Prediction of Hydro-mechanical Stability of Dam: Using Calibrated Model from Back Analysis and Monitoring Data

Earth-fill dam safety and stability control during service period is important at the view point of economics and social security. Monitoring is very important to control dam security, to compare real action with predicted planning and to make good experience and opportunity for future planning. In this paper, water pore pressure and settlements in different parts of Sattarkhan dam during service period was studied. So at first, according to instrument data installed in the body of dam, calibration of numerical model done and by doing back analysis real properties of materials of dam defined. Then by using the calibrated model, pore water pressures and settlements of dam studied. Analysis carried out by Flac2D Finite Difference software. The constitutive model used was Mohr-Coulomb at the state of plane strain. Results showed that dam will be safe during service period at the view point of hydro-mechanical behaviour. Finally, stability of dam studied from the view of rapid depletion of the reservoir, which results showed safety conditions. 

Artificial neural network to predict the health risk caused by whole body vibration of mining trucks

Drivers of mining trucks are exposed to whole-body vibrations (WBV) and shocks during the various working cycles. These exposures have an adversely influence on the health, comfort and also working efficiency of drivers. Determination and prediction of the vibrational health risk of the mining haul trucks at thevarious operational conditions is the main goal of this study. To this aim, three haul roads with low, medium and poor qualities are considered based on the ISO 8608 standard. Accordingly, the vibration of a mining truck in different speeds, weights and distribution qualities of the materials in the dump body are evaluated for each haul road quality using the Trucksim software. An artificial neural network (ANN) is used to predict the vibrational health risk. The obtained results indicate that the haul road qualities, the truck speeds and the accumulation sides of material in the truck dump body have significant effects on the root mean square (RMS) of vertical vibrations. However, there is no significant relation between the material’s weight and the RMS values. Also, the application of ANN revealed that there is a good correlation between the predicted and simulated RMS values. The performance of the proposed neural network to predict the moderate and high health risk are 88.11% and 93.93% respectively</span

A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation

Point cloud analysis has a wide range of applications in many areas such as computer vision, robotic manipulation, and autonomous driving. While deep learning has achieved remarkable success on image-based tasks, there are many unique challenges faced by deep neural networks in processing massive, unordered, irregular and noisy 3D points. To stimulate future research, this paper analyzes recent progress in deep learning methods employed for point cloud processing and presents challenges and potential directions to advance this field. It serves as a comprehensive review on two major tasks in 3D point cloud processing-- namely, 3D shape classification and semantic segmentation.Comment: Published in Springer Nature (Machine Vision and Applications

A METHODOLOGY FOR TRUCK ALLOCATION PROBLEMS CONSIDERING DYNAMIC CIRCUMSTANCES IN OPEN PIT MINES, CASE STUDY OF THE SUNGUN COPPER MINE

Problem raspodjele kamiona smatra se jednim od najvažnijih čimbenika u postizanju planiranih proizvodnih kapaciteta u rudarstvu. Tradicionalne tehnike raspodjele kamiona (npr. matematičko programiranje, teorije čekanja u redu) podliježu različitim razinama pojednostavljenja u formuliranju stvarnoga prijevoza u heterogenim okolnostima. U ovome radu analiziran je problem raspodjele kamiona razvojem metode za optimizaciju raspodjele kamiona koja se temelji na simulaciji optimizacije (SBO) s obzirom na nesigurnosti tijekom rada kamionskoga voznog parka. Metoda osigurava integriranu strukturu simultanom kombinacijom optimizacije i simulacije stohastičkih diskretnih događaja. Ciljna je funkcija minimiziranje ukupnoga broja kamiona za transport sa simulacijom diskretnih događaja korištenih za modeliranje rubnih uvjeta. U ovome radu istražen je rad voznoga parka na primjeru rudnika bakra Sungun kako bi se postigla optimalna raspodjela kamiona pri različitim radnim operacijama na eksploatacijskome polju rudnika. Pojedinosti rada procijenjene su na temelju različitih pokazatelja kao što su iskorištenje, vrijeme čekanja i količina transportiranoga materijala za svaku radnu operaciju. K onačno, uska grla operacija prepoznata su za svaku situaciju.Truck allocation problems are considered as one of the most substantial factors in the achievement of planned production capacity in the mining industry. Traditional truck allocation techniques (e.g. mathematical programming, queueing theories) have undergone different levels of simplifications in formulating actual haulage operations under heterogeneous circumstances. In this study, the truck allocation problem is analysed through the development of the simulation-based optimization (SBO) method for the optimization of truck assignment considering uncertainties during fleet operation. This method provides an integrated structure by the simultaneous combination of optimization and stochastic discrete-event simulation. The objective function is to minimize the total number of trucks for haulage operation with discrete-event simulation employed to model the constraints. As a case study, the fleet operation of the Sungun copper mine is investigated to accomplish an optimal truck allocation for various working benches in the mine site. Operation details are evaluated through different indicators such as utilization, waiting times, and the amount of transported materials for each working bench. Finally, the operation bottlenecks are recognized for each situation

