Assessing the stability of pillars in underground mines(especially in deep underground mines)is a critical concern during both the design and the operational phases of a project.This study mainly focuses on developing...Assessing the stability of pillars in underground mines(especially in deep underground mines)is a critical concern during both the design and the operational phases of a project.This study mainly focuses on developing two practical models to predict pillar stability status.For this purpose,two robust models were developed using a database including 236 case histories from seven underground hard rock mines,based on gene expression programming(GEP)and decision tree-support vector machine(DT-SVM)hybrid algorithms.The performance of the developed models was evaluated based on four common statistical criteria(sensitivity,specificity,Matthews correlation coefficient,and accuracy),receiver operating characteristic(ROC)curve,and testing data sets.The results showed that the GEP and DT-SVM models performed exceptionally well in assessing pillar stability,showing a high level of accuracy.The DT-SVM model,in particular,outperformed the GEP model(accuracy of 0.914,sensitivity of 0.842,specificity of 0.929,Matthews correlation coefficient of 0.767,and area under the ROC of 0.897 for the test data set).Furthermore,upon comparing the developed models with the previous ones,it was revealed that both models can effectively determine the condition of pillar stability with low uncertainty and acceptable accuracy.This suggests that these models could serve as dependable tools for project managers,aiding in the evaluation of pillar stability during the design and operational phases of mining projects,despite the inherent challenges in this domain.展开更多
The traction characteristics of the grouser, cutting the simulative soil of deepsea sediment, with different tooth widths, tooth heights, and ground pressures are studied with traction characteristic test apparatus. A...The traction characteristics of the grouser, cutting the simulative soil of deepsea sediment, with different tooth widths, tooth heights, and ground pressures are studied with traction characteristic test apparatus. A traction-displacement model is obtained by combining the analysis of the cutting mechanism. The results show that the tractiondisplacement curves of grousers with different tooth widths, tooth heights, and ground pressures have the same changing trend, which matches the Wong traction model. Their sensitivity coefficient and shear modulus are slightly fluctuated. Therefore, the average values can be used as the traction model parameters. The maximum traction of the grouser with a two-side edge and a 10 mm tooth width increment changing with the tooth height and ground pressure can be determined according to the grousers with different tooth widths. By combining the traction model parameters, the traction-displacement curve of the grouser with a certain group values of tooth width, tooth height, and ground pressure can be predicted. Therefore, the slip of the mining machine can be prevented to improve the mining efficiency.展开更多
The risk of rockbursts is one of the main threats in hard coal mines. Compared to other underground mines, the number of factors contributing to the rockburst at underground coal mines is much greater.Factors such as ...The risk of rockbursts is one of the main threats in hard coal mines. Compared to other underground mines, the number of factors contributing to the rockburst at underground coal mines is much greater.Factors such as the coal seam tendency to rockbursts, the thickness of the coal seam, and the stress level in the seam have to be considered, but also the entire coal seam-surrounding rock system has to be evaluated when trying to predict the rockbursts. However, in hard coal mines, there are stroke or stress-stroke rockbursts in which the fracture of a thick layer of sandstone plays an essential role in predicting rockbursts. The occurrence of rockbursts in coal mines is complex, and their prediction is even more difficult than in other mines. In recent years, the interest in machine learning algorithms for solving complex nonlinear problems has increased, which also applies to geosciences. This study attempts to use machine learning algorithms, i.e. neural network, decision tree, random forest, gradient boosting, and extreme gradient boosting(XGB), to assess the rockburst hazard of an active hard coal mine in the Upper Silesian Coal Basin. The rock mass bursting tendency index WTGthat describes the tendency of the seam-surrounding rock system to rockbursts and the anomaly of the vertical stress component were applied for this purpose. Especially, the decision tree and neural network models were proved to be effective in correctly distinguishing rockbursts from tremors, after which the excavation was not damaged. On average, these models correctly classified about 80% of the rockbursts in the testing datasets.展开更多
According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are comput...According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are computed to determine the time delay and the embedding dimension.Due to different features of the data,data mining algorithm is conducted to classify the data into different groups.Redundant information is eliminated by the advantage of data mining technology,and the historical loads that have highly similar features with the forecasting day are searched by the system.As a result,the training data can be decreased and the computing speed can also be improved when constructing support vector machine(SVM) model.Then,SVM algorithm is used to predict power load with parameters that get in pretreatment.In order to prove the effectiveness of the new model,the calculation with data mining SVM algorithm is compared with that of single SVM and back propagation network.It can be seen that the new DSVM algorithm effectively improves the forecast accuracy by 0.75%,1.10% and 1.73% compared with SVM for two random dimensions of 11-dimension,14-dimension and BP network,respectively.This indicates that the DSVM gains perfect improvement effect in the short-term power load forecasting.展开更多
Discrimination of seismicity distributed in different areas is essential for reliable seismic risk assessment in mines.Although machine learning has been widely applied in seismic data processing,feasibility and relia...Discrimination of seismicity distributed in different areas is essential for reliable seismic risk assessment in mines.Although machine learning has been widely applied in seismic data processing,feasibility and reliability of applying this technique to classify spatially clustered seismic events in underground mines are yet to be investigated.In this research,two groups of seismic events with a minimum local magnitude(ML) of-3 were observed in an underground coal mine.They were respectively located around a dyke and the longwall face.Additionally,two types of undesired signals were also recorded.Four machine learning methods,i.e.random forest(RF),support vector machine(SVM),deep convolutional neural network(DCNN),and residual neural network(ResNN),were used for classifying these signals.The results obtained based on a primary dataset showed that these seismic events could be classified with at least 91% accuracy.The DCNN using seismogram images as the inputs reached the best performance with more than 94% accuracy.As mining is a dynamic progress which could change the characteristics of seismic signals,the temporal variance in the prediction performance of DCNN was also investigated to assess the reliability of this classifier during mining.A cascaded workflow consisting of database update,model training,signal prediction,and results review was established.By progressively calibrating the DCNN model,it achieved up to 99% prediction accuracy.The results demonstrated that machine learning is a reliable tool for the automatic discrimination of spatially clustered seismicity in underground mining.展开更多
