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International Journal of Mining Science and Technology 2021年第5期31卷 799-812页

作者： Jian Zhou Chao Chen Mingzheng Wang manoj khandelwal School of Resources and Safety Engineering Central South UniversityChangsha 410083China Bharti School of Engineering Laurentian UniversitySudburyOntarioCanada School of Engineering Information Technology and Physical SciencesFederation University AustraliaBallaratAustralia

Coal burst is a severe hazard that can result in fatalities and damage of facilities in underground coal *** address this issue,a robust unascertained combination model is proposed to study the coal burst hazard based on an updated *** assessment indexes are used in the model,which are the dynamic failure duration(DT),elastic energy index(WET),impact energy index(KE)and uniaxial compressive strength(RC).Four membership functions,including linear(L),parabolic(P),S and Weibull(W)functions,are proposed to measure the uncertainty level of individual *** corresponding weights are determined through information entropy(EN),analysis hierarchy process(AHP)and synthetic weights(CW).Simultaneously,the classification criteria,including unascertained cluster(UC)and credible identification principle(CIP),are *** combination algorithm,consisting of P function,CW and CIP(P-CW-CIP),is selected as the optimal classification model in function of theory analysis and to train the ***,the established ensemble model is further validated through test samples with 100%*** results reveal that the hybrid model has a great potential in the coal burst hazard evaluation in underground coal mines.

关键词： Coal burst liability(CBL) Unascertained measurement(UM) Analysis hierarchy process Information entropy Synthetic weights Comprehensive evaluation

A true triaxial strength criterion for rocks by gene expression programming

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Journal of Rock Mechanics and Geotechnical Engineering 2023年第10期15卷 2508-2520页

作者： Jian Zhou Rui Zhang Yingui Qiu manoj khandelwal School of Resources and Safety Engineering Central South UniversityChangsha410083China Institute of Innovation Science and SustainabilityFederation University AustraliaBallaratVIC3350Australia

Rock strength is a crucial factor to consider when designing and constructing underground *** study utilizes a gene expression programming(GEP)algorithm-based model to predict the true triaxial strength of rocks,taking into account the influence of rock genesis on their mechanical behavior during the model building process.A true triaxial strength criterion based on the GEP model for igneous,metamorphic and magmatic rocks was obtained by training the model using collected *** to the modified Weibols-Cook criterion,the modified Mohr-Coulomb criterion,and the modified Lade criterion,the strength criterion based on the GEP model exhibits superior prediction accuracy *** strength criterion based on the GEP model has better performance in R2,RMSE and MAPE for the data set used in this ***,the strength criterion based on the GEP model shows greater stability in predicting the true triaxial strength of rocks across different *** to the existing strength criterion based on the genetic programming(GP)model,the proposed criterion based on GEP model achieves more accurate predictions of the variation of true triaxial strength(s1)with intermediate principal stress(s2).Finally,based on the Sobol sensitivity analysis technique,the effects of the parameters of the three obtained strength criteria on the true triaxial strength of the rock are *** general,the proposed strength criterion exhibits superior performance in terms of both accuracy and stability of prediction results.

关键词： Gene expression programming(GEP) True triaxial strength Rock failure criteria Intermediate principal stress

Prediction of flyrock in open pit blasting operation using machine learning method

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International Journal of Mining Science and Technology 2013年第3期23卷 313-316页

作者： manoj khandelwal M. Monjezi Department of Mining Engineering College of Technology & Engineering Maharana Pratap University of Agriculture and Technology Faculty of Engineering Tarbiat Modares University

Flyrock is one of the most hazardous events in blasting operation of surface mines. There are several empirical methods to predict flyrock. Low performance of such models is due to the complexity of flyrock analysis. Existence of various effective parameters and their unknown relationships are the main reasons for inaccuracy of the empirical models. Presently, the application of new approaches such as artificial intelligence is highly recommended. In this paper, an attempt has been made to predict flyrock in blasting operations of Soungun Copper Mine, Iran incorporating rock properties and blast design parameters using support vector machine (SVM) method. To investigate the suitability of this approach, the predictions by SVM have been compared with multivariate regression analysis (MVRA), too. Coefficient of determination (CoD) and mean absolute error (MAE) were taken as performance measures. It was found that CoD between measured and predicted flyrock was 0.948 and 0.440 by SVM and MVRA, respectively, whereas MAE between measured and predicted flyrock was 3.11 and 7.74 by SVM and MVRA, respectively.

