In blasting operation,the aim is to achieve proper fragmentation and to avoid undesirable events such as backbreak.Therefore,predicting rock fragmentation and backbreak is very important to arrive at a technically and...In blasting operation,the aim is to achieve proper fragmentation and to avoid undesirable events such as backbreak.Therefore,predicting rock fragmentation and backbreak is very important to arrive at a technically and economically successful outcome.Since many parameters affect the blasting results in a complicated mechanism,employment of robust methods such as artificial neural network may be very useful.In this regard,this paper attends to simultaneous prediction of rock fragmentation and backbreak in the blasting operation of Tehran Cement Company limestone mines in Iran.Back propagation neural network（BPNN） and radial basis function neural network（RBFNN） are adopted for the simulation.Also,regression analysis is performed between independent and dependent variables.For the BPNN modeling,a network with architecture 6-10-2 is found to be optimum whereas for the RBFNN,architecture 636-2 with spread factor of 0.79 provides maximum prediction aptitude.Performance comparison of the developed models is fulfilled using value account for（VAF）,root mean square error（RMSE）,determination coefficient（R2） and maximum relative error（MRE）.As such,it is observed that the BPNN model is the most preferable model providing maximum accuracy and minimum error.Also,sensitivity analysis shows that inputs burden and stemming are the most effective parameters on the outputs fragmentation and backbreak,respectively.On the other hand,for both of the outputs,specific charge is the least effective parameter.展开更多
Geo-engineering problems are known for their complexity and high uncertainty levels,requiring precise defini-tions,past experiences,logical reasoning,mathematical analysis,and practical insight to address them effecti...Geo-engineering problems are known for their complexity and high uncertainty levels,requiring precise defini-tions,past experiences,logical reasoning,mathematical analysis,and practical insight to address them effectively.Soft Computing(SC)methods have gained popularity in engineering disciplines such as mining and civil engineering due to computer hardware and machine learning advancements.Unlike traditional hard computing approaches,SC models use soft values and fuzzy sets to navigate uncertain environments.This study focuses on the application of SC methods to predict backbreak,a common issue in blasting operations within mining and civil projects.Backbreak,which refers to the unintended fracturing of rock beyond the desired blast perimeter,can significantly impact project timelines and costs.This study aims to explore how SC methods can be effectively employed to anticipate and mitigate the undesirable consequences of blasting operations,specifically focusing on backbreak prediction.The research explores the complexities of backbreak prediction and highlights the potential benefits of utilizing SC methods to address this challenging issue in geo-engineering projects.展开更多
The accurate prediction of backbreak,a crucial parameter in mining operations,has a significant influence on safety and operational efficiency.The occurrence of this phenomenon is detrimental to the safety,capital and...The accurate prediction of backbreak,a crucial parameter in mining operations,has a significant influence on safety and operational efficiency.The occurrence of this phenomenon is detrimental to the safety,capital and human resources of a mine.This framework applies machine learning algorithms to predict backbreak.An enhanced precision will be explored specifically employing gradient boosting decision trees(GBDT),light gradient boosting machines(LightGBM),backpropagation neural network(BPNN),Graph Neural Networks(GNNs)and Kolmogorov-Arnold Network(KAN)algorithm.The study utilises a comprehensive dataset from the Goldfields Ghana Limited,Damang Mine comprising geomechanical,drilling,and blasting parameters(burden,spacing,stemming height,geometric stiffness,and powder factor)as well as backbreak data.The potential of each algorithm to learn the intricate relationships between the input features and backbreak values is investigated.To quantitatively assess the predictive performance of the models,the evaluation metrics,coefficient of determination(R^(2)),mean absolute error(MAE),and mean square error(MSE)are employed.The results revealed that GBDT and BPNN algorithms exhibited robust predictive capabilities,capturing the complex non-linear patterns in the dataset,achieving higher R^(2)values(97%and 92%respectively)and lower MAE scores(0.2603 and 0.456,respectively)and MSE scores(0.1456 and 0.3798,respectively).The LightGBM and KAN models also showed their predictive potential and captured the complex nonlinear patterns in the dataset but not as efficiently as GBDT and BPNN.GNN showed the least performance in backbreak prediction.The findings highlighted the potential of the GBDT model to enhance backbreak prediction accuracy,thereby aiding in safer and more efficient excavation practices.展开更多