A procedure to recognize individual discontinuities in rock mass from measurement while drilling(MWD)technology is developed,using the binary pattern of structural rock characteristics obtained from in-hole images for...A procedure to recognize individual discontinuities in rock mass from measurement while drilling(MWD)technology is developed,using the binary pattern of structural rock characteristics obtained from in-hole images for calibration.Data from two underground operations with different drilling technology and different rock mass characteristics are considered,which generalizes the application of the methodology to different sites and ensures the full operational integration of MWD data analysis.Two approaches are followed for site-specific structural model building:a discontinuity index(DI)built from variations in MWD parameters,and a machine learning(ML)classifier as function of the drilling parameters and their variability.The prediction ability of the models is quantitatively assessed as the rate of recognition of discontinuities observed in borehole logs.Differences between the parameters involved in the models for each site,and differences in their weights,highlight the site-dependence of the resulting models.The ML approach offers better performance than the classical DI,with recognition rates in the range 89%to 96%.However,the simpler DI still yields fairly accurate results,with recognition rates 70%to 90%.These results validate the adaptive MWD-based methodology as an engineering solution to predict rock structural condition in underground mining operations.展开更多
This study was conducted to establish a Support Vector Machines(SVM)-Markov Chain prediction model for prediction of mining water inflow. According to the raw data sequence, the Support Vector Machines(SVM) model was ...This study was conducted to establish a Support Vector Machines(SVM)-Markov Chain prediction model for prediction of mining water inflow. According to the raw data sequence, the Support Vector Machines(SVM) model was built, and then revised by means of a Markov state change probability matrix. Through dividing the state and analyzing absolute errors and relative errors and other indexes of the measured value and the fitted value of SVM, the prediction results were improved. Finally,the model was used to calculate relative errors. Through predicting and analyzing mining water inflow, the prediction results of the model were satisfactory. The results of this study enlarge the application scope of the Support Vector Machines(SVM) prediction model and provide a new method for scientific forecasting water inflow in coal mining.展开更多
Assessing machine's performance through comparing the same or similar machines is important to implement intelligent maintenance for swarm machine.In this paper,an outlier mining based abnormal machine detection a...Assessing machine's performance through comparing the same or similar machines is important to implement intelligent maintenance for swarm machine.In this paper,an outlier mining based abnormal machine detection algorithm is proposed for this purpose.Firstly,the outlier mining based on clustering is introduced and the definition of cluster-based global outlier factor(CBGOF) is presented.Then the modified swarm intelligence clustering(MSIC) algorithm is suggested and the outlier mining algorithm based on MSIC is proposed.The algorithm can not only cluster machines according to their performance but also detect possible abnormal machines.Finally,a comparison of mobile soccer robots' performance proves the algorithm is feasible and effective.展开更多
An approach which combines particle swarm optimization and support vector machine(PSO–SVM)is proposed to forecast large-scale goaf instability(LSGI).Firstly,influencing factors of goaf safety are analyzed,and followi...An approach which combines particle swarm optimization and support vector machine(PSO–SVM)is proposed to forecast large-scale goaf instability(LSGI).Firstly,influencing factors of goaf safety are analyzed,and following parameters were selected as evaluation indexes in the LSGI:uniaxial compressive strength(UCS)of rock,elastic modulus(E)of rock,rock quality designation(RQD),area ration of pillar(Sp),the ratio of width to height of the pillar(w/h),depth of ore body(H),volume of goaf(V),dip of ore body(a)and area of goaf(Sg).Then LSGI forecasting model by PSO-SVM was established according to the influencing factors.The performance of hybrid model(PSO+SVM=PSO–SVM)has been compared with the grid search method of support vector machine(GSM–SVM)model.The actual data of 40 goafs are applied to research the forecasting ability of the proposed method,and two cases of underground mine are also validated by the proposed model.The results indicated that the heuristic algorithm of PSO can speed up the SVM parameter optimization search,and the predictive ability of the PSO–SVM model with the RBF kernel function is acceptable and robust,which might hold a high potential to become a useful tool in goaf risky prediction research.展开更多
Remote sensing,particularly satellite-based,can play a valuable role in monitoring areas prone to geohazards.The high spatial and temporal coverage provided by satellite data can be used to reconstruct past events and...Remote sensing,particularly satellite-based,can play a valuable role in monitoring areas prone to geohazards.The high spatial and temporal coverage provided by satellite data can be used to reconstruct past events and continuously monitor sensitive areas for potential hazards.This paper presents a range of techniques and methods that were applied for in-depth analysis and utilization of Earth observation data,with a particular emphasis on:(1)detecting mining subsidence,where a novel approach is proposed by combining an improved U-Net model and Interferometry Synthetic Aperture Radar(InSAR)technology.The results showed that the Efficient Channel Attention(ECA)U-Net model performed better than the U-Net(baseline)model in terms of Mean Intersection over Union(MIoU)and Intersection over Union(IoU)indicators;(2)monitoring water conservancy and hydropower engineering.The Xiaolangdi multipurpose dam complex was monitored using Small BAsline Subsets(SBAS)InSAR method on Sentinel-1 time series data and four small regions with high deformation rates were identified on the slope of the reservoir bank on the north side.The dam body also showed obvious deformation with a velocity exceeding 60 mm/a;(3)the evaluation of the potential of InSAR results to integrate monitoring and warning systems for valuable heritage and architectural preservation.The overall outcome of these methods showed that the use of Artificial Intelligence(AI)techniques in combination with InSAR data leads to more efficient analysis and interpretation,resulting in improved accuracy and prompt identification of potential hazards;and(4)finally,this study also presents a method for detecting landslides in mountainous regions,using optical imagery.The new temporal landslide detection method is evaluated over a 7-year analysis period and unlike conventional bi-temporal change detection methods,this approach does not depend on any prior-knowledge and can potentially detect landslides over extended periods of time such as decades.展开更多