Long-term open-pit planning by ant colony optimization

The problem of long-term planning of a hard rock open pit mine (discontinuous exploitation operation) is a large combinatorial problem which cannot be solved in a reasonable amount of time through mathematical programming models because of its large size. In this thesis, a new metaheuristic algorithm has been developed based on the Ant Colony Optimization (ACO) and its application in long-term scheduling of a two dimensional hypothetical block model has been analysed. ACO is inspired by the foraging behaviour of ants (i.e. finding the shortest way from the colony to the food source), and has been successfully implemented in several combinatorial optimization problems. In nature, ants transmit a message to other members by laying down a trail with a chemical called pheromones. Instead of travelling in a random manner, the pheromone trail allows the ants to trace the path. Over time, the pheromones layed over longer paths evaporate, whereas those over shorter routes continue to be marched over. In order to simulate the ACO process for long-term planning of a hard rock open-pit mine, various programming variables have been considered for each block as the pheromone trails. The number of these variables is equal to the number of planning periods. In fact these pheromone trails represent the desirability of the block for being the deepest point of the mine in that column for the given mining period. The shape of any given pit (in respect to the slope angles) can be represented by means of a simple array of integer numbers. Each element in this array shows the depth of the pit in an individual column of block model. Extending this concept to a long-term production planning, a mine schedule would be represented by an array that has several mine depths at each column of block model related to different production periods. At the beginning, the values of the pheromone trails are initialized according to a mine schedule generated by Lerchs-Grossmann’s algorithm and the alternative to parameterization algorithm of Wang & Sevim. During initialization, relatively higher values of pheromones are assigned to those blocks that are close to the deepest points of the push backs in the initial mine schedule. This leads the procedure to construct a series of random schedules which are not far from the initial solution. In each ACO iteration, several mine schedules are constructed based on current pheromone trails. This is implemented through a process called “depth determination”. In this process the depth of a mine in each period is determined for each column of the block model. The higher the value of the pheromone trail of a particular block, the higher the possibility of selecting that block as the pit depth in that period. Subsequently the pheromone values of all blocks are reduced to a certain percentage (evaporation) and additionally the pheromone value of the participating blocks used in defining the constructed schedules are increased according to the quality of the generated solutions. Through repeated iterations, the pheromone values of the blocks which define the shape of the optimum solution are increased whereas those of the others have been significantly evaporated. The ACO optimization iterations could be implemented in a variety of ways. The Ant System (AS) is the first and simplest method, whereby all of the constructed schedules are allowed to contribute in the pheromone deposition. In each iteration of the second method, the Elitist Ant System (EAS), the best schedule found up to that iteration (the best-so-far schedule) is also allowed to deposit pheromones. ASrank is the third method in which only a few good schedules are able to add pheromones. The other variants are the Max-Min Ant System (MMAS) and the Ant Colony System (ACS), which allow only the best-so-far schedule to deposit pheromones and utilise special pheromone limitations in order to prevent the stagnation in local optimums. To test the efficiency of the algorithm, a computer program has been developed in Visual Basic 2005 programming language. As a case study, the block model of a hypothetical iron ore deposit with 1000 blocks was considered and different variants of ACO had been analysed in order to find the best combination of ACO parameters. The analysis revealed that the ACO is able to improve the value of the initial mining schedule by up to 34% in a reasonable computational time. This is mainly contributed to the consideration of the penalties to the deviations of the capacities and the production qualities from their permitted limits. It had also been proved that the MMAS is the most explorative variant, while ACS is the fastest method. These two variants also count as the only variants which could be applied to a large block model in respect to the amount of memory needed

