Rockbursts, which mainly affect mining roadways, are dynamic disasters arising from the surrounding rock under high stress. Understanding the interaction between supports and the surrounding rock is necessary for effe...Rockbursts, which mainly affect mining roadways, are dynamic disasters arising from the surrounding rock under high stress. Understanding the interaction between supports and the surrounding rock is necessary for effective rockburst control. In this study, the squeezing behavior of the surrounding rock is analyzed in rockburst roadways, and a mechanical model of rockbursts is established considering the dynamic support stress, thus deriving formulas and providing characteristic curves for describing the interaction between the support and surrounding rock. Design principles and parameters of supports for rockburst control are proposed. The results show that only when the geostress magnitude exceeds a critical value can it drive the formation of rockburst conditions. The main factors influencing the convergence response and rockburst occurrence around roadways are geostress, rock brittleness, uniaxial compressive strength, and roadway excavation size. Roadway support devices can play a role in controlling rockburst by suppressing the squeezing evolution of the surrounding rock towards instability points of rockburst. Further, the higher the strength and the longer the impact stroke of support devices with constant resistance, the more easily multiple balance points can be formed with the surrounding rock to control rockburst occurrence. Supports with long impact stroke allow adaptation to varying geostress levels around the roadway, aiding in rockburst control. The results offer a quantitative method for designing support systems for rockburst-prone roadways. The design criterion of supports is determined by the intersection between the convergence curve of the surrounding rock and the squeezing deformation curve of the support devices.展开更多
To evaluate the accuracy of rockburst tendency classification in coal-bearing sandstone strata,this study conducted uniaxial compression loading and unloading tests on sandstone samples with four distinct grain sizes....To evaluate the accuracy of rockburst tendency classification in coal-bearing sandstone strata,this study conducted uniaxial compression loading and unloading tests on sandstone samples with four distinct grain sizes.The tests involved loading the samples to 60%,70%,and 80%of their uniaxial compressive strength,followed by unloading and reloading until failure.Key parameters such as the elastic energy index and linear elasticity criteria were derived from these tests.Additionally,rock fragments were collected to calculate their initial ejection kinetic energy,serving as a measure of rockburst tendency.The classification of rockburst tendency was conducted using grading methods based on burst energy index(WET),pre-peak stored elastic energy(PES)and experimental observations.Multi-class classification and regression analyses were applied to machine learning models using experimental data to predict rockburst tendency levels.A comparative analysis of models from two libraries revealed that the Random Forest model achieved the highest accuracy in classification,while the Ada Boost Regressor model excelled in regression predictions.This study highlights that on a laboratory scale,integrating ejection kinetic energy with the unloading ratio,failure load,W_(ET)and PES through machine learning offers a highly accurate and reliable approach for determining rockburst tendency levels.展开更多
The scientific community recognizes the seriousness of rockbursts and the need for effective mitigation measures.The literature reports various successful applications of machine learning(ML)models for rockburst asses...The scientific community recognizes the seriousness of rockbursts and the need for effective mitigation measures.The literature reports various successful applications of machine learning(ML)models for rockburst assessment;however,a significant question remains unanswered:How reliable are these models,and at what confidence level are classifications made?Typically,ML models output single rockburst grade even in the face of intricate and out-of-distribution samples,without any associated confidence value.Given the susceptibility of ML models to errors,it becomes imperative to quantify their uncertainty to prevent consequential failures.To address this issue,we propose a conformal prediction(CP)framework built on traditional ML models(extreme gradient boosting and random forest)to generate valid classifications of rockburst while producing a measure of confidence for its output.The proposed framework guarantees marginal coverage and,in most cases,conditional coverage on the test dataset.The CP was evaluated on a rockburst case in the Sanshandao Gold Mine in China,where it achieved high coverage and efficiency at applicable confidence levels.Significantly,the CP identified several“confident”classifications from the traditional ML model as unreliable,necessitating expert verification for informed decision-making.The proposed framework improves the reliability and accuracy of rockburst assessments,with the potential to bolster user confidence.展开更多