The advancement of intelligent mining in open-pit operations has imposed higher demands on geological transparency,aiming to provide a robust foundation for intelligent drilling and charging.In this study,a linear arr...The advancement of intelligent mining in open-pit operations has imposed higher demands on geological transparency,aiming to provide a robust foundation for intelligent drilling and charging.In this study,a linear array of 120 nodal seismometers was deployed along the surfaces of the C8 and C9 platforms at Fenghuang Mountain to investigate cavities within the rock mass and prevent improper intelligent charging.The seismometers were 1 m apart along measurement lines,with a 2-m spacing between lines,and the monitoring time for each line was set at 2 h.This deployment was paired with spatial autocorrelation and station autocorrelation to analyze ambient noise seismic data and image the velocity and structure within the rock mass.The results demonstrate that the locations and sizes of cavities or loose structures can be accurately identified at the prepared excavation site.Compared with traditional geological exploration methods for openpit mines,the approach in this study off ers higher accuracy,greater efficiency,reduced labor intensity,and insensitivity to water conditions.Ambient noise seismic imaging for detecting adverse geological conditions in open-pit mines provides critical insights and references for intelligent mining advancements.展开更多
Laser cladding of 316 L steel powders on pick substrate of coal mining machine was conducted, and microstructure of laser cladding coating was analyzed. The micro-hardness of laser cladding coating was examined. The r...Laser cladding of 316 L steel powders on pick substrate of coal mining machine was conducted, and microstructure of laser cladding coating was analyzed. The micro-hardness of laser cladding coating was examined. The results show that microstructure of laser cladding zone is exiguous dentrite, and there are hard spots dispersible distribution in the laser cladding zone. Performances of erode-resistant, surface micro-hardness and wear-resistant are improved obviously.展开更多
In this paper, we conduct research on the structured data mining algorithm and applications on machine learning field. Various fields due to the advancement of informatization and digitization, a lot of multi-source a...In this paper, we conduct research on the structured data mining algorithm and applications on machine learning field. Various fields due to the advancement of informatization and digitization, a lot of multi-source and heterogeneous data distributed storage, in order to achieve the sharing, we must solve from the storage management to the interoperability of a series of mechanism, the method and implementation technology. Unstructured data does not have strict structure, therefore, compared with structured information that is more difficult to standardization, with management more difficult. According to these characteristics, the large capacity of unstructured data or using files separately store, is stored in the database index of similar pointer. Under this background, we propose the new idea on the structured data mining algorithm that is meaningful.展开更多
The failure characteristic of talus-derived rock mass continues to challenge quantitative hazard assessments in open-pit mining. Physical model test was used to assess the failure modes and mechanisms on talus-derived...The failure characteristic of talus-derived rock mass continues to challenge quantitative hazard assessments in open-pit mining. Physical model test was used to assess the failure modes and mechanisms on talus-derived rock mass. The different types of failure modes of the talus-derived rock mass were introduced and a possible failure mechanism relation between the failure zone and the structure of the talus-derived rock mass was also shown. The physical model test results indicate that the rainfall has significant influence on the stability and failure modes of talus-derived rock mass during open-pit mining. The development of the seepage area caused by rainfall initiates the localized failure in that particular area, and the initiation of localized instability is mainly induced by stress changes concentrated in the seepage area.展开更多
Alloys designed with the traditional trial and error method have encountered several problems,such as long trial cycles and high costs.The rapid development of big data and artificial intelligence provides a new path ...Alloys designed with the traditional trial and error method have encountered several problems,such as long trial cycles and high costs.The rapid development of big data and artificial intelligence provides a new path for the efficient development of metallic materials,that is,machine learning-assisted design.In this paper,the basic strategy for the machine learning-assisted rational design of alloys was introduced.Research progress in the property-oriented reversal design of alloy composition,the screening design of alloy composition based on models established using element physical and chemical features or microstructure factors,and the optimal design of alloy composition and process parameters based on iterative feedback optimization was reviewed.Results showed the great advantages of machine learning,including high efficiency and low cost.Future development trends for the machine learning-assisted rational design of alloys were also discussed.Interpretable modeling,integrated modeling,high-throughput combination,multi-objective optimization,and innovative platform building were suggested as fields of great interest.展开更多
The retained coal in the end slope of an open-pit mine can be mined by the highwall mining techniques.However,the instability mechanism of the reserved rib pillar under dynamic loads of mining haul trucks and static l...The retained coal in the end slope of an open-pit mine can be mined by the highwall mining techniques.However,the instability mechanism of the reserved rib pillar under dynamic loads of mining haul trucks and static loads of the overlying strata is not clear,which restricts the safe and efcient application of highwall mining.In this study,the load-bearing model of the rib pillar in highwall mining was established,the cusp catastrophe theory and the safety coefcient of the rib pillar were considered,and the criterion equations of the rib pillar stability were proposed.Based on the limit equilibrium theory,the limit stress of the rib pillar was analyzed,and the calculation equations of plastic zone width of the rib pillar in highwall mining were obtained.Based on the Winkler foundation beam theory,the elastic foundation beam model composed of the rib pillar and roof under the highwall mining was established,and the calculation equations for the compression of the rib pillar under dynamic and static loads were developed.The results showed that with the increase of the rib pillar width,the total compression of the rib pillar under dynamic and static loads decreases nonlinearly,and the compression of the rib pillar caused by static loads of the overlying strata and trucks has a decisive role.Numerical simulation and theoretical calculation were also performed in this study.In the numerical simulation,the coal seam with a buried depth of 122 m and a thickness of 3 m is mined by highwall mining techniques.According to the established rib pillar instability model of the highwall mining system,it is found that when the mining opening width is 3 m,the reasonable width of the rib pillar is at least 1.3 m,and the safety factor of the rib pillar is 1.3.The numerical simulation results are in good agreement with the results of theoretical calculation,which verifes the feasibility of the theoretical analysis of the rib pillar stability.This research provides a reference for the stability analysis of rib pillars under highwall mining.展开更多
