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1.
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.  相似文献   

2.
基于Mamdani FIS模型的滑坡易发性评价研究   总被引：1，自引：0，他引：1  
张纫兰  王少军  李江风 《岩土力学》2014,35(Z2):437-444
滑坡的形成是众多非线性关系的影响因子相互作用的结果,传统滑坡预测方法需要大量实地勘查数据。利用Mamdani FIS(模糊推理系统)模型对三峡库区巴东-秭归段进行滑坡易发性预测,并对结果进行评价。通过地理信息系统(geographic information system,GIS)、遥感(remote sensing,RS)技术和区域地质背景资料获取地形类、生态环境类和地质背景类共3类7种滑坡影响因子,建立了192条相关的推理规则,在Matlab平台下基于Mamdani FIS模型得到研究区滑坡易发性预测指数,并生成滑坡易发性区划图。预测结果的受试者工作特征曲线下的面积值为82.8%,显示滑坡评估效果良好。结果证明,与其他模型相比,基于空间信息技术的Mamdani FIS模型,利用其非线性分析能力和基于专家意见的推理规则,评估滑坡易发性时不需要先验知识支撑,简化了模型使用时对数据的要求。另外,该模型只需通过专家意见改变推理规则就可以应用于不同的地质地理环境区域,显示其较强的适应性。  相似文献   

3.

Prediction and control of blast-induced ground vibration is a matter of concern in mining industry since long. Several approaches ranging from scaled distance regression, different numerical methods to wave superimposition theories have been tried by many researchers for better prediction and control of blast-induced ground vibration. Signature hole analysis is one of the popular simulation methods to predict the ground vibration generated due to production blast. It superimposes the recorded signature hole waveform using a computer program to predict the production blast-induced vibration. The technique inputs the designated time of detonation of each hole and superimposes the waves generated by each hole to predict the nearest value of peak particle velocity and frequency of blast-induced ground vibration. Although a very useful approach, it requires a computer program to simulate the linear superimposition of waveforms. The simulation is not possible for every blast as it takes time and also is difficult for field engineers to simulate every time, whereas it is always easy for blasting engineers to adapt and use an empirical equation/approach for prediction and control of blast-induced ground vibration than simulation. In this paper, an attempt has been made to develop an innovative and simplified analytical approach of signature hole analysis. The simplified sinusoidal wave equation is obtained from recorded signature hole ground vibration waveform properties and is superimposed mathematically according to the multi-hole blast design to predict the production blast-induced ground vibrations. The validation of the developed approach was done in three different sites, and up to 15% more accuracy in prediction of the blast, vibrations are achieved in comparison with signature hole analysis prediction.

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4.
The environmental effects of blasting must be controlled in order to comply with regulatory limits. Because of safety concerns and risk of damage to infrastructures, equipment, and property, and also having a good fragmentation, flyrock control is crucial in blasting operations. If measures to decrease flyrock are taken, then the flyrock distance would be limited, and, in return, the risk of damage can be reduced or eliminated. This paper deals with modeling the level of risk associated with flyrock and, also, flyrock distance prediction based on the rock engineering systems (RES) methodology. In the proposed models, 13 effective parameters on flyrock due to blasting are considered as inputs, and the flyrock distance and associated level of risks as outputs. In selecting input data, the simplicity of measuring input data was taken into account as well. The data for 47 blasts, carried out at the Sungun copper mine, western Iran, were used to predict the level of risk and flyrock distance corresponding to each blast. The obtained results showed that, for the 47 blasts carried out at the Sungun copper mine, the level of estimated risks are mostly in accordance with the measured flyrock distances. Furthermore, a comparison was made between the results of the flyrock distance predictive RES-based model, the multivariate regression analysis model (MVRM), and, also, the dimensional analysis model. For the RES-based model, R 2 and root mean square error (RMSE) are equal to 0.86 and 10.01, respectively, whereas for the MVRM and dimensional analysis, R 2 and RMSE are equal to (0.84 and 12.20) and (0.76 and 13.75), respectively. These achievements confirm the better performance of the RES-based model over the other proposed models.  相似文献   