关键词： Blasting Soungun Copper Mine Flyrock Support vector machine MVRA

Enhancing diamond drilling performance by the addition of non-ionic polymer to the flushing media

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Mining Science and Technology 2010年第3期20卷 400-405页

作者： BHATNAGAR Anupam khandelwal manoj RAO Karanam Uma Maheshwar Department of Mining Engineering College of Technology & EngineeringMaharana Pratap University of Agrl & TechnologyUdaipur 313001India Department of Mining Engineering Indian Institute of TechnologyKharagpur 721302India

Drilling is a most important and crucial operation in the excavation *** the objective of looking into the enhancement of diamond drilling performance detailed laboratory investigations were carried out on phosphate *** effect of Poly（Ethylene Oxide）（PEO） added to the drilling water was studied by varying machine parameters and PEO *** responses were rate of penetration and torque at the bit rock *** durability tests were also performed to understand the slaking behavior of phosphate rock in PEO solutions.

关键词： rotary diamond drilling flushing media polyethylene oxide rate of penetration torque zero surface charge drilling

Stability prediction of underground entry-type excavations based on particle swarm optimization and gradient boosting decision tree

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Underground Space 2023年第2期9卷 234-249页

作者： Jian Zhou Shuai Huang Ming Tao manoj khandelwal Yong Dai Mingsheng Zhao School of Resources and Safety Engineering Central South UniversityChangsha 410083China Institute of Innovation Science and SustainabilityFederation University AustraliaBallaratVIC 3350Australia Poly Xinlian Blasting Engineer Limited Corp GuiyangGuizhou 550002China Re

The stability of underground entry-type excavations will directly affect the working environment and the safety of *** critical span graphs and traditional statistics learning methods can not meet the requirements of high accuracy for stability assessment of entry-type ***,this study proposes a new prediction method based on machine learning to scientifically adjust the critical span ***,the particle swarm optimization(PSO)algorithm is used to optimize the core parameters of the gradient boosting decision tree(GBDT),abbreviated as ***,the classification performance of eight other classifiers including GDBT,k-nearest neighbors(KNN),two kinds of support vector machines(SVM),Gaussian naive Bayes(GNB),logistic regression(LR)and linear discriminant analysis(LDA)are also applied to compare with the proposed *** revealed that compared with the other eight models,the prediction performance of PSO-GBDT is undoubtedly the most reliable,and its classification accuracy is up to ***,this model has great potential to provide a more scientific and accurate choice for the stability prediction of underground *** addition,each classification model is used to predict the stability category of several grid points divided by the critical span graph,and the updated critical span graph of each model is discussed in combination with previous *** results show that the PSO-GBDT model has the advantages of being scientific,accurate and efficient in updating the critical span graph,and its output decision boundary has strict theoretical support,which can help mine operators make favorable economic decisions.

关键词： Stability Entry-type excavations Critical span graph Gradient boosting decision tree Particle swarm optimization

An ANN-based approach to predict blast-induced ground vibration of Gol-E-Gohar iron ore mine,Iran

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Journal of Rock Mechanics and Geotechnical Engineering 2014年第1期6卷 67-76页

作者： Mahdi Saadat manoj khandelwal M.Monjezi Department of Mining Engineering Shahid Bahonar University of Kerman Department of Mining Engineering College of Technology&EngineeringMaharana Pratap University of Agriculture&Technology Faculty of Engineering Tarbiat Modares University

Blast-induced ground vibration is one of the inevitable outcomes of blasting in mining projects and may cause substantial damage to rock mass as well as nearby structures and human *** this paper,an attempt has been made to present an application of artificial neural network(ANN)to predict the blast-induced ground vibration of the Gol-E-Gohar(GEG)iron mine,Iran.A four-layer feed-forward back propagation multi-layer perceptron(MLP)was used and trained with Levenberg–Marquardt *** construct ANN models,the maximum charge per delay,distance from blasting face to monitoring point,stemming and hole depth were taken as inputs,whereas peak particle velocity(PPV)was considered as an output parameter.A database consisting of69data sets recorded at strategic and vulnerable locations of GEG iron mine was used to train and test the generalization capability of ANN *** of determination(R2)and mean square error(MSE)were chosen as the indicators of the performance of the networks.A network with architecture4-11-5-1and R2of0.957and MSE of0.000722was found to be *** demonstrate the supremacy of ANN approach,the same69data sets were used for the prediction of PPV with four common empirical models as well as multiple linear regression(MLR)*** results revealed that the proposed ANN approach performs better than empirical and MLR models.

