Physical model simulation of block caving in jointed rock mass

Incorrect estimation of undercut dimensions in the block caving method can lead to the cessation of caving operations and loss of a large portion of deposits. Numerical modeling is one of the methods for determining the minimum caving span. Numerical and physical modeling methods are useful for an accurate understanding of caving operations. Accordingly, this research focused on investigating the performance of physical and numerical modeling in determining the effects of depth and joint orientation on the minimum required caving span for the initiation and propagation of caving. The physical model was made with 1.5*1.5 square meter dimensions and consisted of travertine blocks with 4*4 square centimeter dimensions. In addition, joints were modeled with dips of 0, 90, 45, 135, 30, and 120 degrees. The physical model could simulate ground stress conditions to great depths and show the behavior of the jointed rock mass in a two-dimensional space. Further, by capturing this behavior, it was possible to compare its result with UDEC software. The results demonstrated that the number of falling blocks and the height of the caving increased by increasing the dip. Furthermore, the formation of arches due to high horizontal stress stops the caving, which will occur again with the increasing span. Although the horizontal stresses and geometrical properties of the joints affect the shape of the caving area, its shape largely follows the dip and orientation of the rock mass joints. Poor draw control causes caved ore columns, which can lead to the formation of a stable arc. Finally, the height of the caved back increases in each span by increasing the depth while decreasing the dip of the joints

Numerical Analysis of the Primer Location Effect on Ground Vibration Caused by Blasting

Ground vibration is one of the undesirable results of blasting operations. Different methods have been proposed to predict and control ground vibration that is caused by blasting. These methods can be classified as laboratory studies, fieldwork and numerical modeling. Among these methods, numerical modeling is the one which saves time and cuts costs since it takes into account the basic principles of mechanics and provides step by step time-domain solutions. In order to use numerical analysis in predicting the results of blasting operations, the accuracy of the output must be verified through field test. In this study, ground vibration caused by blasting in a field operation in Miduk Copper Mine was recorded using 3-components seismometers of the Vibracord seismograph and analyzed by Vibration-Meter software. Propagation of the waves caused by blasting in the mine slope was modeled using discrete element logic in the UDEC numerical software and compared to the results of the field test. Having tested the accuracy of the results obtained, the effect of primer location and the direction of detonation propagation in the blast hole on the rate of ground vibration caused by blasting was investigated. The results show that by changing primer location from the bottom of the hole to its top, the rate of ground vibration caused by blasting increases

Predicting the Occurrence of Hydraulic Fracture in Grouting Operations Based on the Pressure in the Penetrated Cement Grout

Cement grouting is an operation often carried out to consolidate and seal the rock mass in dam sites and tunnels. The quality and efficiency of a grouting operation depends on various factors such as water take, grout properties and grouting pressure. One of the parameters which have the highest effect is pressure since the application of excessive pressure causes the hydraulic fracture phenomenon to occur in the rock mass and too little pressure leads to incomplete grouting and failure to seal the site in a perfect manner. Mathematical modeling is used for the first time in this study to predict and determine the optimum pressure. Thus, the joints that exist in the rock mass are simulated using cylindrical shell model. The joint surroundings are also modeled through Pasternak environment. To obtain equations governing the joints and the surroundings, energy method is used accompanied by Hamilton principle. In the end, an analytical solution method is used to obtain the maximum grouting pressure. In order to validate the modeling, the grouting pressure values obtained by the model were used in the sites of Seymareh and Aghbolagh dams and the relative error rates were measured considering the differences between calculated and actual pressures. Modeling in the examined sections of Seymareh dam showed 29.61, 5.57, 21.98, 32.50 and 9.09 percent error rates and in the sections of Aghbolagh dam it rendered the values of 4.32, 5.40 and 2.96 percent. The results indicate that this modeling can be used to estimate the amount of pressure for hydraulic fracture in grouting, to predict it and to prevent it

Prediction of fragmentation due to blasting using mutual information and rock engineering system; case study: Meydook copper mine

One of the key outcomes of blasting in mines is found to be rock fragmentation which profoundly affects downstream expenses. In fact, size prediction of rock fragmentation is the first leap towards the optimization of blasting design parameters. This paper makes an attempt to present a model to predict rock fragmentation using Mutual Information (MI) in Meydook copper mine. Ten parameters are considered to influence fragmentation. On the other hand, Rock Engineering System (RES) is employed for sake of comparison between different methods. To validate the results, six blasting scenarios are selected out and compared with results of both models. The coefficients R2, RMSE and MAE were used in an attempt to assess the performance of presented models. The values of the coefficients R2, RMSE and MAE considering two methods of MI and RES for 30 blasting cycles are calculated as (0.81, 10.7, and 9.02) and (0.75, 11.87, and 9.61), respectively, implying the better capability of MI model to predict fragmentation

Feasibility of ICA in approximating ground vibration resulting from mine blasting