5.
Studies on Flyrock at Limestone Quarries   总被引：3，自引：0，他引：3  
Summary Observed flyrock distances for 47 blasts at six limestone quarries along with blast design parameters are presented. The influence of blasthole diameter, burden, stemming length, powder factor, the condition of blastholes (dry or wet) and the initiation systems on generation of flyrock is analysed and the most critical parameters for flyrock control are identified. Based on the analysis of results, suggestions are given to minimise the flyrock hazards at limestone quarries.  相似文献   

6.
Excavation of coal, overburden, and mineral deposits by blasting is dominant over the globe to date, although there are certain undesirable effects of blasting which need to be controlled. Blast-induced vibration is one of the major concerns for blast designers as it may lead to structural damage. The empirical method for prediction of blast-induced vibration has been adopted by many researchers in the form of predictor equations. Predictor equations are site specific and indirectly related to physicomechanical and geological properties of rock mass as blast-induced ground vibration is a function of various controllable and uncontrollable parameters. Rock parameters for blasting face and propagation media for blast vibration waves are uncontrollable parameters, whereas blast design parameters like hole diameter, hole depth, column length of explosive charge, total number of blast holes, burden, spacing, explosive charge per delay, total explosive charge in a blasting round, and initiation system are controllable parameters. Optimization of blast design parameters is based on site condition and availability of equipment. Most of the smaller mines have predesigned blasting parameters except explosive charge per delay, total explosive charge, and distance of blast face from surface structures. However, larger opencast mines have variations in blast design parameters for different benches based on strata condition: Multivariate predictor equation is necessary in such case. This paper deals with a case study to establish multivariate predictor equation for Moher and Moher Amlohri Extension opencast mine of India. The multivariate statistical regression approach to establish linear and logarithmic scale relation between variables to predict peak particle velocity (PPV) has been used for this purpose. Blast design has been proposed based on established multivariate regression equation to optimize blast design parameters keeping PPV within legislative limits.  相似文献   

7.
A Hybrid Neuro-Fuzzy Model for Mineral Potential Mapping   总被引：5，自引：0，他引：5  
A GIS-based hybrid neuro-fuzzy approach to mineral potential mapping implements a Takagi–Sugeno type fuzzy inference system in a four-layered feed-forward adaptive neural network. In this approach, each unique combination of predictor patterns is considered a feature vector whose components are derived by knowledge-based ordinal encoding of the constituent predictor patterns. A subset of feature vectors with a known output target vector (i.e., unique conditions known to be associated with either a mineralized or a barren location), extracted from a set of all feature vectors, is used for the training of an adaptive neuro-fuzzy inference system. Training involves iterative adjustment of parameters of the adaptive neuro-fuzzy inference system using a hybrid learning procedure for mapping each training vector to its output target vector with minimum sum of squared error. The trained adaptive neuro-fuzzy inference system is used to process all feature vectors. The output for each feature vector is a value that indicates the extent to which a feature vector belongs to the mineralized class or the barren class. These values are used to generate a favorability map. The procedure is applied to regional-scale base metal potential mapping in a study area located in the Aravalli metallogenic province (western India). The adaptive neuro-fuzzy inference system demarcates high favorability zones occupying 9.75% of the study area and identifies 96% of the known base metal deposits. This result is significant both in terms of reduction in search area and the percentage of deposits identified.  相似文献   

8.
中吉乌铁路是我国西北地区通往中亚、南欧国家的一条国际通路,对其方案线沿线的地质灾害的调查及预测可为其选线提供一定建议。本文基于Mamdani模糊推理系统(Mamdani FIS)对方案线北线AK53-AK130、南线AK61-AK131段研究区的滑坡易发性进行预测。通过区域地质背景资料和遥感影像人机交互解译获取了该区地质环境背景、地形因素以及生态环境3类9种滑坡影响因子,建立768条推理规则,通过Mamdani FIS模型得到区内滑坡敏感度文件,在GIS环境中制作研究区滑坡易发性等级图,将研究区划分为极低易发区、低易发区、中等易发区、高易发区和极高易发区。结果显示,滑坡极高易发区和高易发区分布在研究区东北部的费尔干纳山脉附近以及南部的亚瑟河流域。采用受试者工作特征曲线(Receiver Operating Characteristic Curve,ROC曲线)对结果进行验证,曲线下面积为0.859,表明预测结果精度较高。  相似文献   

