authors regret that the Declaration of Competing Interest for this publication is incomplete.The appropriate Declaration of Competing Interest is as follows.
基金This work was financially supported by the Key Project for National Science of "9.5" (Reward Ⅱ for National Science and Technol
文摘The nonlinear dynamical behaviors of artificial neural network (ANN) and their application to science and engineering were summarized. The mechanism of two kinds of dynamical processes, i.e. weight dynamics and activation dynamics in neural networks, and the stability of computing in structural analysis and design were stated briefly. It was successfully applied to nonlinear neural network to evaluate the stability of underground stope structure in a gold mine. With the application of BP network, it is proven that the neuro-com- puting is a practical and advanced tool for solving large-scale underground rock engineering problems.
基金financially supported by the Major Program of the National Natural Science Foundation of China(No.52394191)the Outstanding Ph.D Dissertation Cultivating Program of Xi’an University of Science and Technology(No.PY22001)the National Foundation for studying abroad(No.[2022]87)。
文摘In recent years,the mining depth of steeply inclined coal seams in the Urumqi mining area has gradually increased.Local deformation of mining coal-rock results in frequent rockbursts.This has become a critical issue that affects the safe mining of deep,steeply inclined coal seams.In this work,we adopt a perspective centered on localized deformation in coal-rock mining and systematically combine theoretical analyses and extensive data mining of voluminous microseismic data.We describe a mechanical model for the urgently inclined mining of both the sandwiched rock pillar and the roof,explaining the mechanical response behavior of key disaster-prone zones within the deep working face,affected by the dynamics of deep mining.By exploring the spatial correlation inherent in extensive microseismic data,we delineate the“time-space”response relationship that governs the dynamic failure of coal-rock during the progression of the sharply inclined working face.The results disclose that(1)the distinctive coal-rock occurrence structure characterized by a“sandwiched rock pillar-B6 roof”constitutes the origin of rockburst in the southern mining area of the Wudong Coal Mine,with both elements presenting different degrees of deformation localization with increasing mining depth.(2)As mining depth increases,the bending deformation and energy accumulation within the rock pillar and roof show nonlinear acceleration.The localized deformation of deep,steeply inclined coal-rock engenders the spatial superposition of squeezing and prying effects in both the strike and dip directions,increasing the energy distribution disparity and stress asymmetry of the“sandwiched rock pillar-B3+6 coal seam-B6 roof”configuration.This makes worse the propensity for frequent dynamic disasters in the working face.(3)The developed high-energy distortion zone“inner-outer”control technology effectively reduces high stress concentration and energy distortion in the surrounding rock.After implementation,the average apparent resistivity in the rock pillar and B6 roof substantially increased by 430%and 300%,respectively,thus guaranteeing the safe and efficient development of steeply inclined coal seams.
基金This work was financially supported by the National Natural Science Foundation of China (No. 10402033) and the Key Lab. Foun-dation of the Ministry of Education of China (No.04JS19).
文摘The engineering and geological characteristics of a steep slope consisting of coal gangue, rock and soil medium in Huating coal mine have been comprehensively investigated. Owing to humid weather, heavy rainfall, vegetation and porous characteristics of the soil and rock mass, the steep slope will be destabilized and induce mud-rock flow or derive hazard easily. Firstly, based on the classical slope reinforcement theory, some regularity between the shear and displacement in the destabilized zone of the slope with or without root strength contribution is presented. Then, based on the experimental and statistical analysis of root strength, hydrological characteristics and stability status, etc., some possible biotechnical techniques for reinforcement of the steep slope have been suggested. These methods are important for quantitative analysis of destabilization of the slope and design of the biotechnical reinforcement.
基金funded by the National Natural Science Foundation of China(52004201,52274143,52204153)China Postdoctoral Science Foundation(2021T140551).
