When the expressway crosses the goafs inevitably,the design is generally to build the road on coal pillars as much as possible.However,the existing coal pillars are often unable to meet relevant requirements of highwa...When the expressway crosses the goafs inevitably,the design is generally to build the road on coal pillars as much as possible.However,the existing coal pillars are often unable to meet relevant requirements of highway construction.Combining three-dimensional physical model tests,numerical simulations and field monitoring,with the Urumqi East Second Ring Road passing through acute inclined goafs as a background,the deformation and failure mechanism of the overlying rock and coal pillars in acute inclined goafs under expressway load were studied.And in accordance with construction requirements of subgrade,comprehensive consideration of the deformation and instability mechanism of acute inclined goafs,the treatment measures and suggestions for this type of geological disasters were put forward.The research results confirmed the rationality of coal pillars in acute inclined goafs under the expressway through grouting.According to the ratio of diff erent overlying rock thickness to coal pillar height,the change trend and value of the required grouting range were summarized,which can provide reference for similar projects.展开更多
The influence of different solution and aging conditions on the microstructure,impact toughness,and crack initiation and propagation mechanisms of the novel α+β titanium alloy Ti6422 was systematically investigated....The influence of different solution and aging conditions on the microstructure,impact toughness,and crack initiation and propagation mechanisms of the novel α+β titanium alloy Ti6422 was systematically investigated.By adjusting the furnace cooling time after solution treatment and the aging temperature,Ti6422 alloy samples were developed with a multi-level lamellar microstructure,in-cluding microscaleαcolonies and α_(p) lamellae,as well as nanoscale α_(s) phases.Extending the furnace cooling time after solution treatment at 920℃ for 1 h from 240 to 540 min,followed by aging at 600℃ for 6 h,increased the α_(p) lamella content,reduced the α_(s) phase content,expanded theαcolonies and α_(p) lamellae size,and improved the impact toughness from 22.7 to 53.8 J/cm^(2).Additionally,under the same solution treatment,raising the aging temperature from 500 to 700℃ resulted in a decrease in the α_(s) phase content and a growth in the thickness of the α_(p) lamella and α_(s) phase.The impact toughness increased significantly with these changes.Samples with high α_(p) lamellae content or large α_(s) phase size exhibited high crack initiation and propagation energies.Impact deformation caused severe kinking of the α_(p) lamellae in crack initiation and propagation areas,leading to a uniform and high-density kernel average misorientation(KAM)distribu-tion,enhancing plastic deformation coordination and uniformity.Moreover,the multidirectional arrangement of coarserαcolonies and α_(p) lamellae continuously deflect the crack propagation direction,inhibiting crack propagation.展开更多
In exploiting shallow coal resources in western China, conservation of water resources is often subjugated to considerations of safety and production in coal mines. In order to maintain a sustainable development in th...In exploiting shallow coal resources in western China, conservation of water resources is often subjugated to considerations of safety and production in coal mines. In order to maintain a sustainable development in the Shenfu-Dongsheng coalfield, we propose a technology of constructing groundwater reservoirs in goafs in shallow coalfields to protect fragile ecological environments. Given the premise of safe production, we selected an appropriate goaf as the site for constructing a groundwater reservoir and used a mine water recharge technique in combination with other related techniques for effective water conservation. Then filtering and purification techniques were used to purify the mine water given the physical and chemical properties of mine water and its filling material, ,thereby greatly reducing suspended matter, calcium and other harmful ions in the water. With the potential of widely application, the research result has been successfully applied in the Daliuta coal mine, to great economic and ecological effect. Therefore, this achievement provides a new way for mine water conservation in shallow coal resources in western China.展开更多
In order to precisely predict the hazard degree of goaf(HDG), the RS-TOPSIS model was built based on the results of expert investigation. To evaluate the HDG in the underground mine, five structure size factors, i.e. ...In order to precisely predict the hazard degree of goaf(HDG), the RS-TOPSIS model was built based on the results of expert investigation. To evaluate the HDG in the underground mine, five structure size factors, i.e. goaf span, exposed area, goaf height, goaf depth, and pillar width, were selected as the evaluation indexes. And based on rough dependability in rough set(RS)theory, the weights of evaluation indexes were identified by calculating rough dependability between evaluation indexes and evaluation results. Fourty goafs in some mines of western China, whose indexes parameters were measured by cavity monitoring system(CMS), were taken as evaluation objects. In addition, the characteristic parameters of five grades' typical goafs were built according to the interval limits value of single index evaluation. Then, using the technique for order preference by similarity to ideal solution(TOPSIS), five-category classification of HDG was realized based on closeness degree, and the HDG was also identified.Results show that the five-category identification of mine goafs could be realized by RS-TOPSIS method, based on the structure-scale-effect. The classification results are consistent with those of numerical simulation based on stress and displacement,while the coincidence rate is up to 92.5%. Furthermore, the results are more conservative to safety evaluation than numerical simulation, thus demonstrating that the proposed method is more easier, reasonable and more definite for HDG identification.展开更多
Targeting at the coal seam with useful value discarded above goafs,attempted to explore the feasibility of'mining technique in the condition of floor failure' from theoretical point of view,and predicted.It in...Targeting at the coal seam with useful value discarded above goafs,attempted to explore the feasibility of'mining technique in the condition of floor failure' from theoretical point of view,and predicted.It indicated that mining technique in the condition of floor failure used above Longwall Goafs in Baijiazhuang Mining is totally feasible.At law,the deformation of the floor in the mining technique by means of probability-integral method.And it is discov- ered that deformed basin can emerge in the footwall of No.6 coal seam and its maximum subsidence was possibly 1 633 mm or so and its maximum positive curvature is 61.74/10^(-3). At last,it therefore suggests appropriate ground pressure control measures as strengthening observation of ground pressure and adopting false slope for exploitation and strengthening support for reasonable push and slide based on the adverse ground pressure behaviors possibly occurring in the mining technique.This serves to gather data and lay sturdy founda- tion for further probe into the mining technique,and offers theoretical and technical grounds for concrete implementation of the mining technique.展开更多
