Prediction and controlling of flyrock in blasting operation using artificial neural network

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 complexity of flyrock analysis. Existence of various effective parameters and their unknown relationships are the main reasons for inaccuracy of the empirical models. Presently, application of new approaches such as artificial intelligence is highly recommended. In this paper, an attempt has been made to predict and control flyrock in blasting operation of Sangan iron mine, Iran incorporating rock properties and blast design parameters using artificial neural network (ANN) method. A three-layer feedforward back-propagation neural network having 13 hidden neurons with nine input parameters and one output parameter were trained using 192 experimental blast datasets. It was also observed that in ascending order, blastability index, charge per delay, hole diameter, stemming length, powder factor are the most effective parameters on the flyrock. Reducing charge per delay caused significant reduction in the flyrock from 165 to 25 m in the Sangan iron mine.     

Prediction of flyrock and backbreak in open pit blasting operation: a neuro-genetic approach

An ideally performed blasting operation enormously influences the mining overall cost. This aim can be achieved by proper prediction and attenuation of flyrock and backbreak. Poor performance of the empirical models has urged the application of new approaches. In this paper, an attempt has been made to develop a new neuro-genetic model for predicting flyrock and backbreak in Sungun copper mine, Iran. Recognition of the optimum model with this method as compared with the classic neural networks is faster and convenient. Genetic algorithm was utilized to optimize neural network parameters. Parameters such as number of neurons in hidden layer, learning rate, and momentum were considered in the model construction. The performance of the model was examined by statistical method in which absolutely higher efficiency of neuro-genetic modeling was proved. Sensitivity analysis showed that the most influential parameters on flyrock are stemming and powder factor, whereas for backbreak, stemming and charge per delay are the most effective parameters.     

Prediction of swelling pressure of soil using artificial intelligence techniques

The swelling pressure of soil depends upon various soil parameters such as mineralogy, clay content, Atterberg’s limits, dry density, moisture content, initial degree of saturation, etc. along with structural and environmental factors. It is very difficult to model and analyze swelling pressure effectively taking all the above aspects into consideration. Various statistical/empirical methods have been attempted to predict the swelling pressure based on index properties of soil. In this paper, the computational intelligence techniques artificial neural network and support vector machine have been used to develop models based on the set of available experimental results to predict swelling pressure from the inputs; natural moisture content, dry density, liquid limit, plasticity index, and clay fraction. The generalization of the model to new set of data other than the training set of data is discussed which is required for successful application of a model. A detailed study of the relative performance of the computational intelligence techniques has been carried out based on different statistical performance criteria.     

人工神经网络在爆破块度预测中的应用研究

利用人工神经网络模型对爆破块度进行预测,实验结果表明,该方法是完全可行的。通过对实验样本数据进行归一化处理后再对人工神经网络模型进行训练和预测,其预测精度会得到大大提高。     

An application of artificial intelligence for rainfall-runoff modeling

This study proposes an application of two techniques of artificial intelligence (AI) for rainfall-runoff modeling: the artificial neural networks (ANN) and the evolutionary computation (EC). Two different ANN techniques, the feed forward back propagation (FFBP) and generalized regression neural network (GRNN) methods are compared with one EC method, Gene Expression Programming (GEP) which is a new evolutionary algorithm that evolves computer programs. The daily hydrometeorological data of three rainfall stations and one streamflow station for Juniata River Basin in Pennsylvania state of USA are taken into consideration in the model development. Statistical parameters such as average, standard deviation, coefficient of variation, skewness, minimum and maximum values, as well as criteria such as mean square error (MSE) and determination coefficient (R ²) are used to measure the performance of the models. The results indicate that the proposed genetic programming (GP) formulation performs quite well compared to results obtained by ANNs and is quite practical for use. It is concluded from the results that GEP can be proposed as an alternative to ANN models.     

一种高地应力条件下爆破开挖诱发振动峰值的预测模型

高地应力条件下深部岩体爆破开挖过程中,炸药爆炸产生的能量和岩体开挖释放的应变能共同构成了振动的能量源。采用传统的基于单响药量的萨道夫斯基经验公式及其改进公式预测高地应力条件下爆破开挖诱发振动峰值精度不高。通过量纲分析,提出了一种基于能量平衡原理的振动峰值预测模型。结合锦屏二级深埋引水隧洞爆破试验,以上半洞实测振动数据为学习样本,训练模型;以下半洞实测振动数据为对比样本,检验模型。结果表明:与传统预测模型相比,预测模型具有较高的拟合相关系数和较低的预测均方根误差,可以更好地应用于高地应力条件下爆破开挖诱发振动峰值的预测。     

Some aspects of cast blasting techniques for dam construction

Summary A proposal is under consideration to construct a dam on an experimental basis by a cast (or directional) blasting technique, DBT. The dam site is located at Bharari Khad, a tributary of the Sutlaz river in Himachal Pradesh. Site investigations have been completed and a large scale blast has been designed for construction of the experimental dam.The paper describes the basic design concept of DBT and application of throw and caving methods for construction of dams. The preliminary tests required to design the blasting pattern are detailed. The technique has a great potential because it reduces construction cost and time particularly in inaccessible mountain regions.     