With resource exploitation and engineering construction gradually going deeper,the surrounding rock dynamic disaster becomes frequent and violent.The anchorage support is a common control method of surrounding rock in...With resource exploitation and engineering construction gradually going deeper,the surrounding rock dynamic disaster becomes frequent and violent.The anchorage support is a common control method of surrounding rock in underground engineering.To study the dynamic damage characteristics of anchored rock and the energy absorption control mechanism of dynamic disasters,a new type of constant resistance and energy absorption(CREA)material with high strength,high elongation and high energy absorption characteristics is developed.A contrast test of rockbursts in anchored rock with different support materials is conducted.The test results show that the surface damage rates and energy release degree of anchored rock with common bolt(CB)and CREA are lower than those of unanchored rock,respectively.The total energy,average energy and maximum energy released by CREA anchored rock are 30.9%,94.3%and 84.4%lower than those of CB anchored rock.Compared with unanchored rock,the rockburst peak stress in the CREA anchored rock is increased by 39.9%,and the rockburst time is delayed by 53.2%.Based on the rockburst energy calculation model,the evolution law of rockburst peak stress and energy release is investigated.The control mechanism of CREA support units on rock dynamic failure is clarified.展开更多
A new rockburst classification, innovative works in developing a ‘‘strainburst test machine" and an‘‘impact-induced rockburst test machine" that can reproduce rockbursts in laboratory were researched.New...A new rockburst classification, innovative works in developing a ‘‘strainburst test machine" and an‘‘impact-induced rockburst test machine" that can reproduce rockbursts in laboratory were researched.New concepts were proposed regarding the stress paths that take into account both the static and dynamic stresses analogous to that at excavation boundaries for generating artificially-induced strainburst and impact-induced rockburst. As an important method for rockburst control, a novel energyabsorbing bolt was developed, which has a constant-resistance under both static and impact loadings and a large-elongation capacity for containing large deformations of rock masses under burst-prone conditions.展开更多
Rockburst represents a very dangerous phenomenon in deep underground mining in unfavourable conditions such as great depth, high horizontal stress, proximity of important tectonic structures, and unmined pillars. The ...Rockburst represents a very dangerous phenomenon in deep underground mining in unfavourable conditions such as great depth, high horizontal stress, proximity of important tectonic structures, and unmined pillars. The case study describes a recorded heavy rockburst in the Czech part of the Upper Silesian Coal Basin, which occurred during longwall mining near the protective pillar. The artificial dividing of geological blocks and creation of mining protective pillars(shaft pillars, crosscut pillars etc.) is a dangerous task in light of rockbursts occurring mainly due to overstressing of remaining pillars. A simple model of this situation is presented. Natural and mining conditions are analysed and presented in detail as well as registered seismicity during longwall mining in the area. Recorded rockbursts in the area of interest are described and their causes discussed. Many rockbursts near protective pillars were recorded in this mining region. Methodical instructions for rockburst prevention in proximity of protective pillars as well as for gates driving were devised based on the evaluation of rockburst causes. The paper presents these principles for prevention.展开更多
One of the most serious mining disasters in underground mines is rockburst phenomena.They can lead to injuries and even fatalities as well as damage to underground openings and mining equipment.This has forced many re...One of the most serious mining disasters in underground mines is rockburst phenomena.They can lead to injuries and even fatalities as well as damage to underground openings and mining equipment.This has forced many researchers to investigate alternative methods to predict the potential for rockburst occurrence.However,due to the highly complex relation between geological,mechanical and geometric parameters of the mining environment,the traditional mechanics-based prediction methods do not always yield precise results.With the emergence of machine learning methods,a breakthrough in the prediction of rockburst occurrence has become possible in recent years.This paper presents a state-ofthe-art review of various applications of machine learning methods for the prediction of rockburst potential.First,existing rockburst prediction methods are introduced,and the limitations of such methods are highlighted.A brief overview of typical machine learning methods and their main features as predictive tools is then presented.The current applications of machine learning models in rockburst prediction are surveyed,with related mechanisms,technical details and performance analysis.展开更多