As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods ge...As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods generally have problems such as insufficient 3D scene description capability and low dynamic update efficiency,which are difficult to meet the demand of real-time accurate management.For this reason,this paper proposes a vehicle twin modeling method for road tunnels.This approach starts from the actual management needs,and supports multi-level dynamic modeling from vehicle type,size to color by constructing a vehicle model library that can be flexibly invoked;at the same time,semantic constraint rules with geometric layout,behavioral attributes,and spatial relationships are designed to ensure that the virtual model matches with the real model with a high degree of similarity;ultimately,the prototype system is constructed and the case region is selected for the case study,and the dynamic vehicle status in the tunnel is realized by integrating real-time monitoring data with semantic constraints for precise virtual-real mapping.Finally,the prototype system is constructed and case experiments are conducted in selected case areas,which are combined with real-time monitoring data to realize dynamic updating and three-dimensional visualization of vehicle states in tunnels.The experiments show that the proposed method can run smoothly with an average rendering efficiency of 17.70 ms while guaranteeing the modeling accuracy(composite similarity of 0.867),which significantly improves the real-time and intuitive tunnel management.The research results provide reliable technical support for intelligent operation and emergency response of road tunnels,and offer new ideas for digital twin modeling of complex scenes.展开更多
The umbilical,a key component in offshore energy extraction,plays a vital role in ensuring the stable operation of the entire production system.The extensive variety of cross-sectional components creates highly comple...The umbilical,a key component in offshore energy extraction,plays a vital role in ensuring the stable operation of the entire production system.The extensive variety of cross-sectional components creates highly complex layout combinations.Furthermore,due to constraints in component quantity and geometry within the cross-sectional layout,filler bodies must be incorporated to maintain cross-section performance.Conventional design approaches based on manual experience suffer from inefficiency,high variability,and difficulties in quantification.This paper presents a multi-level automatic filling optimization design method for umbilical cross-sectional layouts to address these limitations.Initially,the research establishes a multi-objective optimization model that considers compactness,balance,and wear resistance of the cross-section,employing an enhanced genetic algorithm to achieve a near-optimal layout.Subsequently,the study implements an image processing-based vacancy detection technique to accurately identify cross-sectional gaps.To manage the variability and diversity of these vacant regions,the research introduces a multi-level filling method that strategically selects and places filler bodies of varying dimensions,overcoming the constraints of uniform-size fillers.Additionally,the method incorporates a hierarchical strategy that subdivides the complex cross-section into multiple layers,enabling layer-by-layer optimization and filling.This approach reduces manufac-turing equipment requirements while ensuring practical production process feasibility.The methodology is validated through a specific umbilical case study.The results demonstrate improvements in compactness,balance,and wear resistance compared with the initial cross-section,offering novel insights and valuable references for filler design in umbilical cross-sections.展开更多
The stability and fracture behavior of a goaf roof beneath an open-pit bench are critical concerns,especially under impact loading.However,the effect of the thickness-to-span ratio on dynamic failure modes remains lar...The stability and fracture behavior of a goaf roof beneath an open-pit bench are critical concerns,especially under impact loading.However,the effect of the thickness-to-span ratio on dynamic failure modes remains largely unexplored,as existing research focuses mainly on static stability.Energy dissipation and instability evolution under impact loading require further study.To address this gap,this study conducts drop-weight impact experiments on specimens with circular perforations,complemented by numerical simulations.By integrating dimensional analysis,cusp catastrophe theory,and strength reduction techniques,the dynamic instability mechanism of goaf roofs with varying thickness-to-span ratios is revealed.Results show that the thickness-to-span ratio significantly influences energy accumulation and dissipation during roof failure.A higher ratio increases both the magnitude and rate of energy dissipation,particularly during crack initiation and stable propagation,while its impact diminishes in the final failure stage.Optimizing the thickness-to-span ratio within a critical range enhances structural stability,improving the safety factor by up to 83%.However,beyond a certain threshold,additional thickness yields diminishing benefits.This study provides new insights into the energy-based instability mechanism of goaf roofs under impact loads,establishing a theoretical foundation for early warning systems and optimized safety design.展开更多
China has a long history of coal mining,among which open-pit coal mines have a large number of small coal mine goafs underground.The distribution,shape,structure and other characteristics of goafs are isolated and dis...China has a long history of coal mining,among which open-pit coal mines have a large number of small coal mine goafs underground.The distribution,shape,structure and other characteristics of goafs are isolated and discontinuous,and there is no definite geological law to follow,which seriously threatens the safety of coal mine production and personnel life.Conventional ground geophysical methods have low accuracy in detecting goaf areas affected by mechanical interference from open-pit mines,especially for waterless goaf areas,which cannot be detected by existing methods.This article proposes the use of high-frequency electromagnetic waves for goaf detection.The feasibility of using drilling radar to detect goaf was theoretically analyzed,and a goaf detection model was established.The response characteristics of different fillers in the goaf under different frequencies of high-frequency electromagnetic waves were simulated and analyzed.In a certain open-pit mine in Inner Mongolia,100MHz high-frequency electromagnetic waves were used to detect the goaf through directional drilling on the ground.After detection,excavation verification was carried out,and the location of one goaf detected was verified.The results of engineering practice show that the application of high-frequency electromagnetic waves in goaf detection expands the detection radius of boreholes,has the advantages of high efficiency and accuracy,and has important theoretical and practical significance.展开更多