文摘Coal-rock interface identification technology was pivotal in automatically adjusting the shearer's cutting drum during coal mining.However,it also served as a technical bottleneck hindering the advancement of intelligent coal mining.This study aimed to address the poor accuracy of current coal-rock identification technology on comprehensive working faces,coupled with the limited availability of coal-rock datasets.The loss function of the SegFormer model was enhanced,the model's hyperparameters and learning rate were adjusted,and an automatic recognition method was proposed for coal-rock interfaces based on FL-SegFormer.Additionally,an experimental platform was constructed to simulate the dusty environment during coal-rock cutting by the shearer,enabling the collection of coal-rock test image datasets.The morphology-based algorithms were employed to expand the coal-rock image datasets through image rotation,color dithering,and Gaussian noise injection so as to augment the diversity and applicability of the datasets.As a result,a coal-rock image dataset comprising 8424 samples was generated.The findings demonstrated that the FL-SegFormer model achieved a Mean Intersection over Union(MIoU)and mean pixel accuracy(MPA)of 97.72%and 98.83%,respectively.The FLSegFormer model outperformed other models in terms of recognition accuracy,as evidenced by an MloU exceeding 95.70% of the original image.Furthermore,the FL-SegFormer model using original coal-rock images was validated from No.15205 working face of the Yulin test mine in northern Shaanxi.The calculated average error was only 1.77%,and the model operated at a rate of 46.96 frames per second,meeting the practical application and deployment requirements in underground settings.These results provided a theoretical foundation for achieving automatic and efficient mining with coal mining machines and the intelligent development of coal mines.
基金the financial supports of the National Natural Science Foundation of China Youth Science Foundation Project(52204152,52204111,52204153)the Postdoctoral Innovation Talent Support Program(BX2020275)the Postdoctoral Science Foundation(2020M683521).
文摘This paper proposes a detection method based on an improved Mask Region-based Convolutional Neural Network(Mask R-CNN)model for crack recognition in shallow-buried compressed air energy storage(CAES)cavern linings,enabling a comprehensive safety assessment of gas storage caverns.Flexible concrete samples are prepared to simulate the crack characteristics of the sealing lining,providing data support for the recognition module.The Convolutional Block Attention Module is introduced into the ResNet-50 backbone to adaptively adjust feature map weights and enhance feature extraction.Additionally,the mask segmentation loss function is optimized by combining Binary Cross-Entropy loss and Dice loss to improve crack region recognition.Experimental results show that the improved Mask R-CNN model achieves a mean average precision of 89.3%,a 17.2%improvement over the original model,and an intersection over union of 88.41%.Compared to RCNN,Faster R-CNN,YOLOv5,and SSD,the improved model shows superior performance with higher average precision(AP)50:95,AP50,and AP75 values in crack recognition tasks.The proposed method effectively identifies cracks in the flexible concrete sealing lining of shallow-buried CAES caverns,contributing significantly to the prevention of gas storage leaks and providing a valuable approach for the comprehensive safety assessment of CAES gas storage caverns.
基金funded by Special scientific research project of Shaanxi Provincial Department of Education(No.24JK0545)Shaanxi Province postdoctoral research project(No.2023BSHEDZZ313).
文摘Aiming at the technical problems of regional rock burst control and disaster reduction,the indoor comparative tests of three kinds of variables are designed,involving water content,borehole diameter and borehole filling materials.This research analyzed the characteristics of the whole process of energy evolution of rock impacted by different regulation methods,and revealed the differences and applicable conditions of different regulation methods in reducing the impact mechanism.The results show that different control methods can effectively change the mechanical parameters of the target object.There are significant stage differences in the energy evolution of impact rocks.By constructing the energy conversion efficiency model,the study further elaborated on the water injection softening mechanism of"release first and then weaken",the drilling pressure relief mechanism of"guide first and then release"and the filling strengthening regulation mechanism of"release first and then absorb".The study of the optimal application conditions of different control measures provides an important basis for the regulation and disaster reduction of rock burst.
文摘The authors regret that the Declaration of Competing Interest for this publication is incomplete.The appropriate Declaration of Competing Interest is as follows.