Given the conditions of residual coal from the boundary of a flat dipping open-pit mine,which uses strip areas mining and inner dumping with slope-covering,we propose an open-pit and underground integrated mining tech...Given the conditions of residual coal from the boundary of a flat dipping open-pit mine,which uses strip areas mining and inner dumping with slope-covering,we propose an open-pit and underground integrated mining technology for residual coal of end slopes.In the proposal a conveyance road and ventilation conveyance near the slope are built,corresponding to the pit mining area and the surface coal mine dump,as well as an interval haulage tunnel and air-inlet tunnel.The outcome shows that such mining method may reduce the effect to slope stability from underground mining,it does not affect the dumping advance and has a high recovery rate of residual coal resources.The working face is timbered by single hydraulic props,transported by a scraper conveyor and supported by coal walls.This method of mining is one of layered top coal caving,with high resource recovery,low production cost where positive economic benefit can be realized.展开更多
Real-time perception of rock mass information is of great importance to efficient tunneling and hazard prevention in tunnel boring machines(TBMs).In this study,a TBM-rock mutual feedback perception method based on dat...Real-time perception of rock mass information is of great importance to efficient tunneling and hazard prevention in tunnel boring machines(TBMs).In this study,a TBM-rock mutual feedback perception method based on data mining(DM) is proposed,which takes 10 tunneling parameters related to surrounding rock conditions as input features.For implementation,first,the database of TBM tunneling parameters was established,in which 10,807 tunneling cycles from the Songhua River water conveyance tunnel were accommodated.Then,the spectral clustering(SC) algorithm based on graph theory was introduced to cluster the TBM tunneling data.According to the clustering results and rock mass boreability index,the rock mass conditions were classified into four classes,and the reasonable distribution intervals of the main tunneling parameters corresponding to each class were presented.Meanwhile,based on the deep neural network(DNN),the real-time prediction model regarding different rock conditions was established.Finally,the rationality and adaptability of the proposed method were validated via analyzing the tunneling specific energy,feature importance,and training dataset size.The proposed TBM-rock mutual feedback perception method enables the automatic identification of rock mass conditions and the dynamic adjustment of tunneling parameters during TBM driving.Furthermore,in terms of the prediction performance,the method can predict the rock mass conditions ahead of the tunnel face in real time more accurately than the traditional machine learning prediction methods.展开更多
In this study,twelve machine learning(ML)techniques are used to accurately estimate the safety factor of rock slopes(SFRS).The dataset used for developing these models consists of 344 rock slopes from various open-pit...In this study,twelve machine learning(ML)techniques are used to accurately estimate the safety factor of rock slopes(SFRS).The dataset used for developing these models consists of 344 rock slopes from various open-pit mines around Iran,evenly distributed between the training(80%)and testing(20%)datasets.The models are evaluated for accuracy using Janbu's limit equilibrium method(LEM)and commercial tool GeoStudio methods.Statistical assessment metrics show that the random forest model is the most accurate in estimating the SFRS(MSE=0.0182,R2=0.8319)and shows high agreement with the results from the LEM method.The results from the long-short-term memory(LSTM)model are the least accurate(MSE=0.037,R2=0.6618)of all the models tested.However,only the null space support vector regression(NuSVR)model performs accurately compared to the practice mode by altering the value of one parameter while maintaining the other parameters constant.It is suggested that this model would be the best one to use to calculate the SFRS.A graphical user interface for the proposed models is developed to further assist in the calculation of the SFRS for engineering difficulties.In this study,we attempt to bridge the gap between modern slope stability evaluation techniques and more conventional analysis methods.展开更多
The design and practice in supporting the cut slope of an open-pit mine wereintroduced, in which the high pressure grouting method was used in reinforcing the weak formation inthe slopes. Based on a detailed geologica...The design and practice in supporting the cut slope of an open-pit mine wereintroduced, in which the high pressure grouting method was used in reinforcing the weak formation inthe slopes. Based on a detailed geological survey of the slope, a theoretical analysis was carriedout, and the design parameters were proposed, where the Tresca or Mohr-Coulomb yield criteria wasemployed. A patent technology, named 'Technology of high pressure and multiple grouting in differentlevels within a single hole', was employed in the construction. Anchor bars were also installed asgrouting proceeds. This method combines anchoring and grouting comprehensively and was foundsuccessful in practice.展开更多
文摘Assessing the stability of pillars in underground mines(especially in deep underground mines)is a critical concern during both the design and the operational phases of a project.This study mainly focuses on developing two practical models to predict pillar stability status.For this purpose,two robust models were developed using a database including 236 case histories from seven underground hard rock mines,based on gene expression programming(GEP)and decision tree-support vector machine(DT-SVM)hybrid algorithms.The performance of the developed models was evaluated based on four common statistical criteria(sensitivity,specificity,Matthews correlation coefficient,and accuracy),receiver operating characteristic(ROC)curve,and testing data sets.The results showed that the GEP and DT-SVM models performed exceptionally well in assessing pillar stability,showing a high level of accuracy.The DT-SVM model,in particular,outperformed the GEP model(accuracy of 0.914,sensitivity of 0.842,specificity of 0.929,Matthews correlation coefficient of 0.767,and area under the ROC of 0.897 for the test data set).Furthermore,upon comparing the developed models with the previous ones,it was revealed that both models can effectively determine the condition of pillar stability with low uncertainty and acceptable accuracy.This suggests that these models could serve as dependable tools for project managers,aiding in the evaluation of pillar stability during the design and operational phases of mining projects,despite the inherent challenges in this domain.