China,as the world’s largest coal producer and consumer,faces increasingly severe challenges from coal mine goaf areas formed through decades of intensive mining.These underground voids,resulting from exhausted resou...China,as the world’s largest coal producer and consumer,faces increasingly severe challenges from coal mine goaf areas formed through decades of intensive mining.These underground voids,resulting from exhausted resources or technical limitations,not only cause environmental issues like land subsidence and groundwater contamination but also pose critical safety risks for ongoing mining operations,including water inrushes,gas outbursts,and roof collapses.Conventional geophysical methods such as seismic surveys and electromagnetic detection demonstrate limited effectiveness in complex geological conditions due to susceptibility to electrical heterogeneity,electromagnetic interference,and interpretation ambiguities.This study presents an innovative integrated approach combining the Audio-Frequency Electrical Transillumination(AFET)method with multi-parameter borehole logging to establish a three-dimensional detection system.The AFET technique employs 0.1–10 kHz electromagnetic waves to identify electrical anomalies associated with goafs,enabling extensive horizontal scanning.This is complemented by vertical high-resolution profiling through borehole measurements of resistivity,spontaneous potential,and acoustic velocity.Field applications in Shanxi Province’s typical coal mines achieved breakthrough results:Using a grid-drilling pattern(15 m spacing,300 m depth),the method successfully detected three concealed goafs missed by conventional approaches,with spatial positioning errors under 0.5 m.Notably,it accurately identified two un-collapsed water-filled cavities.This surface-borehole synergistic approach overcomes single-method limitations,enhancing goaf detection accuracy to over 92%.The technique provides reliable technical support for safe mining practices and represents significant progress in precise detection of hidden geological hazards in Chinese coal mines,offering valuable insights for global mining geophysics.展开更多
Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,ther...Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,there is limited research on the spatiotemporal characteristics of landslide deformation.This paper proposes a novel Multi-Relation Spatiotemporal Graph Residual Network with Multi-Level Feature Attention(MFA-MRSTGRN)that effectively improves the prediction performance of landslide displacement through spatiotemporal fusion.This model integrates internal seepage factors as data feature enhancements with external triggering factors,allowing for accurate capture of the complex spatiotemporal characteristics of landslide displacement and the construction of a multi-source heterogeneous dataset.The MFA-MRSTGRN model incorporates dynamic graph theory and four key modules:multilevel feature attention,temporal-residual decomposition,spatial multi-relational graph convolution,and spatiotemporal fusion prediction.This comprehensive approach enables the efficient analyses of multi-source heterogeneous datasets,facilitating adaptive exploration of the evolving multi-relational,multi-dimensional spatiotemporal complexities in landslides.When applying this model to predict the displacement of the Liangshuijing landslide,we demonstrate that the MFA-MRSTGRN model surpasses traditional models,such as random forest(RF),long short-term memory(LSTM),and spatial temporal graph convolutional networks(ST-GCN)models in terms of various evaluation metrics including mean absolute error(MAE=1.27 mm),root mean square error(RMSE=1.49 mm),mean absolute percentage error(MAPE=0.026),and R-squared(R^(2)=0.88).Furthermore,feature ablation experiments indicate that incorporating internal seepage factors improves the predictive performance of landslide displacement models.This research provides an advanced and reliable method for landslide displacement prediction.展开更多
The potential for CO_(2)sequestration in the goaf of abandoned coal mines is significant due to the extensive fracture spaces and substantial residual coal present.Firstly,the adsorption characteristics of residual co...The potential for CO_(2)sequestration in the goaf of abandoned coal mines is significant due to the extensive fracture spaces and substantial residual coal present.Firstly,the adsorption characteristics of residual coal in goaf on CO_(2)were studied by the isothermal adsorption test of CO_(2).Then,to accurately calculate the amount of adsorbed CO_(2)within the residual coal in the goaf,the bidisperse diffusion numerical model considering only Fick diffusion was modified in combination with the diffusion mechanisms.The simulation results showed that the modified model can well describe the diffusion behavior of CO_(2)in the residual coal matrix.Finally,the numerical simulation of CO_(2)sequestration in the goaf of abandoned coal mines was carried out,and the influence of different injection well deployment positions and various thicknesses of residual coal on the migration law and storage effect of CO_(2)in goaf was analyzed.The results showed that CO_(2)preferentially flowed into the caving zone with higher permeability.The distribution of CO_(2)streamlines in the goaf was the most dense in the caving zone and the streamlines in the fracture zone were gradually sparse from bottom to top.When the injection well was deployed at the interface of the two zones,the CO_(2)had the best seepage path.The total storage capacity within90 days was 7.702754×10^(6)kg,of which the free state storage capacity in the fracture of the goaf and the adsorbed state storage capacity in the residual coal were 6.611451×10^(6)and 1.091303×10^(6)kg,respectively.When the injection well was deployed in the middle of the residual coal seam in the goaf and the middle of the fracture zone,the total storage capacity at the same time was 7.613508×10^(6)and 6.021495×10^(6)kg,respectively.The coal with different thicknesses remaining at the bottom of the goaf significantly affected the adsorbed state storage,but had little effect on the free state storage.When the thickness of the residual coal seam was 0.20,0.35,and 0.50 m,the adsorbed state storage capacity within 130 days was 4.37623×10^(5),7.65791×10^(5),and 1.093406×10^(6)kg,respectively.展开更多
Deep learning networks are increasingly exploited in the field of neuronal soma segmentation.However,annotating dataset is also an expensive and time-consuming task.Unsupervised domain adaptation is an effective metho...Deep learning networks are increasingly exploited in the field of neuronal soma segmentation.However,annotating dataset is also an expensive and time-consuming task.Unsupervised domain adaptation is an effective method to mitigate the problem,which is able to learn an adaptive segmentation model by transferring knowledge from a rich-labeled source domain.In this paper,we propose a multi-level distribution alignment-based unsupervised domain adaptation network(MDA-Net)for segmentation of 3D neuronal soma images.Distribution alignment is performed in both feature space and output space.In the feature space,features from different scales are adaptively fused to enhance the feature extraction capability for small target somata and con-strained to be domain invariant by adversarial adaptation strategy.In the output space,local discrepancy maps that can reveal the spatial structures of somata are constructed on the predicted segmentation results.Then thedistribution alignment is performed on the local discrepancies maps across domains to obtain a superior discrepancy map in the target domain,achieving refined segmentation performance of neuronal somata.Additionally,after a period of distribution align-ment procedure,a portion of target samples with high confident pseudo-labels are selected as training data,which assist in learning a more adaptive segmentation network.We verified the superiority of the proposed algorithm by comparing several domain adaptation networks on two 3D mouse brain neuronal somata datasets and one macaque brain neuronal soma dataset.展开更多