Long-term open-pit planning by ant colony optimization

The problem of long-term planning of a hard rock open pit mine (discontinuous exploitation operation) is a large combinatorial problem which cannot be solved in a reasonable amount of time through mathematical programming models because of its large size. In this thesis, a new metaheuristic algorithm has been developed based on the Ant Colony Optimization (ACO) and its application in long-term scheduling of a two dimensional hypothetical block model has been analysed. ACO is inspired by the foraging behaviour of ants (i.e. finding the shortest way from the colony to the food source), and has been successfully implemented in several combinatorial optimization problems. In nature, ants transmit a message to other members by laying down a trail with a chemical called pheromones. Instead of travelling in a random manner, the pheromone trail allows the ants to trace the path. Over time, the pheromones layed over longer paths evaporate, whereas those over shorter routes continue to be marched over. In order to simulate the ACO process for long-term planning of a hard rock open-pit mine, various programming variables have been considered for each block as the pheromone trails. The number of these variables is equal to the number of planning periods. In fact these pheromone trails represent the desirability of the block for being the deepest point of the mine in that column for the given mining period. The shape of any given pit (in respect to the slope angles) can be represented by means of a simple array of integer numbers. Each element in this array shows the depth of the pit in an individual column of block model. Extending this concept to a long-term production planning, a mine schedule would be represented by an array that has several mine depths at each column of block model related to different production periods. At the beginning, the values of the pheromone trails are initialized according to a mine schedule generated by Lerchs-Grossmann’s algorithm and the alternative to parameterization algorithm of Wang & Sevim. During initialization, relatively higher values of pheromones are assigned to those blocks that are close to the deepest points of the push backs in the initial mine schedule. This leads the procedure to construct a series of random schedules which are not far from the initial solution. In each ACO iteration, several mine schedules are constructed based on current pheromone trails. This is implemented through a process called “depth determination”. In this process the depth of a mine in each period is determined for each column of the block model. The higher the value of the pheromone trail of a particular block, the higher the possibility of selecting that block as the pit depth in that period. Subsequently the pheromone values of all blocks are reduced to a certain percentage (evaporation) and additionally the pheromone value of the participating blocks used in defining the constructed schedules are increased according to the quality of the generated solutions. Through repeated iterations, the pheromone values of the blocks which define the shape of the optimum solution are increased whereas those of the others have been significantly evaporated. The ACO optimization iterations could be implemented in a variety of ways. The Ant System (AS) is the first and simplest method, whereby all of the constructed schedules are allowed to contribute in the pheromone deposition. In each iteration of the second method, the Elitist Ant System (EAS), the best schedule found up to that iteration (the best-so-far schedule) is also allowed to deposit pheromones. ASrank is the third method in which only a few good schedules are able to add pheromones. The other variants are the Max-Min Ant System (MMAS) and the Ant Colony System (ACS), which allow only the best-so-far schedule to deposit pheromones and utilise special pheromone limitations in order to prevent the stagnation in local optimums. To test the efficiency of the algorithm, a computer program has been developed in Visual Basic 2005 programming language. As a case study, the block model of a hypothetical iron ore deposit with 1000 blocks was considered and different variants of ACO had been analysed in order to find the best combination of ACO parameters. The analysis revealed that the ACO is able to improve the value of the initial mining schedule by up to 34% in a reasonable computational time. This is mainly contributed to the consideration of the penalties to the deviations of the capacities and the production qualities from their permitted limits. It had also been proved that the MMAS is the most explorative variant, while ACS is the fastest method. These two variants also count as the only variants which could be applied to a large block model in respect to the amount of memory needed