基金Project supported by the National Natural Science Foundation of China(No.51274251)
文摘The traction characteristics of the grouser, cutting the simulative soil of deepsea sediment, with different tooth widths, tooth heights, and ground pressures are studied with traction characteristic test apparatus. A traction-displacement model is obtained by combining the analysis of the cutting mechanism. The results show that the tractiondisplacement curves of grousers with different tooth widths, tooth heights, and ground pressures have the same changing trend, which matches the Wong traction model. Their sensitivity coefficient and shear modulus are slightly fluctuated. Therefore, the average values can be used as the traction model parameters. The maximum traction of the grouser with a two-side edge and a 10 mm tooth width increment changing with the tooth height and ground pressure can be determined according to the grousers with different tooth widths. By combining the traction model parameters, the traction-displacement curve of the grouser with a certain group values of tooth width, tooth height, and ground pressure can be predicted. Therefore, the slip of the mining machine can be prevented to improve the mining efficiency.
基金supported by the Ministry of Science and Higher Education, Republic of Poland (Statutory Activity of the Central Mining Institute, Grant No. 11133010)
文摘The risk of rockbursts is one of the main threats in hard coal mines. Compared to other underground mines, the number of factors contributing to the rockburst at underground coal mines is much greater.Factors such as the coal seam tendency to rockbursts, the thickness of the coal seam, and the stress level in the seam have to be considered, but also the entire coal seam-surrounding rock system has to be evaluated when trying to predict the rockbursts. However, in hard coal mines, there are stroke or stress-stroke rockbursts in which the fracture of a thick layer of sandstone plays an essential role in predicting rockbursts. The occurrence of rockbursts in coal mines is complex, and their prediction is even more difficult than in other mines. In recent years, the interest in machine learning algorithms for solving complex nonlinear problems has increased, which also applies to geosciences. This study attempts to use machine learning algorithms, i.e. neural network, decision tree, random forest, gradient boosting, and extreme gradient boosting(XGB), to assess the rockburst hazard of an active hard coal mine in the Upper Silesian Coal Basin. The rock mass bursting tendency index WTGthat describes the tendency of the seam-surrounding rock system to rockbursts and the anomaly of the vertical stress component were applied for this purpose. Especially, the decision tree and neural network models were proved to be effective in correctly distinguishing rockbursts from tremors, after which the excavation was not damaged. On average, these models correctly classified about 80% of the rockbursts in the testing datasets.
基金Project(70671039) supported by the National Natural Science Foundation of China
文摘According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are computed to determine the time delay and the embedding dimension.Due to different features of the data,data mining algorithm is conducted to classify the data into different groups.Redundant information is eliminated by the advantage of data mining technology,and the historical loads that have highly similar features with the forecasting day are searched by the system.As a result,the training data can be decreased and the computing speed can also be improved when constructing support vector machine(SVM) model.Then,SVM algorithm is used to predict power load with parameters that get in pretreatment.In order to prove the effectiveness of the new model,the calculation with data mining SVM algorithm is compared with that of single SVM and back propagation network.It can be seen that the new DSVM algorithm effectively improves the forecast accuracy by 0.75%,1.10% and 1.73% compared with SVM for two random dimensions of 11-dimension,14-dimension and BP network,respectively.This indicates that the DSVM gains perfect improvement effect in the short-term power load forecasting.