As we look ahead to future lunar exploration missions, such as crewed lunar exploration and establishing lunar scientific research stations, the lunar rovers will need to cover vast distances. These distances could ra...As we look ahead to future lunar exploration missions, such as crewed lunar exploration and establishing lunar scientific research stations, the lunar rovers will need to cover vast distances. These distances could range from kilometers to tens of kilometers, and even hundreds and thousands of kilometers. Therefore, it is crucial to develop effective long-range path planning for lunar rovers to meet the demands of lunar patrol exploration. This paper presents a hierarchical map model path planning method that utilizes the existing high-resolution images, digital elevation models and mineral abundance maps. The objective is to address the issue of the construction of lunar rover travel costs in the absence of large-scale, high-resolution digital elevation models. This method models the reference and semantic layers using the middle- and low-resolution remote sensing data. The multi-scale obstacles on the lunar surface are extracted by combining the deep learning algorithm on the high-resolution image, and the obstacle avoidance layer is modeled. A two-stage exploratory path planning decision is employed for long-distance driving path planning on a global–local scale. The proposed method analyzes the long-distance accessibility of various areas of scientific significance, such as Rima Bode. A high-precision digital elevation model is created using stereo images to validate the method. Based on the findings, it can be observed that the entire route spans a distance of 930.32 km. The route demonstrates an impressive ability to avoid meter-level impact craters and linear structures while maintaining an average slope of less than 8°. This paper explores scientific research by traversing at least seven basalt units, uncovering the secrets of lunar volcanic activities, and establishing ‘golden spike’ reference points for lunar stratigraphy. The final result of path planning can serve as a valuable reference for the design, mission demonstration, and subsequent project implementation of the new manned lunar rover.展开更多
Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and treatment.However,achieving precise segmentation remains a challenge due to vari...Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and treatment.However,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound images.Although existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,etc.To address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule segmentation.The MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding methods.This transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the data.The approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the data.Furthermore,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation accuracy.Experimental results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital Database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)dataset.These findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.展开更多
Coal is an essential component of global energy;however,the processes of coal mining and utilization produce significant amounts of coal mine goafs,accompanied by coal-based solid wastes and emitted CO_(2),resulting i...Coal is an essential component of global energy;however,the processes of coal mining and utilization produce significant amounts of coal mine goafs,accompanied by coal-based solid wastes and emitted CO_(2),resulting in severe ecological and environmental challenges.In response to this issue,this study pro-poses a novel approach for filling coal mine goafs using cementitious materials prepared by coal-based solid wastes mineralized with CO_(2)(15%in concentration).The CO_(2) sequestration capacities of individual solid wastes are ranked as follows:carbide slag(CS)>red mud(RM)>fly ash(FA).The performance of filling material prepared from composite solid waste(FA-CS-RM)mineralized with CO_(2) meets the filling requirements of goaf.The filling material(F60C20R20)obtained by CO_(2) mineralization was 14.9 MPa in maximum compressive strength,increasing by 32.2%compared to the non-mineralized material.The prepared filling material exhibits excellent CO_(2) sequestration capacity(i.e.,14.4 kg·t^(−1) in maximum amount of CO_(2) sequestration).According to the analysis of carbon sequestration potential,in China,the annual production of FA,CS,and RM is approximately 899,30,and 107 Mt,respectively in the year of 2023.The utilization of FA,CS,and RM individually can achieve carbon emission reductions of 3.42,10.78,and 0.61 Mt,respectively.The composite solid waste(FA-CS-RM)mineralized with CO_(2) can achieve 1.23 Mt in carbon emissions reduction.Additionally,taking Yellow River Basin of China as a case study,the total volume of underground space in coal mine goafs from 2016 to 2030 is estimated at 8.16 Gm3,indicating that this technology can sequester 0.18 Gt of CO_(2).This approach offers a promising solution for large-scale flue gas CO_(2) sequestration,recycling coal-based solid wastes,and remediating coal mine goafs,contributing to green utilization of coal and the emission reduction of carbon.展开更多
A Bayes discriminant analysis method to identify the risky of complicated goaf in mines was presented. Nine factors influencing the stability of goaf risky, including uniaxial compressive strength of rock, elastic mod...A Bayes discriminant analysis method to identify the risky of complicated goaf in mines was presented. Nine factors influencing the stability of goaf risky, including uniaxial compressive strength of rock, elastic modulus of rock, rock quality designation （RQD）, area ratio of pillar, ratio of width to height of pillar, depth of ore body, volume of goaf, dip of ore body and area of goal, were selected as discriminant indexes in the stability analysis of goal. The actual data of 40 goals were used as training samples to establish a discriminant analysis model to identify the stability of goaf. The results show that this discriminant analysis model has high precision and misdiscriminant ratio is 0.025 in re-substitution process. The instability identification of a metal mine was distinguished by using this model and the identification result is identical with that of practical situation.展开更多
基金Science and Technology Major Project of Xinjiang Uygur Autonomous Region(2020A03003-7)Fundamental Research on Natural Science Program of Shaanxi Province(2021JM-180)+2 种基金Fundamental Research Funds for the Central Universities,CHD(Project for Leading Talents)(300102211302)Tianshan Cedar Plan of Science and Technology Department of Xinjiang Uygur Autonomous Region(2017XS13)Shaanxi Province Young Talent Lifting Program(CLGC202219).
