m. monjezi

The occurrence of settlement induced by tunneling operations in urban environments is an inevitable phenomenon. The risk of damage to nearby infrastructures and surface structures can be greatly reduced by predicting and controlling this event. This paper uses multiple linear regression (MLR) model and random forest (RF) algorithm to predict the maximum surface settlement (Smax) due to shallow tunnel excavation. Nine input parameters, including the distance of the tunnel center from the ground surface (H), height of the underground water level above the tunnel (W.T), tunnel diameter (D), elastic modulus of soil (E), undrained shear strength of soil (Cu), earth pressure coefficient (K0), unit weight of soil (γ), gap parameter (g), and stability number (N) were selected from 24 data sets related to 14 tunneling projects. The MLR and RF techniques were then implemented for predicting Smax. Three performance indicators of coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) were employed for the training and test phases to evaluate the efficiency of the suggested models. The coefficient of determination values for MLR and RF for training data were 0.814 and 0.957, respectively, while for test data were 0.793 and 0.96, indicating that the RF approach is more efficient than MLR. Moreover, the findings reveal that the RF algorithm exhibits lower RMSE and MAE values in both the training and testing phases compared to the MLR method. This suggests that the RF algorithm exhibits reduced error and higher reliability and accuracy when compared to the MLR model. Also, the performance study demonstrates that among the input parameters, the gap parameter (g) and the undrained shear strength of soil (Cu) have the greatest and least influence on Smax, respectively.

Loading and haulage operation in open pit mines is the last stage of the mining process. truck- shovel system, due to its many advantages  including high flexibility, is preferred for this operation. Due to high operating costs, proper fleet management and optimization can significantly affect the project economics. Truck allocation and dispatching issue is a very complex problem, especially in large mines with numerous loading and dumping points. Because of the problem size and complexity, employing mathematical methods is not justified due to very high solution time which leads to employing super computers. To overcome the aforesaid shortcoming, heuristic algorithms can be applied. In this paper, in MATLAB environment, a heuristic algorithm was developed to solve allocation and dispatching problem of transportation fleet of a real mine. According to the obtained results, a running time of 39 seconds was computed for the heuristic algorithm. Finally, the same problem was solved with an available mathematical model with a running time of 24 hours which shows the superiority of the proposed algorithm over the mathematical modeling.

The production cycle in open-pit mines includes the drilling, blasting, loading, and haulage. Since loading and haulage account for a large part of the mining costs, it is very important to optimize the transport fleet from the economic viewpoint. Simulation is one of the most widely used methods in the field of fleet design. However, it is unable to propose an optimized scenario for which the appropriate metaheuristic method should be employed. This paper considers the Sungun copper mine as the case study, and attempts to find the most feasible transportation arrangement. In the first step, in this work, we compare the flexible dispatching with the fixed allocation methods using the Arena software. Accordingly, the use of flexible dispatching reveals the increase in the production rate (20%) and productivity (25%), and the decrease (20%) in the idle time. The firefly metaheuristic algorithm used in the second step shows that the combined scenario of the 35-ton and 100-ton trucks is the most suitable option in terms of productivity and cost. In another attempt, comparing different heterogeneous truck fleets, we have found that the scenarios 35-100 and 35-60-100-144 increase the production rate by 39% and 49%, respectively. Also, in both scenarios, the production cost decreases by 11% and 21%, respectively.

In each project, there is always a possibility of occurrence of hazards and risks. Accidents cause many damages such as financial and psychological problems, that may have a negative effect on the workers life. To prevent or reduce the occurrence of incidents, it is necessary to identify and manage the relevant affecting factors. Blasting is one of the events that has frequently led to accidents. In this paper, 13 factors affecting the occurrence of blasting related accidents in the mining and construction projects, have been selected according to the opinion of experts and ranked to identify the most important one. For this purpose, Monte Carlo simulation method and analytical hierarchy process method were implemented. The factors were ranked based on the opinion of 15 experts in this field. Finally, based on the obtained results, inappropriate blasting time, unprofessional personnel were selected as the most important factors. Also, blasting pattern specifications, type of explosive and rock mass characteristics have the least effect in this regard. Validity of the paper outcomes was checked with comparing the real recorded events.

Waste rock dumping is very important in the production planning of open-pit mines. This subject is more crucial when there is a potential of acid-forming (PAF) by waste rocks. In such a type of mines, to protect the environment, the PAF materials should be encapsulated by non-harmful rocks. Therefore, block sequencing of the mined materials should be in such a way that both the environmental and economic considerations are considered. If non-acid forming (NAF) rocks are not mined in a proper time, then a stockpile is required for the NAF materials, which later on would be re-handled for encapsulation of PAF rocks. In the available models, the focus is on either block sequencing or waste dumping strategy. In this work, an attempt has been made to develop an integrated mathematical model for simultaneous optimization of block sequencing and waste rock dumping. The developed model not only maximizes the net present value (NPV) but also decreases the destructive environmental effects of inappropriate waste dumping. The proposed model, which is solved by a CPLEX engine, is applied to two different iron deposits. Also the performance of the proposed model is cross-checked by applying the available (traditional) models in a two-step manner. According to the results obtained, it can be considered that utilizing the developed model, because of extensive re-handling cost reduction, the NPV improvement is significant, especially when the overall stripping ratio is higher (deposit case A).