关键词： Blast-induced ground vibration Empirical predictors Artificial neural network(ANN) Multiple linear regression

An evolutionary adaptive neuro-fuzzy inference system for estimating field penetration index of tunnel boring machine in rock mass

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Journal of Rock Mechanics and Geotechnical Engineering 2021年第6期13卷 1290-1299页

作者： Maryam Parsajoo Ahmed Salih Mohammed Saffet Yagiz Danial Jahed Armaghani manoj khandelwal Department of Mining Engineering Imam Khomeini International UniversityQazvin34149-16818Iran Civil Engineering Department College of EngineeringUniversity of SulaimaniKurdistan Region46001Iraq School of Mining and Geosciences Nazarbayev UniversityNur-Sultan010000Kazakhstan Department of Urban Planning Engineering Networks and SystemsInstitute of Architecture and ConstructionSouth Ural State UniversityChelyabinsk454080Russia School of Engineering Information Technology and Physical SciencesFederation University AustraliaBallarat3350Australia

Field penetration index(FPI) is one of the representative key parameters to examine the tunnel boring machine(TBM) *** of accurate FPI prediction can be responsible for numerous disastrous incidents associated with rock mechanics and *** study aims to predict TBM performance(***) by an efficient and improved adaptive neuro-fuzzy inference system(ANFIS) *** was done using an evolutionary algorithm,*** bee colony(ABC) algorithm mixed with the ANFIS *** role of ABC algorithm in this system is to find the optimum membership functions(MFs) of ANFIS model to achieve a higher degree of *** procedure and modeling were conducted on a tunnelling database comprising of more than 150 data samples where brittleness index(BI),fracture spacing,α angle between the plane of weakness and the TBM driven direction,and field single cutter load were assigned as model inputs to approximate FPI *** to the results obtained by performance indices,the proposed ANFISABC model was able to receive the highest accuracy level in predicting FPI values compared with ANFIS *** terms of coefficient of determination(R^(2)),the values of 0.951 and 0.901 were obtained for training and testing stages of the proposed ANFISABC model,respectively,which confirm its power and capability in solving TBM performance *** proposed model can be used in the other areas of rock mechanics and underground space technologies with similar conditions.

关键词： Tunnel boring machine(TBM) Field penetration index(FPI) Neuro-fuzzy technique Evolutionary computation Artificial bee colony(ABC)

Prediction of blasting mean fragment size using support vector regression combined with five optimization algorithms

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Journal of Rock Mechanics and Geotechnical Engineering 2021年第6期13卷 1380-1397页

作者： Enming Li Fenghao Yang Meiheng Ren Xiliang Zhang Jian Zhou manoj khandelwal School of Resources and Safety Engineering Central South UniversityChangsha410083China Universidad Politecnica de Madrid-ETSI Minas y Energia Rios Rosas 21Madrid28003Spain College of Locomotive and Rolling Stock Engineering Dalian Jiaotong UniversityDalian116028China Department of Mathematics University of California Santa BarbaraSanta BarbaraCA93106USA State Key Laboratory of Safety and Health for Metal Mines Maanshan243000China School of Engineering Information Technology and Physical SciencesFederation University AustraliaBallaratVIC3350Australia

The main purpose of blasting operation is to produce desired and optimum mean size rock *** or fine fragments cause the loss of ore during loading and transportation,whereas large or coarser fragments need to be further processed,which enhances production ***,accurate prediction of rock fragmentation is crucial in blasting *** fragment size(MFS) is a crucial index that measures the goodness of blasting *** the past decades,various models have been proposed to evaluate and predict blasting *** these models,artificial intelligence(AI)-based models are becoming more popular due to their outstanding prediction results for multiinfluential *** this study,support vector regression(SVR) techniques are adopted as the basic prediction tools,and five types of optimization algorithms,*** search(GS),grey wolf optimization(GWO),particle swarm optimization(PSO),genetic algorithm(GA) and salp swarm algorithm(SSA),are implemented to improve the prediction performance and optimize the *** prediction model involves 19 influential factors that constitute a comprehensive blasting MFS evaluation system based on AI *** all the models,the GWO-v-SVR-based model shows the best comprehensive performance in predicting MFS in blasting *** types of mathematical indices,*** square error(MSE),coefficient of determination(R^(2)) and variance accounted for(VAF),are utilized for evaluating the performance of different prediction *** R^(2),MSE and VAF values for the training set are 0.8355,0.00138 and 80.98,respectively,whereas 0.8353,0.00348 and 82.41,respectively for the testing ***,sensitivity analysis is performed to understand the influence of input parameters on *** shows that the most sensitive factor in blasting MFS is the uniaxial compressive strength.