9.
Flyrock arising from blasting operations is one of the crucial and complex problems in mining industry and its prediction plays an important role in the minimization of related hazards. In past years, various empirical methods were developed for the prediction of flyrock distance using statistical analysis techniques, which have very low predictive capacity. Artificial intelligence (AI) techniques are now being used as alternate statistical techniques. In this paper, two predictive models were developed by using AI techniques to predict flyrock distance in Sungun copper mine of Iran. One of the models employed artificial neural network (ANN), and another, fuzzy logic. The results showed that both models were useful and efficient whereas the fuzzy model exhibited high performance than ANN model for predicting flyrock distance. The performance of the models showed that the AI is a good tool for minimizing the uncertainties in the blasting operations.  相似文献   

10.
This paper presents an alternative strategy to evaluate the stability of tunnels during the design and construction stages based on a hybrid system, composed by neural, neuro‐fuzzy and analytical solutions. A prototype of this system is designed using a database formed by 261 cases, 45 real and the rest synthetic. This system is capable of reproducing the displacements induced at the periphery of the tunnel before and after support installation. The stability of the excavation process is evaluated using a criterion that considers dimensionless parameters based on the shear strength of the media, the induced deformation level in the ground, the plastic radii and the advance of excavation without support. The efficiency and validity of the prototype is verified with two examples of actual tunnels, one included in the database used to train the system and the other not included. The results of both examples show a better approximation than other commonly used techniques. Copyright © 2005 John Wiley & Sons, Ltd.  相似文献   

11.
Flyrock is an adverse effect produced by blasting in open-pit mines and tunneling projects. So, it seems that the precise estimations and risk level assessment of flyrock are essential in minimizing environmental effects induced by blasting. The first aim of this research is to model the risk level associated with flyrock through rock engineering systems (RES) methodology. In this regard, 62 blasting were investigated in Ulu Tiram quarry, Malaysia, and the most effective parameters of flyrock were measured. Using the most influential parameters on flyrock, the overall risk of flyrock was obtained as 32.95 which is considered as low to medium degree of vulnerability. Moreover, the second aim of this research is to estimate flyrock based on RES and multiple linear regression (MLR). To evaluate performance prediction of the models, some statistical criteria such as coefficient of determination (R2) were computed. Comparing the values predicted by the models demonstrated that the RES has more suitable performance than MLR for predicting the flyrock and it could be introduced as a powerful technique in this field.  相似文献   

12.
利用新型数字激光动态焦散线试验系统,采用有机玻璃板试件进行模型试验,研究了含预制不同形状空孔对岩石定向断裂控制爆破的影响规律。试验结果表明：在定向断裂控制爆破中,设置菱形空孔更有利于实现精细化定向断裂控制爆破,可有效保证巷道周边眼爆破的成型效果;对比含3种不同形状空孔试件爆生主裂纹扩展速度可知,含菱形空孔的扩展速度最大,含圆形空孔的次之,含带刻槽圆形空孔的最低;含圆形空孔试件爆生主裂纹端部动态应力强度因子总体比含菱形和切槽圆形空孔试件爆生主裂纹端部动态应力强度因子大;主裂纹扩展中后期阶段,含菱形空孔试件爆生主裂纹端部动态应力强度因子相对较小。  相似文献   