Backbreak is one of the undesirable phenomena in open-pit mines and causes several adverse hazards,such as lanslide,rock falling off and bench instability.Backbreak is influenced by many factors,such as rock propertie...Backbreak is one of the undesirable phenomena in open-pit mines and causes several adverse hazards,such as lanslide,rock falling off and bench instability.Backbreak is influenced by many factors,such as rock properties,blasting design and local geology,so it is very difficult to assess and evaluate backbreak accurately.Therefore,controlling and accurate prediction of backbreak distance are crucial tasks to reduce hazards in open-pit mines.For this,soft computing-based techniques are considered to be an effective means,as they can integrate various sophisticated factors into a function to predict and evaluate backbreak distance.So,in this study,support vector regression(SVR)based techniques and three different types of bio-inspired meta-heuristic(BIMH)algorithms,such as chicken swarm optimization(CSO),whale optimization algorithm(WOA)and seagull optimization al gorithm(SOA),are used to develop backbreak distance prediction models.The support vector regression is used as a regression tool and BIMH algorithms are used to optimize the hyper-parameters in the support vector regression.Four different types of evaluation metrics are utilized to assess the model performance,namely co efficient of determination(R^(2)),mean square error(MSE),mean absolute error(MAE)and variance account for(VAF).An integrated evaluation system is adopted to provide overall performance for each backbreak prediction scenario.It can be indicated that CSO-SVR based backbreak prediction models can procure the best compre hensive performance and also show the best calculation efficiency.Detailed results include R^(2),VAF,MSE and MAEequal to 0.99475,0.034,99.477 and 0.1553 for a testing set and 0.97450,0.1633,97.466,and 0.1914 for a training set which can be said to be an excellent prediction result.By doing this,the hazard risk induced by backbreak can be indirectly assessed.In addition,it is also found that some superior performance can be obtained in some evaluation metrics compared with previous studies which utilized the same backbreak dataset for prediction.展开更多
文摘In blasting operation,the aim is to achieve proper fragmentation and to avoid undesirable events such as backbreak.Therefore,predicting rock fragmentation and backbreak is very important to arrive at a technically and economically successful outcome.Since many parameters affect the blasting results in a complicated mechanism,employment of robust methods such as artificial neural network may be very useful.In this regard,this paper attends to simultaneous prediction of rock fragmentation and backbreak in the blasting operation of Tehran Cement Company limestone mines in Iran.Back propagation neural network（BPNN） and radial basis function neural network（RBFNN） are adopted for the simulation.Also,regression analysis is performed between independent and dependent variables.For the BPNN modeling,a network with architecture 6-10-2 is found to be optimum whereas for the RBFNN,architecture 636-2 with spread factor of 0.79 provides maximum prediction aptitude.Performance comparison of the developed models is fulfilled using value account for（VAF）,root mean square error（RMSE）,determination coefficient（R2） and maximum relative error（MRE）.As such,it is observed that the BPNN model is the most preferable model providing maximum accuracy and minimum error.Also,sensitivity analysis shows that inputs burden and stemming are the most effective parameters on the outputs fragmentation and backbreak,respectively.On the other hand,for both of the outputs,specific charge is the least effective parameter.
文摘Geo-engineering problems are known for their complexity and high uncertainty levels,requiring precise defini-tions,past experiences,logical reasoning,mathematical analysis,and practical insight to address them effectively.Soft Computing(SC)methods have gained popularity in engineering disciplines such as mining and civil engineering due to computer hardware and machine learning advancements.Unlike traditional hard computing approaches,SC models use soft values and fuzzy sets to navigate uncertain environments.This study focuses on the application of SC methods to predict backbreak,a common issue in blasting operations within mining and civil projects.Backbreak,which refers to the unintended fracturing of rock beyond the desired blast perimeter,can significantly impact project timelines and costs.This study aims to explore how SC methods can be effectively employed to anticipate and mitigate the undesirable consequences of blasting operations,specifically focusing on backbreak prediction.The research explores the complexities of backbreak prediction and highlights the potential benefits of utilizing SC methods to address this challenging issue in geo-engineering projects.
