Optimization of blasting design in open pit limestone mines with the aim of reducing ground vibration using robust techniques

Blasting operations create significant problems to residential and other structures located in the close proximity of the mines. Blast vibration is one of the most crucial nuisances of blasting, which should be accurately estimated to minimize its effect. In this paper, an attempt has been made to apply various models to predict ground vibrations due to mine blasting. To fulfill this aim, 112 blast operations were precisely measured and collected in one the limestone mines of Iran. These blast operation data were utilized to construct the artificial neural network (ANN) model to predict the peak particle velocity (PPV). The input parameters used in this study were burden, spacing, maximum charge per delay, distance from blast face to monitoring point and rock quality designation and output parameter was the PPV. The conventional empirical predictors and multivariate regression analysis were also performed on the same data sets to study the PPV. Accordingly, it was observed that the ANN model is more accurate as compared to the other employed predictors. Moreover, it was also revealed that the most influential parameters on the ground vibration are distance from the blast and maximum charge per delay, whereas the least effective parameters are burden, spacing and rock quality designation. Finally, in order to minimize PPV, the developed ANN model was used as an objective function for imperialist competitive algorithm (ICA). Eventually, it was found that the ICA algorithm is able to decrease PPV up to 59% by considering burden of 2.9 m, spacing of 4.4 m and charge per delay of 627 Kg. © 2020, Springer Nature Switzerland AG

Geo-Mechanical Modelling for Optimization of Rock Slope in an Opencast Coal Mine

Jharia Coalfield, in India, is a prime storehouse of the coking coal. It contains as many as thirty contiguous seams. Multiplicity of seams has contributed to a number of problems, fire, inundated workings, goaf out area and disturbed strata condition. However, due to various geotechnical problems, it was not possible to fully extract coal by underground mining method. Opencast mining is now planned for extraction of virgin coal seams upto an ultimate depth of 500 m for ensuring maximum resources recovery. There are various modeling methods to analyze the behaviour of slopes. To achieve the objective for ensuring the safe slopes with steepest possible angle, the prototype of one of the mine was simulated in physical model i.e. Equivalent material model (EM) incorporating all the pertinent characteristics of rock mass, mining method and geological discontinuities properties. The results of EM are corroborated by Numerical method using Computer code FLAC- 2D. It was observed that when slope reached near the bottom seam the resultant vector of various monitoring points showed toppling tendency of slopes whereas high stress concentration was observed in the toe region and decreases towards the surface

Hybridizing five neural-metaheuristic paradigms to predict the pillar stress in bord and pillar method

Pillar stability is an important condition for safe work in room-and-pillar mines. The instability of pillars will lead to large-scale collapse hazards, and the accurate estimation of induced stresses at different positions in the pillar is helpful for pillar design and guaranteeing pillar stability. There are many modeling methods to design pillars and evaluate their stability, including empirical and numerical method. However, empirical methods are difficult to be applied to places other than the original environmental characteristics, and numerical methods often simplify the boundary conditions and material properties, which cannot guarantee the stability of the design. Currently, machine learning (ML) algorithms have been successfully applied to pillar stability assessment with higher accuracy. Thus, the study adopted a back-propagation neural network (BPNN) and five elements including the sparrow search algorithm (SSA), gray wolf optimizer (GWO), butterfly optimization algorithm (BOA), tunicate swarm algorithm (TSA), and multi-verse optimizer (MVO). Combining metaheuristic algorithms, five hybrid models were developed to predict the induced stress within the pillar. The weight and threshold of the BPNN model are optimized by metaheuristic algorithms, in which the mean absolute error (MAE) is utilized as the fitness function. A database containing 149 data samples was established, where the input variables were the angle of goafline (A), depth of the working coal seam (H), specific gravity (G), distance of the point from the center of the pillar (C), and distance of the point from goafline (D), and the output variable was the induced stress. Furthermore, the predictive performance of the proposed model is evaluated by five metrics, namely coefficient of determination (R2), root mean squared error (RMSE), variance accounted for (VAF), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results showed that the five hybrid models developed have good prediction performance, especially the GWO-BPNN model performed the best (Training set: R2 = 0.9991, RMSE = 0.1535, VAF = 99.91, MAE = 0.0884, MAPE = 0.6107; Test set: R2 = 0.9983, RMSE = 0.1783, VAF = 99.83, MAE = 0.1230, MAPE = 0.9253). Copyright © 2023 Zhou, Chen, Chen, Khandelwal, Monjezi and Peng

An Artificial Intelligence Approach for Tunnel Construction Performance

As massive tunneling projects become more and more popular, predicting the performance of Tunnel Boring Machine (TBM) has been a problem that arose recently. A TBM is a modern piece of machinery that is specially assembled to excavate a tunnel more efficiently and safely. However, the performance of TBM is very difficult to estimate due to the different geological formations and geotechnical factors. This research aims to predict the penetration rate (PR) of TBM utilizing statistical and artificial intelligence methods that are based on the rock mass and rock material properties: rock mass rating, rock quality designation, and rock strength. To achieve this goal, we used two neural network-based models: artificial neural network (ANN) and group method of data handling (GMDH), to forecast the TBM PR values. Then, we compared the performance of these two models using the well-known indices and a ranking system and selected the model with the highest degree of performance. As a result, an ANN model with one hidden layer and seven neurons showed the highest level of capability in predicting TBM PR. Correlation coefficient values of 0.947 and 0.921 for the training and testing phases, respectively, were obtained for the best model in this study. Our research can serve as a fundamental study for future geotechnical engineers or researchers who would like to predict TBM performance with similar rock mass and material properties to this study