Long-term open-pit planning by ant colony optimization

The problem of long-term planning of a hard rock open pit mine (discontinuous exploitation operation) is a large combinatorial problem which cannot be solved in a reasonable amount of time through mathematical programming models because of its large size. In this thesis, a new metaheuristic algorithm has been developed based on the Ant Colony Optimization (ACO) and its application in long-term scheduling of a two dimensional hypothetical block model has been analysed. ACO is inspired by the foraging behaviour of ants (i.e. finding the shortest way from the colony to the food source), and has been successfully implemented in several combinatorial optimization problems. In nature, ants transmit a message to other members by laying down a trail with a chemical called pheromones. Instead of travelling in a random manner, the pheromone trail allows the ants to trace the path. Over time, the pheromones layed over longer paths evaporate, whereas those over shorter routes continue to be marched over. In order to simulate the ACO process for long-term planning of a hard rock open-pit mine, various programming variables have been considered for each block as the pheromone trails. The number of these variables is equal to the number of planning periods. In fact these pheromone trails represent the desirability of the block for being the deepest point of the mine in that column for the given mining period. The shape of any given pit (in respect to the slope angles) can be represented by means of a simple array of integer numbers. Each element in this array shows the depth of the pit in an individual column of block model. Extending this concept to a long-term production planning, a mine schedule would be represented by an array that has several mine depths at each column of block model related to different production periods. At the beginning, the values of the pheromone trails are initialized according to a mine schedule generated by Lerchs-Grossmann’s algorithm and the alternative to parameterization algorithm of Wang & Sevim. During initialization, relatively higher values of pheromones are assigned to those blocks that are close to the deepest points of the push backs in the initial mine schedule. This leads the procedure to construct a series of random schedules which are not far from the initial solution. In each ACO iteration, several mine schedules are constructed based on current pheromone trails. This is implemented through a process called “depth determination”. In this process the depth of a mine in each period is determined for each column of the block model. The higher the value of the pheromone trail of a particular block, the higher the possibility of selecting that block as the pit depth in that period. Subsequently the pheromone values of all blocks are reduced to a certain percentage (evaporation) and additionally the pheromone value of the participating blocks used in defining the constructed schedules are increased according to the quality of the generated solutions. Through repeated iterations, the pheromone values of the blocks which define the shape of the optimum solution are increased whereas those of the others have been significantly evaporated. The ACO optimization iterations could be implemented in a variety of ways. The Ant System (AS) is the first and simplest method, whereby all of the constructed schedules are allowed to contribute in the pheromone deposition. In each iteration of the second method, the Elitist Ant System (EAS), the best schedule found up to that iteration (the best-so-far schedule) is also allowed to deposit pheromones. ASrank is the third method in which only a few good schedules are able to add pheromones. The other variants are the Max-Min Ant System (MMAS) and the Ant Colony System (ACS), which allow only the best-so-far schedule to deposit pheromones and utilise special pheromone limitations in order to prevent the stagnation in local optimums. To test the efficiency of the algorithm, a computer program has been developed in Visual Basic 2005 programming language. As a case study, the block model of a hypothetical iron ore deposit with 1000 blocks was considered and different variants of ACO had been analysed in order to find the best combination of ACO parameters. The analysis revealed that the ACO is able to improve the value of the initial mining schedule by up to 34% in a reasonable computational time. This is mainly contributed to the consideration of the penalties to the deviations of the capacities and the production qualities from their permitted limits. It had also been proved that the MMAS is the most explorative variant, while ACS is the fastest method. These two variants also count as the only variants which could be applied to a large block model in respect to the amount of memory needed