Precise prediction of blast-induced ground vibration is an essential task to reduce the environmental effects in the surface mines, civil and tunneling works. This research investigates the potential of imperialist competitive algorithm (ICA) in approximating ground vibration as a result of blasting at three quarry sites, namely Ulu Tiram, Pengerang and Masai in Malaysia. In ICA modeling, two forms of equations, namely power and quadratic, were developed. For comparison aims, several empirical models were also used. In order to develop the ICA and empirical models, maximum charge weight used per delay (W) and the distance between blasting sites and monitoring stations (D) were utilized as the independent variables, while, peak particle velocity (PPV), as a blast-induced ground vibration descriptor, was utilized as the dependent variable. Totally, 73 blasting events were monitored, and the values of W, D and PPV were carefully measured. Two statistical functions, i.e., root mean square error and coefficient of multiple determination (R2) were used to compare the performance capability of those prediction models. Simulation results show that the proposed ICA quadratic form can get more accurate predicting results than the ICA power form and empirical models

Prediction and minimization of blast-induced flyrock using gene expression programming and firefly algorithm

The main objective of blasting operations is to provide proper rock fragmentation and to avoid undesirable environmental impacts such as flyrock. Flyrock is the source of most of the injuries and property damage in a majority of blasting accidents in surface mines. Therefore, proper prediction and subsequently optimization of flyrock distance may reduce the possible damages. The first objective of this study is to develop a new predictive model based on gene expression programming (GEP) for predicting flyrock distance. To achieve this aim, three granite quarry sites in Malaysia were investigated and a database composed of blasting data of 76 operations was prepared for modelling. Considering changeable GEP parameters, several GEP models were constructed and the best one among them was selected. Coefficient of determination values of 0.920 and 0.924 for training and testing datasets, respectively, demonstrate that GEP predictive equation is capable enough of predicting flyrock. The second objective of this study is to optimize blasting data for minimization purpose of flyrock. To do this, a new non-traditional optimization algorithm namely firefly algorithm (FA) was selected and used. For optimization purposes, a series of analyses were performed on the FA parameters. As a result, implementing FA algorithm, a reduction of about 34 % in results of flyrock distance (from 60 to 39.793 m) was observed. The obtained results of this study are useful to minimize possible damages caused by flyrock

Settlement prediction of the rock-socketed piles through a new technique based on gene expression programming

The settlement design of bored piles socketed into rock has received considerable attention. Although many design methods of pile settlement are recommended in the literature, proposing new/practical technique(s) with higher performance prediction is of advantage. A new model based on gene expression programming (GEP) is presented in this paper for predicting the settlement of the rock-socketed pile. To do this, 96 piles socketed in different types of rock (mostly granite) as part of the Klang Valley Mass Rapid Transit project, Malaysia, were studied. In order to propose a predictive model with higher performance prediction, a series of GEP analyses were conducted using the most important factors on pile settlement, i.e. ratio of length in soil layer to length in rock layer, ratio of total length to diameter, uniaxial compressive strength, standard penetration test and ultimate bearing capacity. For comparison purpose, using the same dataset, linear multiple regression (LMR) technique was also performed. After developing the equations, their prediction performances were checked through several performance indices. The results demonstrated the feasibility of GEP-based predictive model of settlement. Coefficients of determination (CoD) values of 0.872 and 0.861 for training and testing datasets of GEP equation, respectively, show superiority of this model in predicting pile settlement while these values were obtained as 0.835 and 0.751 for the LMR model. Moreover, root mean square error (RMSE) values of (1.293 and 1.656 for training and testing) and (1.737 and 1.767 for training and testing) were achieved for the developed GEP and LMR models, respectively

Application of PSO to develop a powerful equation for prediction of flyrock due to blasting

Drilling and blasting is a widely-used method for rock fragmentation in open-pit mines, tunneling and civil projects. Flyrock, as one of the most dangerous effects induced by blasting, can cause substantial damage to structures and injury to human. Therefore, the ability to make proper predictions of flyrock distance is important to reduce and minimize the environmental side effects caused by blasting operation. The main goal of the present research is to develop a precise equation for predicting flyrock through particle swarm optimization (PSO) approach. For comparison purpose, multiple linear regression (MLR) was also used. In this regard, a database including several controllable blasting parameters was collected from 76 blasting events in three quarry sites, Malaysia. In modeling procedures, five effective parameters on the flyrock including burden, spacing, stemming, powder factor and rock density were used as input parameters, while flyrock was considered as output parameter. In order to check the performance of the developed models, several statistical functions, i.e., root-mean-square error, Nash and Sutcliffe and coefficient of multiple determination (R2), were computed. The results revealed that the proposed PSO equation is more reliable than MLR in predicting the flyrock. Based on sensitivity analysis results, it was also found that the RD was the most effective parameter on the flyrock in the studied cases