关键词： Blasting mean fragment size e-support vector regression(e-SVR) V-support vector regression(v-SVR) Meta-heuristic algorithms Intelligent prediction

Prediction of rock fragmentation in a fiery seam of an open-pit coal mine in India

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Journal of Rock Mechanics and Geotechnical Engineering 2024年第8期16卷 2879-2893页

作者： Mukul Sharma Bhanwar Singh Choudhary Autar K.Raina manoj khandelwal Saurav Rukhiyar Department of Mining Engineering Indian Institute of Technology(ISM)Dhanbad826004India CSIR-Central Institute of Mining and Fuel Research Nagpur Research Center(Mining Technology)Nagpur440006India Institute of Innovation Science and SustainabilityFederation University AustraliaBallaratVIC3350Australia

Spontaneous combustion of coal increases the temperature in adjoining overburden strata of coal seams and poses a challenge when loading *** condition,known as hot-hole blasting,is dangerous due to the increased possibility of premature explosions in loaded ***,it is crucial to load the blastholes with an appropriate amount of explosives within a short period to avoid premature detonation caused by high temperatures of ***,it will help achieve the desired fragment *** study tried to ascertain the most influencial variables of mean fragment size and their optimum values adopted for blasting in a fiery *** on blast design,rock mass,and fragmentation of 100 blasts in fiery seams of a coal mine were collected and used to develop mean fragmentation prediction models using soft computational *** coefficient of determination(R^(2)),root mean square error(RMSE),mean absolute error(MAE),mean square error(MSE),variance account for(VAF)and coefficient of efficiency in percentage(CE)were calculated to validate the *** indicates that the random forest algorithm(RFA)outperforms the artificial neural network(ANN),response surface method(RSM),and decision tree(DT).The values of R^(2),RMSE,MAE,MSE,VAF,and CE for RFA are 0.94,0.034,0.027,0.001,93.58,and 93.01,*** parametric sensitivity analyses(MPSAs)of the input variables showed that the Schmidt hammer rebound number and spacing-to-burden ratio are the most influencial variables for the blast fragment *** analysis was finally used to define the best blast design variables to achieve optimum fragment size from *** optimum factor values for RFA of S/B,ld/B and ls/ld are 1.03,1.85 and 0.7,respectively.

关键词： Fiery seam Rock fragmentation Response Surface Method(RSM) Artificial Neural Network(ANN) Random Forest Algorithm(RFA) Multiple Parametric Sensitivity Analysis (MPSA)

Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization

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Underground Space 2021年第5期6卷 506-515页

作者： Jian Zhou Yingui Qiu Shuangli Zhu Danial Jahed Armaghani manoj khandelwal Edy Tonnizam Mohamad School of Resources and Safety Engineering Central South UniversityChangsha 410083China Department of Civil Engineering Faculty of EngineeringUniversity of Malaya50603 Kuala LumpurMalaysia School of Engineering Information Technology and Physical SciencesFederation University AustraliaBallaratAustralia Centre of Tropical Geoengineering(GEOTROPIK) School of Civil EngineeringFaculty of EngineeringUniversiti Teknologi Malaysia81310 Johor BahruMalaysia

The advance rate(AR)of a tunnel boring machine(TBM)under hard rock conditions is a key parameter in the successful implementation of tunneling *** this study,we improved the accuracy of prediction models by employing a hybrid model of extreme gradient boosting(XGBoost)with Bayesian optimization(BO)to model the TBM *** develop the proposed models,1286 sets of data were collected from the Peng Selangor Raw Water Transfer tunnel project in *** database consists of rock mass and intact rock features,including rock mass rating,rock quality designation,weathered zone,uniaxial compressive strength,and Brazilian tensile *** specifications,including revolution per minute and thrust force,were considered to predict the TBM *** accuracies of the predictive models were examined using the root mean squares error(RMSE)and the coefficient of determination(R^(2))between the observed and predicted yield by employing a five-fold cross-validation *** showed that the BO algorithm can capture better hyper-parameters for the XGBoost prediction model than can the default XGBoost *** robustness and generalization of the BO-XGBoost model yielded prominent results with RMSE and R^(2) values of 0.0967 and 0.9806(for the testing phase),*** results demonstrated the merits of the proposed BO-XGBoost *** addition,variable importance through mutual information tests was applied to interpret the XGBoost model and demonstrated that machine parameters have the greatest impact as compared to rock mass and material properties.

关键词： TBM performance Advance rate XGBoost Bayesian optimization Predictive modeling