Work injuries in mines are complex and generally characterized by several factors starting from personal to technical and technical to social characteristics.In this paper,investigation was made through the applicatio...Work injuries in mines are complex and generally characterized by several factors starting from personal to technical and technical to social characteristics.In this paper,investigation was made through the application of structural equation modeling to study the nature of relationships between the influencing/associating personal factors and work injury and their sequential relationships leading towards work injury occurrences in underground coal mines.Six variables namely,rebelliousness,negative affectivity,job boredom,job dissatisfaction and work injury were considered in this study.Instruments were developed to quantify them through a questionnaire survey.Underground mine workers were randomly selected for the survey.Responses from 300 participants were used for the analysis.The structural model of LISREL was used to estimate the interrelationships amongst the variables.The case study results show that negative affectivity and job boredom induce more job dissatisfaction to the workers whereas risk taking attitude of the individual is positively influenced by job dissatisfaction as well as by rebelliousness characteristics of the individual.Finally,risk taking and job dissatisfaction are having positive significant direct relationship with work injury.The findings of this study clearly reveal that rebelliousness,negative affectivity and job boredom are the three key personal factors influencing work related injuries in mines that need to be addressed properly through effective safety programs.展开更多
Longwall mining is one of the most acclaimed and widely used in underground method for coal extraction. The interaction of powered supports with the roof is the key issue in strata mechanics of longwall mining. Contro...Longwall mining is one of the most acclaimed and widely used in underground method for coal extraction. The interaction of powered supports with the roof is the key issue in strata mechanics of longwall mining. Controlled caving of rock mass is a prerequisite pro thriving exploitation of coal deposits by longwall retreat with caving technique and support resistance has evolved as the most promising and effective scientific tool to predict various aspects related to strata mechanics of such workings. Load density,height of caving block, distance of fractured zone ahead of the face, overhang of goaf and mechanical strength of the debris above and below the support base have been found to influence the magnitude of load on supports. Designing powered support has been attempted at the different countries in different methods. This paper reviews the mechanism of roof caving and the conventional approaches of caving behaviour and support resistance requirement in the context of major strata control experiences gained worldwide. The theoretical explanation of the mechanism of roof caving is still continuing with consistently improved understanding through growing field experiences in the larger domain of geo-mining conditions and state-of-art strata mechanics analysis and monitoring techniques.展开更多
The vibrations generated by rock blasting are a serious and hazardous outcome of these activities,causing harmful effects on the surrounding environment as well as the nearby residents.Both the local ecology and human...The vibrations generated by rock blasting are a serious and hazardous outcome of these activities,causing harmful effects on the surrounding environment as well as the nearby residents.Both the local ecology and human communities suffer from the consequences of these vibrations.Assessing the severity of blasting vibrations necessitates a thorough evaluation of Peak Particle Velocity(PPV)and frequency,which are essential parameters for measuring vibration velocity.Accurate prediction of vibration occurrence is critically important.Therefore,this study employs five machine learning models for predicting the PPV and frequency resulting from quarry blasting.This work compares five machine learning models(XGBoost,Catboost,Bagging,Gradient Boosting,and Random Forest Regression)to choose the most efficient performance model.The performance evaluation of each five machine learning models demonstrates each model achieved a performance of more than 0.90 during the testing phase,there was a strong correlation observed between the actual and the predicted ones.The analysis of performance metrics shows Catboost regression model demonstrate better performance prediction comparing with the other models with R^(2)=0.983,MSE=0.000078,RMSE=0.008,NRMSE=0.019,MAD=0.004,MAPE=35.197 in the PPV prediction,and R^(2)=0.975,MSE=0.000243,RMSE=0.015,NRMSE=0.031,MAD=0.008,MAPE=37.281 for the frequency prediction.This study will help mining engineers and blasting experts to select the best machine learning model and its hyperparameters in estimating ground vibration,and frequency.In the context of the mining and civil industry,the application of this study offers significant potential for enhancing safety protocols and optimizing operational efficiency.By employing machine learning models,this research aims to accurately predict and assess ground vibrations with frequency resulting from rock blasting.展开更多