Application of soft computing in predicting rock fragmentation to reduce environmental blasting side effects

In the blasting operation, risk of facing with undesirable environmental phenomena such as ground vibration, air blast, and flyrock is very high. Blasting pattern should properly be designed to achieve better fragmentation to guarantee the successfulness of the process. A good fragmentation means that the explosive energy has been applied in a right direction. However, many studies indicate that only 20–30 % of the available energy is actually utilized for rock fragmentation. Involvement of various effective parameters has made the problem complicated, advocating application of new approaches such as artificial intelligence-based techniques. In this paper, artificial neural network (ANN) method is used to predict rock fragmentation in the blasting operation of the Sungun copper mine, Iran. The predictive model is developed using eight and three input and output parameters, respectively. Trying various types of the networks, it was found that a trained model with back-propagation algorithm having architecture 8-15-8-3 is the optimum network. Also, performance comparison of the ANN modeling with that of the statistical method was confirmed robustness of the neural networks to predict rock fragmentation in the blasting operation. Finally, sensitivity analysis showed that the most influential parameters on fragmentation are powder factor, burden, and bench height.     

Estimation of air-overpressure produced by blasting operation through a neuro-genetic technique

Blasting operations usually produce significant environmental problems which may cause severe damage to the nearby areas. Air-overpressure (AOp) is one of the most important environmental impacts of blasting operations which needs to be predicted and subsequently controlled to minimize the potential risk of damage. In order to solve AOp problem in Hulu Langat granite quarry site, Malaysia, three non-linear methods namely empirical, artificial neural network (ANN) and a hybrid model of genetic algorithm (GA)–ANN were developed in this study. To do this, 76 blasting operations were investigated and relevant blasting parameters were measured in the site. The most influential parameters on AOp namely maximum charge per delay and the distance from the blast-face were considered as model inputs or predictors. Using the five randomly selected datasets and considering the modeling procedure of each method, 15 models were constructed for all predictive techniques. Several performance indices including coefficient of determination (R ²), root mean square error and variance account for were utilized to check the performance capacity of the predictive methods. Considering these performance indices and using simple ranking method, the best models for AOp prediction were selected. It was found that the GA–ANN technique can provide higher performance capacity in predicting AOp compared to other predictive methods. This is due to the fact that the GA–ANN model can optimize the weights and biases of the network connection for training by ANN. In this study, GA–ANN is introduced as superior model for solving AOp problem in Hulu Langat site.     

桩井爆破振动在邻近边坡产生的特殊效应

为研究桩井爆破地震波在邻近自然边坡传播规律,对桩井开挖爆破振动速度进行了现场测试。在邻近桩井的台阶坡脚及边缘各布置一个测点。测试结果表明,位于台阶边缘测点的径向、垂向振动速度及主频率较位于坡脚处的测点存在增大趋势,出现了坡面效应;切向振动速度及主频率存在减小的趋势,说明爆破地震波的速度及频率变化具有一致性,且爆破地震波的放大效应具有方向性,以垂向为主。坡面效应合理地解释了爆破地震波在台阶边缘的振速放大效应,同时通过“鞭梢效应”对爆破地震波频率的分析,验证了坡面效应的合理性。     

Application of a BP neural network in predicting destroyed floor depth caused by underground pressure

Predicting the destroyed floor depth caused by the mining of coal seams is of great importance in judging whether the mining of a deep coal seam can be safely performed above a confined aquifer and to prevent the inrush of water from the floor. Thirty sets of coal mining data on destroyed floor depth were selected for study. A comprehensive analysis of the factors that influence the depth of destruction of coal seam floor strata was performed and combined with the ability of a BP neural network to address dynamic nonlinear information. Then, a set of test samples was assembled and used to construct a predictive model using a BP neural network. The model was then used to predict the destroyed floor depth of the 7105 working face of the Baizhuang Coal Mine in the Feicheng coal field. To verify the effectiveness of the model, the depth of the destroyed strata comprising the coal seam floor was measured using equipment called the “Double Sided Sealed Borehole Water Injection Device.” By comparing the predictions made by the BP neural network with actual measurements, the conclusion was reached that a BP neural network model can effectively be used to predict the destroyed floor depth caused by the mining of a coal seam.     

董家山隧道小净距段爆破控制的数值模拟

结合都-汶高速公路董家山隧道小净距段的实际情况,应用数值模拟方法,对小净距段在爆破荷载作用下的相互影响问题进行了研究.结果表明在隧道爆破施工中应对迎爆侧进行重点监控,采用合理的开挖和加固方式将有效降低爆破施工对先建隧道的不利影响.研究结论可为类似小净距段隧道的爆破设计、施工及现场监控量测提供参考.     

