Advanced Machine Learning Methods for Prediction of Blast-Induced Flyrock Using Hybrid SVR Methods

Blasting in surface mines aims to fragment rock masses to a proper size.However,flyrock is an undesirable effect of blasting that can result in human injuries.In this study,support vector regression(SVR)is combined with four algorithms:gravitational search algorithm(GSA),biogeography-based optimization(BBO),ant colony optimization(ACO),and whale optimization algorithm(WOA)for predicting flyrock in two surface mines in Iran.Additionally,three other methods,including artificial neural network(ANN),kernel extreme learning machine(KELM),and general regression neural network(GRNN),are employed,and their performances are compared to those of four hybrid SVR models.After modeling,the measured and predicted flyrock values are validated with some performance indices,such as root mean squared error(RMSE).The results revealed that the SVR-WOA model has the most optimal accuracy,with an RMSE of 7.218,while the RMSEs of the KELM,GRNN,SVR-GSA,ANN,SVR-BBO,and SVR-ACO models are 10.668,10.867,15.305,15.661,16.239,and 18.228,respectively.Therefore,combining WOA and SVR can be a valuable tool for accurately predicting flyrock distance in surface mines.

A Comparative Study of Two Tree-Based Models for Predicting Flyrock Velocity at Open Pit Bench Mining

Blasting is a cost-effective technique to break hard rock volumes by using explosives in the mining and civil engineering realms. Moreover, although blasting is a designed process and plays an indispensable role in these industries, it can also have multiple adverse environmental impacts. One such effect is flyrock, which poses risks to nearby machinery, and residential structures, and can even lead to injuries or fatalities. To optimize blasting efficiency as well as restrict side effects, prediction of the blast aftereffects is vital. Therefore, the present work focuses on using two machine learning methods to predict the velocity of flyrock in the open pit mine. To address this issue, a comprehensive dataset was gathered from the open pit mine. Then, Decision Tree and Random Forest algorithms were employed to predict flyrock velocity. The Random Forest model demonstrated superior performance compared to the Decision Tree model. Nonetheless, the performance of the Decision Tree model was deemed satisfactory, as evidenced by its coefficient of determination value of 0.83, mean squared error (MSE) of 4.2, and mean absolute percentage error (MAPE) of 5.6%. Considering these metrics, it is reasonable to conclude that tree-based algorithms can be effective in predicting flyrock velocity.

Prediction of flyrock distance induced by mine blasting using a novel Harris Hawks optimization-based multi-layer perceptron neural network

In mining or construction projects,for exploitation of hard rock with high strength properties,blasting is frequently applied to breaking or moving them using high explosive energy.However,use of explosives may lead to the flyrock phenomenon.Flyrock can damage structures or nearby equipment in the surrounding areas and inflict harm to humans,especially workers in the working sites.Thus,prediction of flyrock is of high importance.In this investigation,examination and estimation/forecast of flyrock distance induced by blasting through the application of five artificial intelligent algorithms were carried out.One hundred and fifty-two blasting events in three open-pit granite mines in Johor,Malaysia,were monitored to collect field data.The collected data include blasting parameters and rock mass properties.Site-specific weathering index(WI),geological strength index(GSI) and rock quality designation(RQD)are rock mass properties.Multi-layer perceptron(MLP),random forest(RF),support vector machine(SVM),and hybrid models including Harris Hawks optimization-based MLP(known as HHO-MLP) and whale optimization algorithm-based MLP(known as WOA-MLP) were developed.The performance of various models was assessed through various performance indices,including a10-index,coefficient of determination(R^(2)),root mean squared error(RMSE),mean absolute percentage error(MAPE),variance accounted for(VAF),and root squared error(RSE).The a10-index values for MLP,RF,SVM,HHO-MLP and WOA-MLP are 0.953,0.933,0.937,0.991 and 0.972,respectively.R^(2) of HHO-MLP is 0.998,which achieved the best performance among all five machine learning(ML) models.

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

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.

Prediction of blast-induced flyrock in Indian limestone mines using neural networks

Frequency and scale of the blasting events are increasing to boost limestone production. Mines areapproaching close to inhabited areas due to growing population and limited availability of land resourceswhich has challenged the management to go for safe blasts with special reference to opencast mining.The study aims to predict the distance covered by the flyrock induced by blasting using artificial neuralnetwork （ANN） and multi-variate regression analysis （MVRA） for better assessment. Blast design andgeotechnical parameters, such as linear charge concentration, burden, stemming length, specific charge,unconfined compressive strength （UCS）, and rock quality designation （RQD）, have been selected as inputparameters and flyrock distance used as output parameter. ANN has been trained using 95 datasets ofexperimental blasts conducted in 4 opencast limestone mines in India. Thirty datasets have been used fortesting and validation of trained neural network. Flyrock distances have been predicted by ANN, MVRA,as well as further calculated using motion analysis of flyrock projectiles and compared with the observeddata. Back propagation neural network （BPNN） has been proven to be a superior predictive tool whencompared with MVRA. 2014 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting byElsevier B.V. All rights reserved.