Application of artificial neural network method in predicting contemporary Iranian family relationships

Virtual social networks play a very important role in social change, especially changes in the structural and emotional relationships of the family. But predicting these changes is very important today. But what method can be used to predict structural changes in the family? This study intends to introduce one of these methods, which is a subset of artificial intelligence, called artificial neural network and as an example to show its effectiveness in predicting the relationships of families affected by virtual social networks. Therefore, the research question is formulated in such a way that by what method or methods can the consequences of the impact of virtual social networks on family relationships be predicted? Since this research is a quantitative research, data collection was done by a questionnaire and the research model was operational. The research model is a fuzzy field statistical model. The result of the research is the prediction of four types of committed, unsuccessful, incompatible and broken families, which were shown on the fuzzy spectrum as follows: Committed family: 75 to 100%; Unsuccessful family: 50 to 75%; Incompatible family 25 to 50 percent and broken family 0 to 25 percent

Comparison and application of top and bottom air decks to improve blasting operations

The blasting operation is an integral part of mines, and it is still being used as the most economical tool to fragment and displace rock mass. Appropriate blast optimization alleviates undesirable side effects, such as ground vibration, air blasts and flyrock, and it and enhances rock fragmentation. Blast optimization can also be effective in reducing the overall mining cost. One way of reducing blasting side effects is to use deck charges instead of continuous ones. The location of the deck(s) is still considered an unanswered question for many researchers. In this study, an investigation was carried out to find an appropriate air deck position(s) within the blast hole. For this, air decks were placed at three different positions (top, middle and bottom) within a blast hole at Cheshmeh-Parvar gypsum and Chah-Gaz iron ore mines to understand and evaluate air deck location impact on blast fragmentation and blast nuisances. The results were compared based on the existing blasting practices at both mines, as well as the air-deck blasting results. The results obtained from the blasting were very satisfactory; it was found that charging with a top air deck, as compared to current blasting practices, causes a decrement in the specific charge, as well as a decrement of 38% in the back break and 50% in flyrock; the average size of fragments obtained from blasting was increased by 26%. Thus, it can be said that the top air deck is more advantageous than the bottom air deck in terms of reducing undesired blasting consequences

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

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 beings. In 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 algorithm. To 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 of 69 data sets recorded at strategic and vulnerable locations of GEG iron mine was used to train and test the generalization capability of ANN models. Coefficient of determination (R2) and mean square error (MSE) were chosen as the indicators of the performance of the networks. A network with architecture 4-11-5-1 and R2 of 0.957 and MSE of 0.000722 was found to be optimum. To demonstrate the supremacy of ANN approach, the same 69 data sets were used for the prediction of PPV with four common empirical models as well as multiple linear regression (MLR) analysis. The results revealed that the proposed ANN approach performs better than empirical and MLR models. © 2013 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences

Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network

The purpose of this article is to evaluate and predict blast-induced ground vibration at Shur River Dam in Iran using different empirical vibration predictors and artificial neural network (ANN) model. Ground vibration is a seismic wave that spreads out from the blasthole when explosive charge is detonated in a confined manner. Ground vibrations were recorded and monitored in and around the Shur River Dam, Iran, at different vulnerable and strategic locations. A total of 20 blast vibration records were monitored, out of which 16 data sets were used for training of the ANN model as well as determining site constants of various vibration predictors. The rest of the 4 blast vibration data sets were used for the validation and comparison of the result of ANN and different empirical predictors. Performances of the different predictor models were assessed using standard statistical evaluation criteria. Finally, it was found that the ANN model is more accurate as compared to the various empirical models available. As such, a high conformity (R 2 = 0.927) was observed between the measured and predicted peak particle velocity by the developed ANN model. © 2012 Springer-Verlag London Limited

Optimization of open pit mines fleet using Simulation-based optimization method (Case study: SONGUN copper mine)

In open pit mines the loaders and hauls choice , allocation and dispatching of trucks is one of the most important and complicated operational processes. That’s optimization results in great thrifts. For the analysis of this type of examples, simulation is a powerful tool.One of the basic points in the topic of simulation models is the presentment of optimization. Simulation based optimization methods are often one objective. In this Thesis it has been objected to use the multi objective forms of these methods. The considered goals are minimizing of operational cost and maximizing of production. Meta heuristic algorithms which is chosen for this issue Multi-Objective-Particle-Swarm-Optimization (MOPSO). For simulation we have used Arena’s software and for optimization used MATLAB software. The results of proposal methods implementation on Sungun mine’s loading and hauling fleet, shows that with the administration of obtained scenarios it is possible to maximize the current production of the mine 2.5 times and to reduce the operational costs of production up to 23

EFFICIENCY IMPROVEMENT OF THE SONGUN COPPER MINE FLEET USING SIMULATION TECHNIQUE

Fleet selection in open-pit mines is of vital importance. Inappropriate selection of fleet can be influential on the project economics in two ways failure in achieving production objectives and increase the attributed costs due to increase in the shipment time. Usually fleet design is fulfilled in the forms of either allocating or dispatching aiming to production maximization and cost minimization. Simulation technique, one of the available fleet selection methods, allows designers to study a system in a virtual environment without executing it in the real world, to technically and economically evaluate its efficiency. In this paper, at first using the simulation technique, transportation system of the Songun copper mine was simulated in the Arena software and then by defining new systems, efficiency of the available fleet was improved. On the basis of the obtained results it was observed that a fleet of 100 t dump trucks, 6.1 m3 front end loaders, 7.0 m3 shovel and 4.6 m3 excavator with weekly production of 550,000 tones and 0.83$ per/ m3 is the best possible alternative