13.
Most of the railway tunnels in Sweden are shallow-seated (<20 m of rock cover) and are located in hard brittle rock masses. The majority of these tunnels are excavated by drilling and blasting, which, consequently, result in the development of a blast-induced damaged zone around the tunnel boundary. Theoretically, the presence of this zone, with its reduced strength and stiffness, will affect the overall performance of the tunnel, as well as its construction and maintenance. The Swedish Railroad Administration, therefore, uses a set of guidelines based on peak particle velocity models and perimeter blasting to regulate the extent of damage due to blasting. However, the real effects of the damage caused by blasting around a shallow tunnel and their criticality to the overall performance of the tunnel are yet to be quantified and, therefore, remain the subject of research and investigation. This paper presents a numerical parametric study of blast-induced damage in rock. By varying the strength and stiffness of the blast-induced damaged zone and other relevant parameters, the near-field rock mass response was evaluated in terms of the effects on induced boundary stresses and ground deformation. The continuum method of numerical analysis was used. The input parameters, particularly those relating to strength and stiffness, were estimated using a systematic approach related to the fact that, at shallow depths, the stress and geologic conditions may be highly anisotropic. Due to the lack of data on the post-failure characteristics of the rock mass, the traditional Mohr–Coulomb yield criterion was assumed and used. The results clearly indicate that, as expected, the presence of the blast-induced damage zone does affect the behaviour of the boundary stresses and ground deformation. Potential failure types occurring around the tunnel boundary and their mechanisms have also been identified.  相似文献   

14.
Flyrock is a rock thrown to greater distance than desired and is a dangerous and unwanted phenomenon in surface mines, particularly, when blasting is proceeding close to human occupation and dwellings. The prediction of flyrock distance is critical in defining the statutory danger zone of blasting and has evaded blasters for quite some time. Control of flyrock with its distance prediction involves identification of key variables and understanding their influence. Theoretical models though provide a good understanding of the phenomenon, the confidence that can be assigned to such models is still very less. This study presents novel method to identify, merge and consolidate independent variables into a simplified equation for flyrock distance prediction without compromising on the actual field applications. Field investigations were carried out in several mines and relevant data were generated relating to flyrock. The key parameters, namely, explosive, blast design and rock mass nature were characterized and analysed. An empirical model involving the key contributors for flyrock generation and distance prediction were assimilated and a new equation was developed based on actual data collected by employing surface response analysis. The developed model was found to be statistically significant and validated. Sensitivity analysis was conducted to ascertain the role of independent factors on flyrock distance.  相似文献   

15.
China is prone to highly frequent earthquakes due to specific geographical location, which could cause significant losses to society and economy. The task of seismic hazard analysis is to estimate the potential level of ground motion parameters that would be produced by future earthquakes. In this paper, a novel method based on fuzzy logic techniques and probabilistic approach is proposed for seismic hazard analysis (FPSHA). In FPSHA, we employ fuzzy sets for quantification of earthquake magnitude and source-to-site distance, and fuzzy inference rules for ground motion attenuation relationships. The membership functions for earthquake magnitude and source-to-site distance are provided based on expert judgments, and the construction of fuzzy rules for peak ground acceleration relationships is also based on expert judgment. This methodology enables to include aleatory and epistemic uncertainty in the process of seismic hazard analysis. The advantage of the proposed method is in its efficiency, reliability, practicability, and precision. A case study is investigated for seismic hazard analysis of Kunming city in Yunnan Province, People’s Republic of China. The results of the proposed fuzzy logic-based model are compared to other models, which confirms the accuracy in predicting the probability of exceeding a certain level of the peak ground acceleration. Further, the results can provide a sound basis for decision making of disaster reduction and prevention in Yunnan province.  相似文献   

16.
Prediction of the Bullet Effect for Rockfall Barriers: a Scaling Approach   总被引：4，自引：2，他引：2  
The so-called “bullet effect” refers to the perforation of a rockfall protection mesh by impact of a small block, which has a kinetic energy lower than the design value, where the design value is determined through tests with relatively large blocks. Despite playing a key role in the overall performance of a flexible rockfall barrier, this phenomenon is still poorly understood at present. An innovative approach for quantitatively characterizing this effect based on dimensional analysis is proposed in this paper. The analysis rests on a hypothesis that the relevant variables in the impact problem can be combined into three strongly correlated dimensionless parameters. The relationship between these dimensionless parameters (i.e., the scaling relationship) is subsequently investigated and validated by means of data generated with a finite element model. The validation process shows that the dimensionless parameters are apt and that the proposed scaling relationship characterizes the bullet effect with a reasonable level of accuracy. An example from the literature involving numerical simulation of a full rock barrier is considered, and satisfactory agreement between the calculated performance of the barrier and that predicted by the established scaling relationship is observed.  相似文献   