文摘When the expressway crosses the goafs inevitably,the design is generally to build the road on coal pillars as much as possible.However,the existing coal pillars are often unable to meet relevant requirements of highway construction.Combining three-dimensional physical model tests,numerical simulations and field monitoring,with the Urumqi East Second Ring Road passing through acute inclined goafs as a background,the deformation and failure mechanism of the overlying rock and coal pillars in acute inclined goafs under expressway load were studied.And in accordance with construction requirements of subgrade,comprehensive consideration of the deformation and instability mechanism of acute inclined goafs,the treatment measures and suggestions for this type of geological disasters were put forward.The research results confirmed the rationality of coal pillars in acute inclined goafs under the expressway through grouting.According to the ratio of diff erent overlying rock thickness to coal pillar height,the change trend and value of the required grouting range were summarized,which can provide reference for similar projects.
基金supported by the National Natural Science Foundation of China(No.52090041).
文摘The influence of different solution and aging conditions on the microstructure,impact toughness,and crack initiation and propagation mechanisms of the novel α+β titanium alloy Ti6422 was systematically investigated.By adjusting the furnace cooling time after solution treatment and the aging temperature,Ti6422 alloy samples were developed with a multi-level lamellar microstructure,in-cluding microscaleαcolonies and α_(p) lamellae,as well as nanoscale α_(s) phases.Extending the furnace cooling time after solution treatment at 920℃ for 1 h from 240 to 540 min,followed by aging at 600℃ for 6 h,increased the α_(p) lamella content,reduced the α_(s) phase content,expanded theαcolonies and α_(p) lamellae size,and improved the impact toughness from 22.7 to 53.8 J/cm^(2).Additionally,under the same solution treatment,raising the aging temperature from 500 to 700℃ resulted in a decrease in the α_(s) phase content and a growth in the thickness of the α_(p) lamella and α_(s) phase.The impact toughness increased significantly with these changes.Samples with high α_(p) lamellae content or large α_(s) phase size exhibited high crack initiation and propagation energies.Impact deformation caused severe kinking of the α_(p) lamellae in crack initiation and propagation areas,leading to a uniform and high-density kernel average misorientation(KAM)distribu-tion,enhancing plastic deformation coordination and uniformity.Moreover,the multidirectional arrangement of coarserαcolonies and α_(p) lamellae continuously deflect the crack propagation direction,inhibiting crack propagation.
基金Projects NCET-05-0480 supported by the New Century Excellent Talents in University50904063 by the National Natural Science Foundation of China+1 种基金07KF09 by the Research Fund of the State Key Laboratory of Coal Resources and Mine Safety of China University of Mining & Technology2008A003 and 2005B002 by the Scientific Research Foundation of China University of Mining & Technology
文摘In exploiting shallow coal resources in western China, conservation of water resources is often subjugated to considerations of safety and production in coal mines. In order to maintain a sustainable development in the Shenfu-Dongsheng coalfield, we propose a technology of constructing groundwater reservoirs in goafs in shallow coalfields to protect fragile ecological environments. Given the premise of safe production, we selected an appropriate goaf as the site for constructing a groundwater reservoir and used a mine water recharge technique in combination with other related techniques for effective water conservation. Then filtering and purification techniques were used to purify the mine water given the physical and chemical properties of mine water and its filling material, ,thereby greatly reducing suspended matter, calcium and other harmful ions in the water. With the potential of widely application, the research result has been successfully applied in the Daliuta coal mine, to great economic and ecological effect. Therefore, this achievement provides a new way for mine water conservation in shallow coal resources in western China.
基金Project(51074178)supported by the National Natural Science Foundation of ChinaProject(2011ssxt274)supported by the Graduated Students’ Research and Innovation Foundation of Central South University of China+1 种基金Project(2011QNZT087)supported by the Graduated Students’ Free Exploration Foundation of Central South University of ChinaProject(1343-76140000011)supported by Scholarship Award for Excellent Doctoral Student granted by Ministry of Education,China
文摘In order to precisely predict the hazard degree of goaf(HDG), the RS-TOPSIS model was built based on the results of expert investigation. To evaluate the HDG in the underground mine, five structure size factors, i.e. goaf span, exposed area, goaf height, goaf depth, and pillar width, were selected as the evaluation indexes. And based on rough dependability in rough set(RS)theory, the weights of evaluation indexes were identified by calculating rough dependability between evaluation indexes and evaluation results. Fourty goafs in some mines of western China, whose indexes parameters were measured by cavity monitoring system(CMS), were taken as evaluation objects. In addition, the characteristic parameters of five grades' typical goafs were built according to the interval limits value of single index evaluation. Then, using the technique for order preference by similarity to ideal solution(TOPSIS), five-category classification of HDG was realized based on closeness degree, and the HDG was also identified.Results show that the five-category identification of mine goafs could be realized by RS-TOPSIS method, based on the structure-scale-effect. The classification results are consistent with those of numerical simulation based on stress and displacement,while the coincidence rate is up to 92.5%. Furthermore, the results are more conservative to safety evaluation than numerical simulation, thus demonstrating that the proposed method is more easier, reasonable and more definite for HDG identification.
基金National Nature Science Foundation of China(50704024)Shanxi Youth Sci-Tech Research Foundation(2007021024)Taiyuan Innovation Program(special item for undergraduate innovation and starting business)(07010746)
文摘Targeting at the coal seam with useful value discarded above goafs,attempted to explore the feasibility of'mining technique in the condition of floor failure' from theoretical point of view,and predicted.It indicated that mining technique in the condition of floor failure used above Longwall Goafs in Baijiazhuang Mining is totally feasible.At law,the deformation of the floor in the mining technique by means of probability-integral method.And it is discov- ered that deformed basin can emerge in the footwall of No.6 coal seam and its maximum subsidence was possibly 1 633 mm or so and its maximum positive curvature is 61.74/10^(-3). At last,it therefore suggests appropriate ground pressure control measures as strengthening observation of ground pressure and adopting false slope for exploitation and strengthening support for reasonable push and slide based on the adverse ground pressure behaviors possibly occurring in the mining technique.This serves to gather data and lay sturdy founda- tion for further probe into the mining technique,and offers theoretical and technical grounds for concrete implementation of the mining technique.