基金funded by the National Natural Science Foundation of China (No. 52304133)the National Key R&D Program of China (No. 2022YFC3004605)the Department of Science and Technology of Liaoning Province (No. 2023-BS-083)。
文摘Rockbursts, which mainly affect mining roadways, are dynamic disasters arising from the surrounding rock under high stress. Understanding the interaction between supports and the surrounding rock is necessary for effective rockburst control. In this study, the squeezing behavior of the surrounding rock is analyzed in rockburst roadways, and a mechanical model of rockbursts is established considering the dynamic support stress, thus deriving formulas and providing characteristic curves for describing the interaction between the support and surrounding rock. Design principles and parameters of supports for rockburst control are proposed. The results show that only when the geostress magnitude exceeds a critical value can it drive the formation of rockburst conditions. The main factors influencing the convergence response and rockburst occurrence around roadways are geostress, rock brittleness, uniaxial compressive strength, and roadway excavation size. Roadway support devices can play a role in controlling rockburst by suppressing the squeezing evolution of the surrounding rock towards instability points of rockburst. Further, the higher the strength and the longer the impact stroke of support devices with constant resistance, the more easily multiple balance points can be formed with the surrounding rock to control rockburst occurrence. Supports with long impact stroke allow adaptation to varying geostress levels around the roadway, aiding in rockburst control. The results offer a quantitative method for designing support systems for rockburst-prone roadways. The design criterion of supports is determined by the intersection between the convergence curve of the surrounding rock and the squeezing deformation curve of the support devices.
基金financial support for this work provided by the National Natural Science Foundation of China (No.52227901)。
文摘To evaluate the accuracy of rockburst tendency classification in coal-bearing sandstone strata,this study conducted uniaxial compression loading and unloading tests on sandstone samples with four distinct grain sizes.The tests involved loading the samples to 60%,70%,and 80%of their uniaxial compressive strength,followed by unloading and reloading until failure.Key parameters such as the elastic energy index and linear elasticity criteria were derived from these tests.Additionally,rock fragments were collected to calculate their initial ejection kinetic energy,serving as a measure of rockburst tendency.The classification of rockburst tendency was conducted using grading methods based on burst energy index(WET),pre-peak stored elastic energy(PES)and experimental observations.Multi-class classification and regression analyses were applied to machine learning models using experimental data to predict rockburst tendency levels.A comparative analysis of models from two libraries revealed that the Random Forest model achieved the highest accuracy in classification,while the Ada Boost Regressor model excelled in regression predictions.This study highlights that on a laboratory scale,integrating ejection kinetic energy with the unloading ratio,failure load,W_(ET)and PES through machine learning offers a highly accurate and reliable approach for determining rockburst tendency levels.
文摘The scientific community recognizes the seriousness of rockbursts and the need for effective mitigation measures.The literature reports various successful applications of machine learning(ML)models for rockburst assessment;however,a significant question remains unanswered:How reliable are these models,and at what confidence level are classifications made?Typically,ML models output single rockburst grade even in the face of intricate and out-of-distribution samples,without any associated confidence value.Given the susceptibility of ML models to errors,it becomes imperative to quantify their uncertainty to prevent consequential failures.To address this issue,we propose a conformal prediction(CP)framework built on traditional ML models(extreme gradient boosting and random forest)to generate valid classifications of rockburst while producing a measure of confidence for its output.The proposed framework guarantees marginal coverage and,in most cases,conditional coverage on the test dataset.The CP was evaluated on a rockburst case in the Sanshandao Gold Mine in China,where it achieved high coverage and efficiency at applicable confidence levels.Significantly,the CP identified several“confident”classifications from the traditional ML model as unreliable,necessitating expert verification for informed decision-making.The proposed framework improves the reliability and accuracy of rockburst assessments,with the potential to bolster user confidence.