Prediction of flyrock induced by mine blasting using a novel kernel-based extreme learning machine

Blasting is a common method of breaking rock in surface mines.Although the fragmentation with proper size is the main purpose,other undesirable effects such as flyrock are inevitable.This study is carried out to evaluate the capability of a novel kernel-based extreme learning machine algorithm,called kernel extreme learning machine(KELM),by which the flyrock distance(FRD) is predicted.Furthermore,the other three data-driven models including local weighted linear regression(LWLR),response surface methodology(RSM) and boosted regression tree(BRT) are also developed to validate the main model.A database gathered from three quarry sites in Malaysia is employed to construct the proposed models using 73 sets of spacing,burden,stemming length and powder factor data as inputs and FRD as target.Afterwards,the validity of the models is evaluated by comparing the corresponding values of some statistical metrics and validation tools.Finally,the results verify that the proposed KELM model on account of highest correlation coefficient(R) and lowest root mean square error(RMSE) is more computationally efficient,leading to better predictive capability compared to LWLR,RSM and BRT models for all data sets.

露天爆破飞石距离智能预测研究

为了在露天爆破中更准确地预测出飞石的抛掷距离,研究引入多科得分的概念,利用多科得分思维进化算法(Multidisciplinary Score Mind Evolutionary Algorithm,MSMEA)对BP神经网络(Back-Propagation Neural Network)进行优化并建立模型来预测飞石距离。通过分析隐含层神经元个数、种群规模、子种群规模、优胜及临时子种群个数建立了64个多科得分思维进化算法优化BP神经网络模型(Back-Propagation Neural Network Optimized by Multidisciplinary Score Mind Evolutionary Algorithm,MSMEA BP),并选取了其中最优的MSMEA BP模型。为了验证预测模型的有效性,分别用MSMEA BP模型、思维进化算法优化BP神经网络模型(Back-Propagation Neural Network Optimized by Mind Evolutionary Algorithm,MEA BP)和BP神经网络模型对10组爆破飞石距离进行预测。结果显示,MSMEA BP模型得到的预测结果与真实值之间的平均相对误差、决定系数、均方根误差、均方根百分比误差分别达到3.67%、0.9808、7.3571、1.33%,依次优于MEA BP模型和BP神经网络模型,表明在相同训练条件下,采用多科得分思维进化算法对BP神经网络模型进行优化,可以克服BP神经网络易陷入局部最优解的问题,进而显著提高模型的预测精度。该方法为预测爆破飞石距离提供了一个新思路。

基于高斯过程回归矿山爆破飞石距离预测模型

为了提高矿山爆破飞石距离预测结果的精度,首先,根据马来西亚某矿山52次爆破作业记录的飞石距离相关参数,建立由6个输入(炮孔孔径、炮孔长度、最小抵抗线/孔距、炮孔填塞长度、最大一段装药量、炸药单耗)和1个输出(飞石距离)组成的数据库。然后,基于高斯过程回归机器学习算法,建立爆破飞石距离的预测模型,将其应用于马来西亚某矿山中,并与2种主流的机器学习方法(支持向量回归和神经网络)的预测结果进行对比。结果表明:从实际图-预测值图和残差分析看,基于双层神经网络构建的飞石距离预测模型的预测效果最差;从回归评价指标看,基于二次有理高斯过程回归建立的飞石距离预测模型的预测效果最优,其R-平方(R^(2))值为0.9、均方根误差(RMSE)值为24.67、均方误差(MSE)值为608.61、平均绝对误差(MAE)值为21.42。由此可知,基于高斯过程回归预测矿山爆破飞石距离更精确。可为类似矿山爆破安全警戒范围计算提供理论基础。

一起定向爆破拆除楼房的电力设施安全管控策略探讨与实践

为确保电力设施周围进行定向爆破作业的安全性,以广州市增城区一起定向爆破拆除楼房为例,运用相对成熟的经验公式和防范做法验证振速、飞石等保护措施设计合理性,分析爆破过程中的监测参数,验证了爆破效果,达到了安全爆破的预期目的。

爆破飞石致人死亡案例分析

根据爆破飞石在空气中的抛掷规律及岩石结构的物理性质 ,对爆破飞石致人死亡典型案例进行分析 ,为法院定案提供了科学依据。

钢筋混凝土立柱爆破破坏过程及个别飞散物试验研究

爆破拆除实践中存在保守设计、过度防护的现象,其根源是不能确定炸药能量“供”与“求”的平衡点。为研究多药包共同作用下钢筋混凝土立柱爆破破坏及个别飞散物运动过程,在野外爆破试验场开展了多组立柱爆破试验。高速摄影观测及破碎碎块分析结果表明:高段位孔内雷管的名义延期时间的误差影响立柱的爆破破坏过程;爆破个别飞散物在100 ms的观测时间内的运动速度与时间呈线性关系,抛掷速度为10~20 m/s,抛掷方向以水平方向为主。在工程实践中,建议将爆破对象外围构件作为防护重点,以柔性防护为主、刚性防护为辅,提高项目经济效益与施工效率。