基金National Natural Science Foundation of China(Nos.42301473,42271424,42171397)Chinese Postdoctoral Innovation Talents Support Program(No.BX20230299)+2 种基金China Postdoctoral Science Foundation(No.2023M742884)Natural Science Foundation of Sichuan Province(Nos.24NSFSC2264,2025ZNSFSC0322)Key Research and Development Project of Sichuan Province(No.24ZDYF0633).
文摘As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods generally have problems such as insufficient 3D scene description capability and low dynamic update efficiency,which are difficult to meet the demand of real-time accurate management.For this reason,this paper proposes a vehicle twin modeling method for road tunnels.This approach starts from the actual management needs,and supports multi-level dynamic modeling from vehicle type,size to color by constructing a vehicle model library that can be flexibly invoked;at the same time,semantic constraint rules with geometric layout,behavioral attributes,and spatial relationships are designed to ensure that the virtual model matches with the real model with a high degree of similarity;ultimately,the prototype system is constructed and the case region is selected for the case study,and the dynamic vehicle status in the tunnel is realized by integrating real-time monitoring data with semantic constraints for precise virtual-real mapping.Finally,the prototype system is constructed and case experiments are conducted in selected case areas,which are combined with real-time monitoring data to realize dynamic updating and three-dimensional visualization of vehicle states in tunnels.The experiments show that the proposed method can run smoothly with an average rendering efficiency of 17.70 ms while guaranteeing the modeling accuracy(composite similarity of 0.867),which significantly improves the real-time and intuitive tunnel management.The research results provide reliable technical support for intelligent operation and emergency response of road tunnels,and offer new ideas for digital twin modeling of complex scenes.
基金financially supported by Guangdong Province Basic and Applied Basic Research Fund Project(Grant No.2022B1515250009)Liaoning Provincial Natural Science Foundation-Doctoral Research Start-up Fund Project(Grant No.2024-BSBA-05)+1 种基金Major Science and Technology Innovation Project in Shandong Province(Grant No.2024CXGC010803)the National Natural Science Foundation of China(Grant Nos.52271269 and 12302147).
文摘The umbilical,a key component in offshore energy extraction,plays a vital role in ensuring the stable operation of the entire production system.The extensive variety of cross-sectional components creates highly complex layout combinations.Furthermore,due to constraints in component quantity and geometry within the cross-sectional layout,filler bodies must be incorporated to maintain cross-section performance.Conventional design approaches based on manual experience suffer from inefficiency,high variability,and difficulties in quantification.This paper presents a multi-level automatic filling optimization design method for umbilical cross-sectional layouts to address these limitations.Initially,the research establishes a multi-objective optimization model that considers compactness,balance,and wear resistance of the cross-section,employing an enhanced genetic algorithm to achieve a near-optimal layout.Subsequently,the study implements an image processing-based vacancy detection technique to accurately identify cross-sectional gaps.To manage the variability and diversity of these vacant regions,the research introduces a multi-level filling method that strategically selects and places filler bodies of varying dimensions,overcoming the constraints of uniform-size fillers.Additionally,the method incorporates a hierarchical strategy that subdivides the complex cross-section into multiple layers,enabling layer-by-layer optimization and filling.This approach reduces manufac-turing equipment requirements while ensuring practical production process feasibility.The methodology is validated through a specific umbilical case study.The results demonstrate improvements in compactness,balance,and wear resistance compared with the initial cross-section,offering novel insights and valuable references for filler design in umbilical cross-sections.
基金support from the Natural Science Foundation of Jiangsu Province(Grant No.BK20242059)the Collaborative Innovation Center for Prevention and Control of Mountain Geological Hazards of Zhejiang Province(PCMGH-2023-02)the opening fund of State Key Laboratory of Coal Mine Disaster Dynamics and Control(2011DA105827-FW202209)are gratefully acknowledged.
文摘The stability and fracture behavior of a goaf roof beneath an open-pit bench are critical concerns,especially under impact loading.However,the effect of the thickness-to-span ratio on dynamic failure modes remains largely unexplored,as existing research focuses mainly on static stability.Energy dissipation and instability evolution under impact loading require further study.To address this gap,this study conducts drop-weight impact experiments on specimens with circular perforations,complemented by numerical simulations.By integrating dimensional analysis,cusp catastrophe theory,and strength reduction techniques,the dynamic instability mechanism of goaf roofs with varying thickness-to-span ratios is revealed.Results show that the thickness-to-span ratio significantly influences energy accumulation and dissipation during roof failure.A higher ratio increases both the magnitude and rate of energy dissipation,particularly during crack initiation and stable propagation,while its impact diminishes in the final failure stage.Optimizing the thickness-to-span ratio within a critical range enhances structural stability,improving the safety factor by up to 83%.However,beyond a certain threshold,additional thickness yields diminishing benefits.This study provides new insights into the energy-based instability mechanism of goaf roofs under impact loads,establishing a theoretical foundation for early warning systems and optimized safety design.
文摘China has a long history of coal mining,among which open-pit coal mines have a large number of small coal mine goafs underground.The distribution,shape,structure and other characteristics of goafs are isolated and discontinuous,and there is no definite geological law to follow,which seriously threatens the safety of coal mine production and personnel life.Conventional ground geophysical methods have low accuracy in detecting goaf areas affected by mechanical interference from open-pit mines,especially for waterless goaf areas,which cannot be detected by existing methods.This article proposes the use of high-frequency electromagnetic waves for goaf detection.The feasibility of using drilling radar to detect goaf was theoretically analyzed,and a goaf detection model was established.The response characteristics of different fillers in the goaf under different frequencies of high-frequency electromagnetic waves were simulated and analyzed.In a certain open-pit mine in Inner Mongolia,100MHz high-frequency electromagnetic waves were used to detect the goaf through directional drilling on the ground.After detection,excavation verification was carried out,and the location of one goaf detected was verified.The results of engineering practice show that the application of high-frequency electromagnetic waves in goaf detection expands the detection radius of boreholes,has the advantages of high efficiency and accuracy,and has important theoretical and practical significance.