17.
Fuzzy Modeling for Reserve Estimation Based on Spatial Variability   总被引：1，自引：0，他引：1  
This article addresses a new reserve estimation method which uses fuzzy modeling algorithms and estimates the reserve parameters based on spatial variability. The proposed fuzzy modeling approach has three stages: (1) Structure identification and preliminary clustering, (2) Variogram analysis, and (3) Clustering based rule system. A new clustering index approach and a new spatial measure function (point semimadogram) are proposed in the paper. The developed methodology uses spatial variability in each step and takes the fuzzy rules from input-output data. The model has been tested using both simulated and real data sets. The performance evaluation indicates that the new methodology can be applied in reserve estimation and similar modeling problems  相似文献   

18.
Groundwater vulnerability modeling is an alternative approach to evaluate groundwater contamination especially in areas affected by intensive anthropogenic activities. However, the DRASTIC model as a well-known method to assess groundwater vulnerability suffers from the inherent uncertainty associated with its seven essential parameters. In this study, three different fuzzy logic (FL) models (Sugeno fuzzy logic, Mamdani fuzzy logic, and Larsen fuzzy logic) are adopted to improve the DRASTIC system to be more realistic. The vulnerability map of groundwater from multiple aquifer systems (i.e., karstic, alluvium, and complex) in Basara basin, Iraq, was created using the FL models. Validation of the FL models results using NO3-N concentration obtained from wells and springs of the study area indicating that all of the three FL models are applicable for improving the DRASTIC model. However, each of the FL models has its own advantages for groundwater vulnerability estimation in different types of aquifer systems in the Basara basin. Therefore, this study proposes the supervised committee fuzzy logic (SCFL) as a multimodel method to combine the advantages of individual FL models. The SCFL method confirms that no water well with high NO3-N levels would be classified as low risk and vice versa. The study suggests that this approach has provided a convenient estimation of pollution risk in the study area and therefore, a more accurate prediction of the intrinsic vulnerability to pollution in the multiple aquifer system can be achieved through SCFL method.  相似文献   

19.
A Sugeno-type fuzzy inference system is implemented in the framework of an adaptive neural network to map Cu–Au prospectivity of the Urumieh–Dokhtar magmatic arc (UDMA) in central Iran. We use the hybrid “Adaptive Neuro Fuzzy Inference System” (ANFIS; Jang, 1993) algorithm to optimize the fuzzy membership values of input predictor maps and the parameters of the output consequent functions using the spatial distribution of known mineral deposits. Generic genetic models of porphyry copper–gold and iron oxide copper–gold (IOCG) deposits are used in conjunction with deposit models of the Dalli porphyry copper–gold deposit, Aftabru IOCG prospect and other less important Cu–Au deposits within the study area to identify recognition criteria for exploration targeting of Cu–Au deposits. The recognition criteria are represented in the form of GIS predictor layers (spatial proxies) by processing available exploration data sets, which include geology, stream sediment geochemistry, airborne magnetics and multi-spectral remote sensing data. An ANFIS is trained using 30% of the 61 known Cu–Au deposits, prospects and occurrences in the area. In a parallel analysis, an exclusively expert-knowledge-driven fuzzy model was implemented using the same input predictor maps. Although the neuro-fuzzy analysis maps the high potential areas slightly better than the fuzzy model, the well-known mineralized areas and several unknown potential areas are mapped by both models. In the fuzzy analysis, the moderate and high favorable areas cover about 16% of the study area, which predict 77% of the known copper–gold occurrences. By comparison, in the neuro-fuzzy approach the moderate and high favorable areas cover about 17% of the study area, which predict 82% of the copper–gold occurrences.  相似文献   

20.
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.  相似文献   

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