天然沉积结构性土的次固结变形预测方法

结构性土存在着其特有的变形和强度特性,次固结特性也表现出明显区别于与重塑土。通过对连云港天然沉积原状土和重塑土进行一维压缩次固结试验,研究了典型结构性土的次固结特性。试验结果表明,土体由于受结构性影响,结构破坏前后其次固结特性发生明显变化;结构屈服前（固结压力小于固结屈服压力）不发生次固结变形或次固结变形甚微;当土体处于屈服状态时,土体次固结变形突然增大,次固结系数Cα出现峰值;结构屈服后（固结压力大于固结屈服压力）,Cα随固结压力的增大而减小,表现为与当前的应力水平和时间密切相关的特性,应力水平对Cα的影响会随着次固结时间的增长而削弱。基于以上机制,建立了考虑结构性影响的次固结变形计算模型,该模型中Cα不仅与压缩指数Cc有关,且也与时间有关,基于该模型计算得到的Cα值和次固结变形均与试验值吻合较好。     

面向人工智能识别的岩浆岩断面形态特征研究

为了研发手机识别岩石的软件,以岩浆岩基础数据库的建立为重点,开展岩浆岩断面外部特征的基础研究.根据可直接用于人工智能识别外部特征的标准,即能够直接被手机摄像头识别、计算机量化和获取特征的标准,筛选出6个具有人工识别意义的外部形态特征,分别是颜色特征、结晶程度、相对粒度、绝对粒度、构造特征和断口特征.根据每种特征不同的量...     

Evaluation of the liquid hydrocarbon potential of coal by artificial maturation techniques

In certain areas, relatively large accumulations of liquid hydrocarbons have been attributed to coals. Evaluating the source rock potential of coal requires definition of both the generative potential (quantity and composition of generated hydrocarbons), and expulsion efficiency. Hydrous pyrolysis experiments were completed using Tertiary lignites (R_o < 0.35%) from North Dakota and the Far East to evaluate the source rock potential of coal. The North Dakota lignite is vitrinite-rich (93%) and liptinite-poor (3%); the Far East lignite is liptinite-rich (32% of total maceral content). These lignites have Hydrogen Index values of 123 and 483 mg HC/g OC, respectively. Differences in oil-pyrolysate yield, composition, and temperature of maximum pyrolysate yield from hydrous pyrolysis experiments for these two lignites are related to the type and amount of liptinite and vitrinite macerals. A maximum of 48 and 158 mg oil-pyrolysate/g OC is generated and expelled from the North Dakota and Far East lignites, respectively. Although these lignites consist predominantly of gas-prone vitrinitic components, their organic-rich nature can compensate for their poor convertibility to liquid hydrocarbons. The composition of these artificially generated oil-pyrolysates are similar to some non-marine oils, suggesting that this type of organic matter can be a significant contributor to many oils. Although the overall composition of the generated products from the two lignites is similar, the distribution of these products is significantly different. Homologous series of methyl ketones and alkyl benzenes have been identified in both oil-pyrolysates. Their presence and characteristic distribution suggest that microbial degradation occurred during the formation of these lignites. Although many coals generate significate amounts of liquid hydrocarbons that are similar to naturally occurring oils, poor explusion efficiency limits their source rock potential. Significant amounts of liquid products are assimilated by the vitrinitic matrix of most coals prior to expulsion, severely limiting the amount of petroleum available for migration and reservoir accumulation. However, adequate expulsion may occur in certain liptinite-rich coals or coals occurring in unique depositional settings.     

人工神经网络在水泥加固土力学性能预测中的应用

提出了基于人工神经网络（ANN）的水泥加固土力学性能指标计算的新方法，并在此基础上预估水泥土搅拌桩体和复合地基的承载力，利用实测资料直接建模，避免了传统方法计算过程中各种人为因素的干扰，所建立的模型预测精度高，简便易行，因而具有广泛的工程实用价值。     

天然气管道沟槽石方爆破开挖技术研究

结合西气东输工程聊城至泰安天然气输气管道沟开挖工程的特点,探讨了沟槽石方爆破的设计原则和爆破施工方案。通过选择合适的炮孔布置方式及参数、分段装药结构和起爆顺序,提出了沟槽微差爆破技术以及减小爆破震动和飞石的安全措施,保证了通讯光缆和周围设施的安全,取得了良好的爆破效果,为工程的顺利进行创造了有利条件     

采水地面沉降时空预测模型研究

地下水开采引起的地面沉降对地面建（构）筑物的正常使用和结构安全构成了严重威胁,深入研究采水地面沉降预测理论对于沉降灾害防治具有重要意义。针对本构模型和土体参数确定上的困难,采用力学推理和数学统计相结合的方法,建立了新的采水地面沉降时空预测模型。首先,利用太沙基固结微分方程,建立了反映地面沉降时间效应的半经验计算模型;其次,在分析采水地面沉降空间分布规律的基础上,利用随机介质理论研究了采水地面沉降空间分布特征;再次,综合考虑采水地面沉降的时间效应和空间分布形态,建立了采水地面沉降的时空预测模型。利用该模型计算地面沉降共需4个计算参数,介绍了参数求解方法。最后,利用时空计算模型预测了某地单井采水引起地面沉降的时空规律。研究表明,所建立的采水地面沉降预测模型能准确地反映采水地面沉降的时空规律,能方便、快捷地预测地下水开采引起的地面沉降。