文摘China,as the world’s largest coal producer and consumer,faces increasingly severe challenges from coal mine goaf areas formed through decades of intensive mining.These underground voids,resulting from exhausted resources or technical limitations,not only cause environmental issues like land subsidence and groundwater contamination but also pose critical safety risks for ongoing mining operations,including water inrushes,gas outbursts,and roof collapses.Conventional geophysical methods such as seismic surveys and electromagnetic detection demonstrate limited effectiveness in complex geological conditions due to susceptibility to electrical heterogeneity,electromagnetic interference,and interpretation ambiguities.This study presents an innovative integrated approach combining the Audio-Frequency Electrical Transillumination(AFET)method with multi-parameter borehole logging to establish a three-dimensional detection system.The AFET technique employs 0.1–10 kHz electromagnetic waves to identify electrical anomalies associated with goafs,enabling extensive horizontal scanning.This is complemented by vertical high-resolution profiling through borehole measurements of resistivity,spontaneous potential,and acoustic velocity.Field applications in Shanxi Province’s typical coal mines achieved breakthrough results:Using a grid-drilling pattern(15 m spacing,300 m depth),the method successfully detected three concealed goafs missed by conventional approaches,with spatial positioning errors under 0.5 m.Notably,it accurately identified two un-collapsed water-filled cavities.This surface-borehole synergistic approach overcomes single-method limitations,enhancing goaf detection accuracy to over 92%.The technique provides reliable technical support for safe mining practices and represents significant progress in precise detection of hidden geological hazards in Chinese coal mines,offering valuable insights for global mining geophysics.
基金the funding support from the National Natural Science Foundation of China(Grant No.52308340)Chongqing Talent Innovation and Entrepreneurship Demonstration Team Project(Grant No.cstc2024ycjh-bgzxm0012)the Science and Technology Projects supported by China Coal Technology and Engineering Chongqing Design and Research Institute(Group)Co.,Ltd.(Grant No.H20230317).
文摘Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,there is limited research on the spatiotemporal characteristics of landslide deformation.This paper proposes a novel Multi-Relation Spatiotemporal Graph Residual Network with Multi-Level Feature Attention(MFA-MRSTGRN)that effectively improves the prediction performance of landslide displacement through spatiotemporal fusion.This model integrates internal seepage factors as data feature enhancements with external triggering factors,allowing for accurate capture of the complex spatiotemporal characteristics of landslide displacement and the construction of a multi-source heterogeneous dataset.The MFA-MRSTGRN model incorporates dynamic graph theory and four key modules:multilevel feature attention,temporal-residual decomposition,spatial multi-relational graph convolution,and spatiotemporal fusion prediction.This comprehensive approach enables the efficient analyses of multi-source heterogeneous datasets,facilitating adaptive exploration of the evolving multi-relational,multi-dimensional spatiotemporal complexities in landslides.When applying this model to predict the displacement of the Liangshuijing landslide,we demonstrate that the MFA-MRSTGRN model surpasses traditional models,such as random forest(RF),long short-term memory(LSTM),and spatial temporal graph convolutional networks(ST-GCN)models in terms of various evaluation metrics including mean absolute error(MAE=1.27 mm),root mean square error(RMSE=1.49 mm),mean absolute percentage error(MAPE=0.026),and R-squared(R^(2)=0.88).Furthermore,feature ablation experiments indicate that incorporating internal seepage factors improves the predictive performance of landslide displacement models.This research provides an advanced and reliable method for landslide displacement prediction.
基金Fundamental Research Funds for the Central Universities,Grant/Award Number:2024KYJD1012。
文摘The potential for CO_(2)sequestration in the goaf of abandoned coal mines is significant due to the extensive fracture spaces and substantial residual coal present.Firstly,the adsorption characteristics of residual coal in goaf on CO_(2)were studied by the isothermal adsorption test of CO_(2).Then,to accurately calculate the amount of adsorbed CO_(2)within the residual coal in the goaf,the bidisperse diffusion numerical model considering only Fick diffusion was modified in combination with the diffusion mechanisms.The simulation results showed that the modified model can well describe the diffusion behavior of CO_(2)in the residual coal matrix.Finally,the numerical simulation of CO_(2)sequestration in the goaf of abandoned coal mines was carried out,and the influence of different injection well deployment positions and various thicknesses of residual coal on the migration law and storage effect of CO_(2)in goaf was analyzed.The results showed that CO_(2)preferentially flowed into the caving zone with higher permeability.The distribution of CO_(2)streamlines in the goaf was the most dense in the caving zone and the streamlines in the fracture zone were gradually sparse from bottom to top.When the injection well was deployed at the interface of the two zones,the CO_(2)had the best seepage path.The total storage capacity within90 days was 7.702754×10^(6)kg,of which the free state storage capacity in the fracture of the goaf and the adsorbed state storage capacity in the residual coal were 6.611451×10^(6)and 1.091303×10^(6)kg,respectively.When the injection well was deployed in the middle of the residual coal seam in the goaf and the middle of the fracture zone,the total storage capacity at the same time was 7.613508×10^(6)and 6.021495×10^(6)kg,respectively.The coal with different thicknesses remaining at the bottom of the goaf significantly affected the adsorbed state storage,but had little effect on the free state storage.When the thickness of the residual coal seam was 0.20,0.35,and 0.50 m,the adsorbed state storage capacity within 130 days was 4.37623×10^(5),7.65791×10^(5),and 1.093406×10^(6)kg,respectively.
基金supported by the Fund of Key Laboratory of Biomedical Engineering of Hainan Province(No.BME20240001)the STI2030-Major Projects(No.2021ZD0200104)the National Natural Science Foundations of China under Grant 61771437.