基金the Australia Coal Association Research Program(ACARP)(Grant Nos.C26006 and C26053)Supports from CSIRO。
文摘Discrimination of seismicity distributed in different areas is essential for reliable seismic risk assessment in mines.Although machine learning has been widely applied in seismic data processing,feasibility and reliability of applying this technique to classify spatially clustered seismic events in underground mines are yet to be investigated.In this research,two groups of seismic events with a minimum local magnitude(ML) of-3 were observed in an underground coal mine.They were respectively located around a dyke and the longwall face.Additionally,two types of undesired signals were also recorded.Four machine learning methods,i.e.random forest(RF),support vector machine(SVM),deep convolutional neural network(DCNN),and residual neural network(ResNN),were used for classifying these signals.The results obtained based on a primary dataset showed that these seismic events could be classified with at least 91% accuracy.The DCNN using seismogram images as the inputs reached the best performance with more than 94% accuracy.As mining is a dynamic progress which could change the characteristics of seismic signals,the temporal variance in the prediction performance of DCNN was also investigated to assess the reliability of this classifier during mining.A cascaded workflow consisting of database update,model training,signal prediction,and results review was established.By progressively calibrating the DCNN model,it achieved up to 99% prediction accuracy.The results demonstrated that machine learning is a reliable tool for the automatic discrimination of spatially clustered seismicity in underground mining.
基金conducted under the illu MINEation project, funded by the European Union’s Horizon 2020 research and innovation program under grant agreement (No. 869379)supported by the China Scholarship Council (No. 202006370006)
文摘A procedure to recognize individual discontinuities in rock mass from measurement while drilling(MWD)technology is developed,using the binary pattern of structural rock characteristics obtained from in-hole images for calibration.Data from two underground operations with different drilling technology and different rock mass characteristics are considered,which generalizes the application of the methodology to different sites and ensures the full operational integration of MWD data analysis.Two approaches are followed for site-specific structural model building:a discontinuity index(DI)built from variations in MWD parameters,and a machine learning(ML)classifier as function of the drilling parameters and their variability.The prediction ability of the models is quantitatively assessed as the rate of recognition of discontinuities observed in borehole logs.Differences between the parameters involved in the models for each site,and differences in their weights,highlight the site-dependence of the resulting models.The ML approach offers better performance than the classical DI,with recognition rates in the range 89%to 96%.However,the simpler DI still yields fairly accurate results,with recognition rates 70%to 90%.These results validate the adaptive MWD-based methodology as an engineering solution to predict rock structural condition in underground mining operations.
文摘This study was conducted to establish a Support Vector Machines(SVM)-Markov Chain prediction model for prediction of mining water inflow. According to the raw data sequence, the Support Vector Machines(SVM) model was built, and then revised by means of a Markov state change probability matrix. Through dividing the state and analyzing absolute errors and relative errors and other indexes of the measured value and the fitted value of SVM, the prediction results were improved. Finally,the model was used to calculate relative errors. Through predicting and analyzing mining water inflow, the prediction results of the model were satisfactory. The results of this study enlarge the application scope of the Support Vector Machines(SVM) prediction model and provide a new method for scientific forecasting water inflow in coal mining.
基金the National Natural Science Foundation of China (No. 50705054)
文摘Assessing machine's performance through comparing the same or similar machines is important to implement intelligent maintenance for swarm machine.In this paper,an outlier mining based abnormal machine detection algorithm is proposed for this purpose.Firstly,the outlier mining based on clustering is introduced and the definition of cluster-based global outlier factor(CBGOF) is presented.Then the modified swarm intelligence clustering(MSIC) algorithm is suggested and the outlier mining algorithm based on MSIC is proposed.The algorithm can not only cluster machines according to their performance but also detect possible abnormal machines.Finally,a comparison of mobile soccer robots' performance proves the algorithm is feasible and effective.
基金supported by the National Basic Research Program Project of China(No.2010CB732004)the National Natural Science Foundation Project of China(Nos.50934006 and41272304)+2 种基金the Graduated Students’ResearchInnovation Fund Project of Hunan Province of China(No.CX2011B119)the Scholarship Award for Excellent Doctoral Student of Ministry of Education of China and the Valuable Equipment Open Sharing Fund of Central South University(No.1343-76140000022)
文摘An approach which combines particle swarm optimization and support vector machine(PSO–SVM)is proposed to forecast large-scale goaf instability(LSGI).Firstly,influencing factors of goaf safety are analyzed,and following parameters were selected as evaluation indexes in the LSGI:uniaxial compressive strength(UCS)of rock,elastic modulus(E)of rock,rock quality designation(RQD),area ration of pillar(Sp),the ratio of width to height of the pillar(w/h),depth of ore body(H),volume of goaf(V),dip of ore body(a)and area of goaf(Sg).Then LSGI forecasting model by PSO-SVM was established according to the influencing factors.The performance of hybrid model(PSO+SVM=PSO–SVM)has been compared with the grid search method of support vector machine(GSM–SVM)model.The actual data of 40 goafs are applied to research the forecasting ability of the proposed method,and two cases of underground mine are also validated by the proposed model.The results indicated that the heuristic algorithm of PSO can speed up the SVM parameter optimization search,and the predictive ability of the PSO–SVM model with the RBF kernel function is acceptable and robust,which might hold a high potential to become a useful tool in goaf risky prediction research.
基金supported by the National Key Research and Development Program of China[grant number 2021YFE0116800]ESA-MOST China Dragon-5 Program[grant number 56796]+1 种基金the National Natural Science Foundation of China[grant number 41977415]the SIAP Project[grant number 1/SAMA/2020/2019(POCI-62-2019-01)]by AMA IP(Portuguese Administrative Modernization Agency).