基金supported by the National Key Research and Development Program of China(No.2023YFC2907600)the National Natural Science Foundation of China(Nos.42477166 and 42277174)+2 种基金the Fundamental Research Funds for the Central Universities,China(No.2024JCCXSB01)the Opening Project of State Key Laboratory of Explosion Science and Safety Protection,Beijing Institute of Technology(No.KFJJ24-01M)the Open Foundation of Collaborative Innovation Center of Green Development and Ecological Restoration of Mineral Resources(No.HLCX2024-04)。
文摘With resource exploitation and engineering construction gradually going deeper,the surrounding rock dynamic disaster becomes frequent and violent.The anchorage support is a common control method of surrounding rock in underground engineering.To study the dynamic damage characteristics of anchored rock and the energy absorption control mechanism of dynamic disasters,a new type of constant resistance and energy absorption(CREA)material with high strength,high elongation and high energy absorption characteristics is developed.A contrast test of rockbursts in anchored rock with different support materials is conducted.The test results show that the surface damage rates and energy release degree of anchored rock with common bolt(CB)and CREA are lower than those of unanchored rock,respectively.The total energy,average energy and maximum energy released by CREA anchored rock are 30.9%,94.3%and 84.4%lower than those of CB anchored rock.Compared with unanchored rock,the rockburst peak stress in the CREA anchored rock is increased by 39.9%,and the rockburst time is delayed by 53.2%.Based on the rockburst energy calculation model,the evolution law of rockburst peak stress and energy release is investigated.The control mechanism of CREA support units on rock dynamic failure is clarified.
基金Financial support from the National Key Research and Development Program (No.2016YFC0600901)the National Natural Science Foundation of China (No.51704298)
文摘A new rockburst classification, innovative works in developing a ‘‘strainburst test machine" and an‘‘impact-induced rockburst test machine" that can reproduce rockbursts in laboratory were researched.New concepts were proposed regarding the stress paths that take into account both the static and dynamic stresses analogous to that at excavation boundaries for generating artificially-induced strainburst and impact-induced rockburst. As an important method for rockburst control, a novel energyabsorbing bolt was developed, which has a constant-resistance under both static and impact loadings and a large-elongation capacity for containing large deformations of rock masses under burst-prone conditions.
基金the project of the Institute of Clean Technologies for Mining and Utilisation of Raw Materials for Energy Use–Sustainability Programme of Czech Republic (No.LO1406)supported by a project for the long-term conceptual development of research organisations (No.RVO:68145535)
文摘Rockburst represents a very dangerous phenomenon in deep underground mining in unfavourable conditions such as great depth, high horizontal stress, proximity of important tectonic structures, and unmined pillars. The case study describes a recorded heavy rockburst in the Czech part of the Upper Silesian Coal Basin, which occurred during longwall mining near the protective pillar. The artificial dividing of geological blocks and creation of mining protective pillars(shaft pillars, crosscut pillars etc.) is a dangerous task in light of rockbursts occurring mainly due to overstressing of remaining pillars. A simple model of this situation is presented. Natural and mining conditions are analysed and presented in detail as well as registered seismicity during longwall mining in the area. Recorded rockbursts in the area of interest are described and their causes discussed. Many rockbursts near protective pillars were recorded in this mining region. Methodical instructions for rockburst prevention in proximity of protective pillars as well as for gates driving were devised based on the evaluation of rockburst causes. The paper presents these principles for prevention.
文摘One of the most serious mining disasters in underground mines is rockburst phenomena.They can lead to injuries and even fatalities as well as damage to underground openings and mining equipment.This has forced many researchers to investigate alternative methods to predict the potential for rockburst occurrence.However,due to the highly complex relation between geological,mechanical and geometric parameters of the mining environment,the traditional mechanics-based prediction methods do not always yield precise results.With the emergence of machine learning methods,a breakthrough in the prediction of rockburst occurrence has become possible in recent years.This paper presents a state-ofthe-art review of various applications of machine learning methods for the prediction of rockburst potential.First,existing rockburst prediction methods are introduced,and the limitations of such methods are highlighted.A brief overview of typical machine learning methods and their main features as predictive tools is then presented.The current applications of machine learning models in rockburst prediction are surveyed,with related mechanisms,technical details and performance analysis.