文摘Deep learning networks are increasingly exploited in the field of neuronal soma segmentation.However,annotating dataset is also an expensive and time-consuming task.Unsupervised domain adaptation is an effective method to mitigate the problem,which is able to learn an adaptive segmentation model by transferring knowledge from a rich-labeled source domain.In this paper,we propose a multi-level distribution alignment-based unsupervised domain adaptation network(MDA-Net)for segmentation of 3D neuronal soma images.Distribution alignment is performed in both feature space and output space.In the feature space,features from different scales are adaptively fused to enhance the feature extraction capability for small target somata and con-strained to be domain invariant by adversarial adaptation strategy.In the output space,local discrepancy maps that can reveal the spatial structures of somata are constructed on the predicted segmentation results.Then thedistribution alignment is performed on the local discrepancies maps across domains to obtain a superior discrepancy map in the target domain,achieving refined segmentation performance of neuronal somata.Additionally,after a period of distribution align-ment procedure,a portion of target samples with high confident pseudo-labels are selected as training data,which assist in learning a more adaptive segmentation network.We verified the superiority of the proposed algorithm by comparing several domain adaptation networks on two 3D mouse brain neuronal somata datasets and one macaque brain neuronal soma dataset.
基金co-supported by the National Key Research and Development Program of China(No.2022YFF0503100)the Youth Innovation Project of Pandeng Program of National Space Science Center,Chinese Academy of Sciences(No.E3PD40012S).
文摘As we look ahead to future lunar exploration missions, such as crewed lunar exploration and establishing lunar scientific research stations, the lunar rovers will need to cover vast distances. These distances could range from kilometers to tens of kilometers, and even hundreds and thousands of kilometers. Therefore, it is crucial to develop effective long-range path planning for lunar rovers to meet the demands of lunar patrol exploration. This paper presents a hierarchical map model path planning method that utilizes the existing high-resolution images, digital elevation models and mineral abundance maps. The objective is to address the issue of the construction of lunar rover travel costs in the absence of large-scale, high-resolution digital elevation models. This method models the reference and semantic layers using the middle- and low-resolution remote sensing data. The multi-scale obstacles on the lunar surface are extracted by combining the deep learning algorithm on the high-resolution image, and the obstacle avoidance layer is modeled. A two-stage exploratory path planning decision is employed for long-distance driving path planning on a global–local scale. The proposed method analyzes the long-distance accessibility of various areas of scientific significance, such as Rima Bode. A high-precision digital elevation model is created using stereo images to validate the method. Based on the findings, it can be observed that the entire route spans a distance of 930.32 km. The route demonstrates an impressive ability to avoid meter-level impact craters and linear structures while maintaining an average slope of less than 8°. This paper explores scientific research by traversing at least seven basalt units, uncovering the secrets of lunar volcanic activities, and establishing ‘golden spike’ reference points for lunar stratigraphy. The final result of path planning can serve as a valuable reference for the design, mission demonstration, and subsequent project implementation of the new manned lunar rover.
文摘Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and treatment.However,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound images.Although existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,etc.To address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule segmentation.The MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding methods.This transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the data.The approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the data.Furthermore,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation accuracy.Experimental results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital Database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)dataset.These findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.
基金supported by the National Natural Science Foundation of China(U21A20321 and 22378241)Research Project Supported by Shanxi Scholarship Council of China(2024-015).
文摘Coal is an essential component of global energy;however,the processes of coal mining and utilization produce significant amounts of coal mine goafs,accompanied by coal-based solid wastes and emitted CO_(2),resulting in severe ecological and environmental challenges.In response to this issue,this study pro-poses a novel approach for filling coal mine goafs using cementitious materials prepared by coal-based solid wastes mineralized with CO_(2)(15%in concentration).The CO_(2) sequestration capacities of individual solid wastes are ranked as follows:carbide slag(CS)>red mud(RM)>fly ash(FA).The performance of filling material prepared from composite solid waste(FA-CS-RM)mineralized with CO_(2) meets the filling requirements of goaf.The filling material(F60C20R20)obtained by CO_(2) mineralization was 14.9 MPa in maximum compressive strength,increasing by 32.2%compared to the non-mineralized material.The prepared filling material exhibits excellent CO_(2) sequestration capacity(i.e.,14.4 kg·t^(−1) in maximum amount of CO_(2) sequestration).According to the analysis of carbon sequestration potential,in China,the annual production of FA,CS,and RM is approximately 899,30,and 107 Mt,respectively in the year of 2023.The utilization of FA,CS,and RM individually can achieve carbon emission reductions of 3.42,10.78,and 0.61 Mt,respectively.The composite solid waste(FA-CS-RM)mineralized with CO_(2) can achieve 1.23 Mt in carbon emissions reduction.Additionally,taking Yellow River Basin of China as a case study,the total volume of underground space in coal mine goafs from 2016 to 2030 is estimated at 8.16 Gm3,indicating that this technology can sequester 0.18 Gt of CO_(2).This approach offers a promising solution for large-scale flue gas CO_(2) sequestration,recycling coal-based solid wastes,and remediating coal mine goafs,contributing to green utilization of coal and the emission reduction of carbon.
基金Project (2010CB732004) supported by the National Basic Research Program of China
文摘A Bayes discriminant analysis method to identify the risky of complicated goaf in mines was presented. Nine factors influencing the stability of goaf risky, including uniaxial compressive strength of rock, elastic modulus of rock, rock quality designation （RQD）, area ratio of pillar, ratio of width to height of pillar, depth of ore body, volume of goaf, dip of ore body and area of goal, were selected as discriminant indexes in the stability analysis of goal. The actual data of 40 goals were used as training samples to establish a discriminant analysis model to identify the stability of goaf. The results show that this discriminant analysis model has high precision and misdiscriminant ratio is 0.025 in re-substitution process. The instability identification of a metal mine was distinguished by using this model and the identification result is identical with that of practical situation.