文摘Remote sensing,particularly satellite-based,can play a valuable role in monitoring areas prone to geohazards.The high spatial and temporal coverage provided by satellite data can be used to reconstruct past events and continuously monitor sensitive areas for potential hazards.This paper presents a range of techniques and methods that were applied for in-depth analysis and utilization of Earth observation data,with a particular emphasis on:(1)detecting mining subsidence,where a novel approach is proposed by combining an improved U-Net model and Interferometry Synthetic Aperture Radar(InSAR)technology.The results showed that the Efficient Channel Attention(ECA)U-Net model performed better than the U-Net(baseline)model in terms of Mean Intersection over Union(MIoU)and Intersection over Union(IoU)indicators;(2)monitoring water conservancy and hydropower engineering.The Xiaolangdi multipurpose dam complex was monitored using Small BAsline Subsets(SBAS)InSAR method on Sentinel-1 time series data and four small regions with high deformation rates were identified on the slope of the reservoir bank on the north side.The dam body also showed obvious deformation with a velocity exceeding 60 mm/a;(3)the evaluation of the potential of InSAR results to integrate monitoring and warning systems for valuable heritage and architectural preservation.The overall outcome of these methods showed that the use of Artificial Intelligence(AI)techniques in combination with InSAR data leads to more efficient analysis and interpretation,resulting in improved accuracy and prompt identification of potential hazards;and(4)finally,this study also presents a method for detecting landslides in mountainous regions,using optical imagery.The new temporal landslide detection method is evaluated over a 7-year analysis period and unlike conventional bi-temporal change detection methods,this approach does not depend on any prior-knowledge and can potentially detect landslides over extended periods of time such as decades.
基金National science and technology signifi cant special(No.2024ZD1003406)Natural Science Research Project of Colleges and Universities in Anhui Province(No.2024AH050374)National Natural Science Foundation of China(Grant No.52274071).
文摘The advancement of intelligent mining in open-pit operations has imposed higher demands on geological transparency,aiming to provide a robust foundation for intelligent drilling and charging.In this study,a linear array of 120 nodal seismometers was deployed along the surfaces of the C8 and C9 platforms at Fenghuang Mountain to investigate cavities within the rock mass and prevent improper intelligent charging.The seismometers were 1 m apart along measurement lines,with a 2-m spacing between lines,and the monitoring time for each line was set at 2 h.This deployment was paired with spatial autocorrelation and station autocorrelation to analyze ambient noise seismic data and image the velocity and structure within the rock mass.The results demonstrate that the locations and sizes of cavities or loose structures can be accurately identified at the prepared excavation site.Compared with traditional geological exploration methods for openpit mines,the approach in this study off ers higher accuracy,greater efficiency,reduced labor intensity,and insensitivity to water conditions.Ambient noise seismic imaging for detecting adverse geological conditions in open-pit mines provides critical insights and references for intelligent mining advancements.
文摘Laser cladding of 316 L steel powders on pick substrate of coal mining machine was conducted, and microstructure of laser cladding coating was analyzed. The micro-hardness of laser cladding coating was examined. The results show that microstructure of laser cladding zone is exiguous dentrite, and there are hard spots dispersible distribution in the laser cladding zone. Performances of erode-resistant, surface micro-hardness and wear-resistant are improved obviously.
文摘In this paper, we conduct research on the structured data mining algorithm and applications on machine learning field. Various fields due to the advancement of informatization and digitization, a lot of multi-source and heterogeneous data distributed storage, in order to achieve the sharing, we must solve from the storage management to the interoperability of a series of mechanism, the method and implementation technology. Unstructured data does not have strict structure, therefore, compared with structured information that is more difficult to standardization, with management more difficult. According to these characteristics, the large capacity of unstructured data or using files separately store, is stored in the database index of similar pointer. Under this background, we propose the new idea on the structured data mining algorithm that is meaningful.
基金Project (41202220) supported by the National Natural Science Foundation of ChinaProject (2-9-2012-65) supported by the Fundamental Research Funds for the Central Universities, ChinaProject (20120022120003) supported by the Ph.D Program Foundation of Ministry of Education of China
文摘The failure characteristic of talus-derived rock mass continues to challenge quantitative hazard assessments in open-pit mining. Physical model test was used to assess the failure modes and mechanisms on talus-derived rock mass. The different types of failure modes of the talus-derived rock mass were introduced and a possible failure mechanism relation between the failure zone and the structure of the talus-derived rock mass was also shown. The physical model test results indicate that the rainfall has significant influence on the stability and failure modes of talus-derived rock mass during open-pit mining. The development of the seepage area caused by rainfall initiates the localized failure in that particular area, and the initiation of localized instability is mainly induced by stress changes concentrated in the seepage area.
基金financially supported by the National Key Research and Development Program of China(No.2021YFB3803101)National Natural Science Foundation of China(Nos.51974028 and 52022011)the Beijing Municipal Science and Technology Commission(No.Z191100001119125)。
文摘Alloys designed with the traditional trial and error method have encountered several problems,such as long trial cycles and high costs.The rapid development of big data and artificial intelligence provides a new path for the efficient development of metallic materials,that is,machine learning-assisted design.In this paper,the basic strategy for the machine learning-assisted rational design of alloys was introduced.Research progress in the property-oriented reversal design of alloy composition,the screening design of alloy composition based on models established using element physical and chemical features or microstructure factors,and the optimal design of alloy composition and process parameters based on iterative feedback optimization was reviewed.Results showed the great advantages of machine learning,including high efficiency and low cost.Future development trends for the machine learning-assisted rational design of alloys were also discussed.Interpretable modeling,integrated modeling,high-throughput combination,multi-objective optimization,and innovative platform building were suggested as fields of great interest.
基金fnancially supported by National Natural Science Foundation of China(Grant No.51974295).