爆破飞石预测公式的量纲分析法

在爆破飞石距离的研究方面,目前还没有一个普遍接受的爆破飞石预测公式。量纲分析法不涉及物理问题的数量方程,可以简化数学分析过程,减少相关参数,因此特别适合用于爆破理论分析。用量纲分析法对爆破飞石的产生过程进行了研究,将其分为炸药爆炸和飞石抛掷两个过程,得出特定情况下爆破飞石抛掷距离的通用预测公式。

69.8m高冷却塔定向爆破拆除

介绍了一座双曲线钢筋混凝土筒式结构冷却塔的定向爆破拆除。阐述了冷却塔结构特点、爆破难点、总体拆除方案、设计参数的选择及飞石防护措施等。爆破取得预期效果,可为同类工程提供参考。

某铅锌矿爆破有害效应分析及安全评估

以某铅锌矿爆破施工为背景,在现场试验的基础上,对测得的振动速度进行回归分析,得到场地爆破振动衰减规律,并结合爆破主频率,确定了周围民房的容许振动速度为2cm/s。同时,对爆破冲击波和噪声进行研究,结果表明:爆破振动、冲击波和噪声均与爆心距、炸药量有关。当爆心距相同时,噪声对建筑物和人员的影响最大,空气冲击波次之,爆破振动较小。主要从控制最大段药量和爆源距安全原则考虑,提出了防爆破振动、噪声和冲击波及飞石的安全距离。另外,还提出了硐口悬挂3层麻袋、堵塞炮泥和控制起爆网络中段间微差等安全措施,并对有害效应进行评估,其结果对后续爆破设计和施工具有实用价值和指导意义。

露天矿边坡在爆破中的飞石距离研究

露天矿边坡爆破产生的飞石对周围人机系统造成破坏,基于颗粒流理论研究了爆破后飞石的飞出距离。以海州矿某边坡为例,在边坡上设置了3种药量TNT的15个爆破点,对计算平衡后模型中颗粒飞出的散落情况进行了研究。结果表明,细观过程可分为3个阶段:爆炸冲击起主导作用的阶段、重力占优势的上覆岩层塌落阶段、颗粒下滑局部调整至平衡阶段。最大飞石距离小于450 m;A1到A15的飞石距离逐渐减小;1 kg TNT使岩石松动滑落,5 kg TNT飞石距离在250 m以内,10 kg TNT的飞石距离随爆破点的位置不同而不同。同时,飞石的距离与上覆岩层的完整性有很大关系。

控制硐室爆破飞石安全问题措施探讨

硐室大爆破产生个别飞石的原因,主要有测量不准确、地质条件不清楚、药包布置不合适、爆破参数选择不当、装药堵塞不按设计要求施工、人为操作失误等。重点探讨控制硐室大爆破飞石应采取的措施,并以湖北郧西30万方级配石料开采硐室大爆破为例,证明应用这些安全措施,成功地将爆破飞石控制在200m的范围之内。

基于灰色Elman神经网络的爆破飞石距离预测研究

针对单一神经网络预测方法存在一些不足,将建立灰色关联分析法与Elman神经网络的耦合模型,对爆破飞石最大飞散距离进行预测研究。首先,利用灰色关联分析方法对数据进行预处理,确定各影响因素与爆破飞石距离之间的关联度;然后,根据关联度的大小,选择关联度较大的影响因素作为Elman神经网络的输入层数据;最后,用神经网络的功能对数据进行训练和预测。研究结果表明:利用灰色关联分析方法确定主要影响因素作为输入层,比单一使用Elman神经网络的预测精度更高,达到95%以上。

铅锌矿爆破有害效应分析及安全评估

以铅锌矿爆破施工为背景,在现场试验的基础上,对测得的振动速度进行了回归分析,得到了场地爆破震动衰减规律,并结合爆破主频率,确定了周围民房的容许振动速度为2 cm/s。同时,对爆破冲击波、噪声和飞石进行了研究并提出了一些预防措施,并对有害效应进行了评估,对后续爆破设计和施工具有指导意义。

竖井爆破控制技术

结合青岛地铁2号线竖井施工,针对竖井施工中控制震动和爆破飞石的重要性,提出爆破过程中应该采取的相应措施,即通过控制单位耗药量、单孔药量、选取合理的孔网参数及起爆网路,并通过一定的防护措施有效控制爆破有害效应。按以上措施进行竖井爆破,并用TC-4850爆破测振仪在地表进行了监测,将振速控制在1cm/s以内,与此同时,爆破飞石也得到了有效控制。

论烟囱定向拆除爆破的安全技术

定向爆破是烟囱、水塔等高耸建筑物最常用的拆除爆破方案。从保证烟囱可靠倒塌、定向倾倒、飞石防护、爆堆控制等方面比较全面地论述了烟囱定向拆除爆破的安全技术。