文摘Work injuries in mines are complex and generally characterized by several factors starting from personal to technical and technical to social characteristics.In this paper,investigation was made through the application of structural equation modeling to study the nature of relationships between the influencing/associating personal factors and work injury and their sequential relationships leading towards work injury occurrences in underground coal mines.Six variables namely,rebelliousness,negative affectivity,job boredom,job dissatisfaction and work injury were considered in this study.Instruments were developed to quantify them through a questionnaire survey.Underground mine workers were randomly selected for the survey.Responses from 300 participants were used for the analysis.The structural model of LISREL was used to estimate the interrelationships amongst the variables.The case study results show that negative affectivity and job boredom induce more job dissatisfaction to the workers whereas risk taking attitude of the individual is positively influenced by job dissatisfaction as well as by rebelliousness characteristics of the individual.Finally,risk taking and job dissatisfaction are having positive significant direct relationship with work injury.The findings of this study clearly reveal that rebelliousness,negative affectivity and job boredom are the three key personal factors influencing work related injuries in mines that need to be addressed properly through effective safety programs.
文摘Longwall mining is one of the most acclaimed and widely used in underground method for coal extraction. The interaction of powered supports with the roof is the key issue in strata mechanics of longwall mining. Controlled caving of rock mass is a prerequisite pro thriving exploitation of coal deposits by longwall retreat with caving technique and support resistance has evolved as the most promising and effective scientific tool to predict various aspects related to strata mechanics of such workings. Load density,height of caving block, distance of fractured zone ahead of the face, overhang of goaf and mechanical strength of the debris above and below the support base have been found to influence the magnitude of load on supports. Designing powered support has been attempted at the different countries in different methods. This paper reviews the mechanism of roof caving and the conventional approaches of caving behaviour and support resistance requirement in the context of major strata control experiences gained worldwide. The theoretical explanation of the mechanism of roof caving is still continuing with consistently improved understanding through growing field experiences in the larger domain of geo-mining conditions and state-of-art strata mechanics analysis and monitoring techniques.
基金research fund comes from the Akita University,SPRING program
文摘The vibrations generated by rock blasting are a serious and hazardous outcome of these activities,causing harmful effects on the surrounding environment as well as the nearby residents.Both the local ecology and human communities suffer from the consequences of these vibrations.Assessing the severity of blasting vibrations necessitates a thorough evaluation of Peak Particle Velocity(PPV)and frequency,which are essential parameters for measuring vibration velocity.Accurate prediction of vibration occurrence is critically important.Therefore,this study employs five machine learning models for predicting the PPV and frequency resulting from quarry blasting.This work compares five machine learning models(XGBoost,Catboost,Bagging,Gradient Boosting,and Random Forest Regression)to choose the most efficient performance model.The performance evaluation of each five machine learning models demonstrates each model achieved a performance of more than 0.90 during the testing phase,there was a strong correlation observed between the actual and the predicted ones.The analysis of performance metrics shows Catboost regression model demonstrate better performance prediction comparing with the other models with R^(2)=0.983,MSE=0.000078,RMSE=0.008,NRMSE=0.019,MAD=0.004,MAPE=35.197 in the PPV prediction,and R^(2)=0.975,MSE=0.000243,RMSE=0.015,NRMSE=0.031,MAD=0.008,MAPE=37.281 for the frequency prediction.This study will help mining engineers and blasting experts to select the best machine learning model and its hyperparameters in estimating ground vibration,and frequency.In the context of the mining and civil industry,the application of this study offers significant potential for enhancing safety protocols and optimizing operational efficiency.By employing machine learning models,this research aims to accurately predict and assess ground vibrations with frequency resulting from rock blasting.