文摘The retained coal in the end slope of an open-pit mine can be mined by the highwall mining techniques.However,the instability mechanism of the reserved rib pillar under dynamic loads of mining haul trucks and static loads of the overlying strata is not clear,which restricts the safe and efcient application of highwall mining.In this study,the load-bearing model of the rib pillar in highwall mining was established,the cusp catastrophe theory and the safety coefcient of the rib pillar were considered,and the criterion equations of the rib pillar stability were proposed.Based on the limit equilibrium theory,the limit stress of the rib pillar was analyzed,and the calculation equations of plastic zone width of the rib pillar in highwall mining were obtained.Based on the Winkler foundation beam theory,the elastic foundation beam model composed of the rib pillar and roof under the highwall mining was established,and the calculation equations for the compression of the rib pillar under dynamic and static loads were developed.The results showed that with the increase of the rib pillar width,the total compression of the rib pillar under dynamic and static loads decreases nonlinearly,and the compression of the rib pillar caused by static loads of the overlying strata and trucks has a decisive role.Numerical simulation and theoretical calculation were also performed in this study.In the numerical simulation,the coal seam with a buried depth of 122 m and a thickness of 3 m is mined by highwall mining techniques.According to the established rib pillar instability model of the highwall mining system,it is found that when the mining opening width is 3 m,the reasonable width of the rib pillar is at least 1.3 m,and the safety factor of the rib pillar is 1.3.The numerical simulation results are in good agreement with the results of theoretical calculation,which verifes the feasibility of the theoretical analysis of the rib pillar stability.This research provides a reference for the stability analysis of rib pillars under highwall mining.
文摘Given the conditions of residual coal from the boundary of a flat dipping open-pit mine,which uses strip areas mining and inner dumping with slope-covering,we propose an open-pit and underground integrated mining technology for residual coal of end slopes.In the proposal a conveyance road and ventilation conveyance near the slope are built,corresponding to the pit mining area and the surface coal mine dump,as well as an interval haulage tunnel and air-inlet tunnel.The outcome shows that such mining method may reduce the effect to slope stability from underground mining,it does not affect the dumping advance and has a high recovery rate of residual coal resources.The working face is timbered by single hydraulic props,transported by a scraper conveyor and supported by coal walls.This method of mining is one of layered top coal caving,with high resource recovery,low production cost where positive economic benefit can be realized.
基金supported by the National Natural Science Foundation of China(Grant Nos.41772309 and 51908431)the Outstanding Youth Foundation of Hubei Province,China(Grant No.2019CFA074)。
文摘Real-time perception of rock mass information is of great importance to efficient tunneling and hazard prevention in tunnel boring machines(TBMs).In this study,a TBM-rock mutual feedback perception method based on data mining(DM) is proposed,which takes 10 tunneling parameters related to surrounding rock conditions as input features.For implementation,first,the database of TBM tunneling parameters was established,in which 10,807 tunneling cycles from the Songhua River water conveyance tunnel were accommodated.Then,the spectral clustering(SC) algorithm based on graph theory was introduced to cluster the TBM tunneling data.According to the clustering results and rock mass boreability index,the rock mass conditions were classified into four classes,and the reasonable distribution intervals of the main tunneling parameters corresponding to each class were presented.Meanwhile,based on the deep neural network(DNN),the real-time prediction model regarding different rock conditions was established.Finally,the rationality and adaptability of the proposed method were validated via analyzing the tunneling specific energy,feature importance,and training dataset size.The proposed TBM-rock mutual feedback perception method enables the automatic identification of rock mass conditions and the dynamic adjustment of tunneling parameters during TBM driving.Furthermore,in terms of the prediction performance,the method can predict the rock mass conditions ahead of the tunnel face in real time more accurately than the traditional machine learning prediction methods.
基金supported via funding from Prince Satam bin Abdulaziz University project number (PSAU/2024/R/1445)The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through large Group Research Project (Grant No.RGP.2/357/44).
文摘In this study,twelve machine learning(ML)techniques are used to accurately estimate the safety factor of rock slopes(SFRS).The dataset used for developing these models consists of 344 rock slopes from various open-pit mines around Iran,evenly distributed between the training(80%)and testing(20%)datasets.The models are evaluated for accuracy using Janbu's limit equilibrium method(LEM)and commercial tool GeoStudio methods.Statistical assessment metrics show that the random forest model is the most accurate in estimating the SFRS(MSE=0.0182,R2=0.8319)and shows high agreement with the results from the LEM method.The results from the long-short-term memory(LSTM)model are the least accurate(MSE=0.037,R2=0.6618)of all the models tested.However,only the null space support vector regression(NuSVR)model performs accurately compared to the practice mode by altering the value of one parameter while maintaining the other parameters constant.It is suggested that this model would be the best one to use to calculate the SFRS.A graphical user interface for the proposed models is developed to further assist in the calculation of the SFRS for engineering difficulties.In this study,we attempt to bridge the gap between modern slope stability evaluation techniques and more conventional analysis methods.
文摘The design and practice in supporting the cut slope of an open-pit mine wereintroduced, in which the high pressure grouting method was used in reinforcing the weak formation inthe slopes. Based on a detailed geological survey of the slope, a theoretical analysis was carriedout, and the design parameters were proposed, where the Tresca or Mohr-Coulomb yield criteria wasemployed. A patent technology, named 'Technology of high pressure and multiple grouting in differentlevels within a single hole', was employed in the construction. Anchor bars were also installed asgrouting proceeds. This method combines anchoring and grouting comprehensively and was foundsuccessful in practice.