文摘The accurate prediction of backbreak,a crucial parameter in mining operations,has a significant influence on safety and operational efficiency.The occurrence of this phenomenon is detrimental to the safety,capital and human resources of a mine.This framework applies machine learning algorithms to predict backbreak.An enhanced precision will be explored specifically employing gradient boosting decision trees(GBDT),light gradient boosting machines(LightGBM),backpropagation neural network(BPNN),Graph Neural Networks(GNNs)and Kolmogorov-Arnold Network(KAN)algorithm.The study utilises a comprehensive dataset from the Goldfields Ghana Limited,Damang Mine comprising geomechanical,drilling,and blasting parameters(burden,spacing,stemming height,geometric stiffness,and powder factor)as well as backbreak data.The potential of each algorithm to learn the intricate relationships between the input features and backbreak values is investigated.To quantitatively assess the predictive performance of the models,the evaluation metrics,coefficient of determination(R^(2)),mean absolute error(MAE),and mean square error(MSE)are employed.The results revealed that GBDT and BPNN algorithms exhibited robust predictive capabilities,capturing the complex non-linear patterns in the dataset,achieving higher R^(2)values(97%and 92%respectively)and lower MAE scores(0.2603 and 0.456,respectively)and MSE scores(0.1456 and 0.3798,respectively).The LightGBM and KAN models also showed their predictive potential and captured the complex nonlinear patterns in the dataset but not as efficiently as GBDT and BPNN.GNN showed the least performance in backbreak prediction.The findings highlighted the potential of the GBDT model to enhance backbreak prediction accuracy,thereby aiding in safer and more efficient excavation practices.
基金the State Key Laboratory of Precision Blasting and Hubei Key Laboratory of Blasting Engineering,Jianghan University in China(No.PBSKL2023A12)the Distinguished Youth Science Foundation of Hunan Province of China(No.2022JJ10073)The first author is supported by China Scholarship Council(No.202006370006).
文摘Backbreak is one of the undesirable phenomena in open-pit mines and causes several adverse hazards,such as lanslide,rock falling off and bench instability.Backbreak is influenced by many factors,such as rock properties,blasting design and local geology,so it is very difficult to assess and evaluate backbreak accurately.Therefore,controlling and accurate prediction of backbreak distance are crucial tasks to reduce hazards in open-pit mines.For this,soft computing-based techniques are considered to be an effective means,as they can integrate various sophisticated factors into a function to predict and evaluate backbreak distance.So,in this study,support vector regression(SVR)based techniques and three different types of bio-inspired meta-heuristic(BIMH)algorithms,such as chicken swarm optimization(CSO),whale optimization algorithm(WOA)and seagull optimization al gorithm(SOA),are used to develop backbreak distance prediction models.The support vector regression is used as a regression tool and BIMH algorithms are used to optimize the hyper-parameters in the support vector regression.Four different types of evaluation metrics are utilized to assess the model performance,namely co efficient of determination(R^(2)),mean square error(MSE),mean absolute error(MAE)and variance account for(VAF).An integrated evaluation system is adopted to provide overall performance for each backbreak prediction scenario.It can be indicated that CSO-SVR based backbreak prediction models can procure the best compre hensive performance and also show the best calculation efficiency.Detailed results include R^(2),VAF,MSE and MAEequal to 0.99475,0.034,99.477 and 0.1553 for a testing set and 0.97450,0.1633,97.466,and 0.1914 for a training set which can be said to be an excellent prediction result.By doing this,the hazard risk induced by backbreak can be indirectly assessed.In addition,it is also found that some superior performance can be obtained in some evaluation metrics compared with previous studies which utilized the same backbreak dataset for prediction.