Long-term open-pit planning by ant colony optimization

The problem of long-term planning of a hard rock open pit mine (discontinuous exploitation operation) is a large combinatorial problem which cannot be solved in a reasonable amount of time through mathematical programming models because of its large size. In this thesis, a new metaheuristic algorithm has been developed based on the Ant Colony Optimization (ACO) and its application in long-term scheduling of a two dimensional hypothetical block model has been analysed. ACO is inspired by the foraging behaviour of ants (i.e. finding the shortest way from the colony to the food source), and has been successfully implemented in several combinatorial optimization problems. In nature, ants transmit a message to other members by laying down a trail with a chemical called pheromones. Instead of travelling in a random manner, the pheromone trail allows the ants to trace the path. Over time, the pheromones layed over longer paths evaporate, whereas those over shorter routes continue to be marched over. In order to simulate the ACO process for long-term planning of a hard rock open-pit mine, various programming variables have been considered for each block as the pheromone trails. The number of these variables is equal to the number of planning periods. In fact these pheromone trails represent the desirability of the block for being the deepest point of the mine in that column for the given mining period. The shape of any given pit (in respect to the slope angles) can be represented by means of a simple array of integer numbers. Each element in this array shows the depth of the pit in an individual column of block model. Extending this concept to a long-term production planning, a mine schedule would be represented by an array that has several mine depths at each column of block model related to different production periods. At the beginning, the values of the pheromone trails are initialized according to a mine schedule generated by Lerchs-Grossmann’s algorithm and the alternative to parameterization algorithm of Wang & Sevim. During initialization, relatively higher values of pheromones are assigned to those blocks that are close to the deepest points of the push backs in the initial mine schedule. This leads the procedure to construct a series of random schedules which are not far from the initial solution. In each ACO iteration, several mine schedules are constructed based on current pheromone trails. This is implemented through a process called “depth determination”. In this process the depth of a mine in each period is determined for each column of the block model. The higher the value of the pheromone trail of a particular block, the higher the possibility of selecting that block as the pit depth in that period. Subsequently the pheromone values of all blocks are reduced to a certain percentage (evaporation) and additionally the pheromone value of the participating blocks used in defining the constructed schedules are increased according to the quality of the generated solutions. Through repeated iterations, the pheromone values of the blocks which define the shape of the optimum solution are increased whereas those of the others have been significantly evaporated. The ACO optimization iterations could be implemented in a variety of ways. The Ant System (AS) is the first and simplest method, whereby all of the constructed schedules are allowed to contribute in the pheromone deposition. In each iteration of the second method, the Elitist Ant System (EAS), the best schedule found up to that iteration (the best-so-far schedule) is also allowed to deposit pheromones. ASrank is the third method in which only a few good schedules are able to add pheromones. The other variants are the Max-Min Ant System (MMAS) and the Ant Colony System (ACS), which allow only the best-so-far schedule to deposit pheromones and utilise special pheromone limitations in order to prevent the stagnation in local optimums. To test the efficiency of the algorithm, a computer program has been developed in Visual Basic 2005 programming language. As a case study, the block model of a hypothetical iron ore deposit with 1000 blocks was considered and different variants of ACO had been analysed in order to find the best combination of ACO parameters. The analysis revealed that the ACO is able to improve the value of the initial mining schedule by up to 34% in a reasonable computational time. This is mainly contributed to the consideration of the penalties to the deviations of the capacities and the production qualities from their permitted limits. It had also been proved that the MMAS is the most explorative variant, while ACS is the fastest method. These two variants also count as the only variants which could be applied to a large block model in respect to the amount of memory needed

Rockfall Dynamics Prediction Using Data-Driven Approaches: A Lab-Scale Study

Predicting rockfall dynamics is essential for effective risk management and mitigation in mining and civil engineering, where uncontrolled rockfalls can have serious safety implications. This study explores machine learning (ML) approaches to model rockfall behavior, using experimentally derived data to predict key parameters: translational and angular velocity, coefficient of restitution (COR), and runout distance. Rockfall behavior is complex, influenced by factors such as rock shape and release angle, which create irregular, nonlinear patterns that challenge traditional modeling techniques. Three ML models&mdash;K-Nearest Neighbors (KNNs), Perceptron, and Deep Neural Networks (DNNs)&mdash;were initially tested for predictive accuracy. This study found that the Perceptron model could not capture the nonlinear intricacies of rockfall dynamics, while DNNs, though theoretically capable of handling complexity, faced issues with overfitting and interpretability due to limited data. KNNs emerged as the most effective model, offering a balance of accuracy and interpretability by using instance-based predictions to reflect localized patterns in rockfall behavior. Each parameter was modeled individually, leveraging KNNs&rsquo; strength in handling the dataset&rsquo;s unique characteristics without excessive computational requirements or extensive preprocessing. The results demonstrate that KNNs effectively predicts rockfall trajectories across diverse shapes and release angles, enhancing its practical application for safety and preventive strategies. This study contributes to the understanding of rockfall mechanics by providing an interpretable, adaptable model that meets the challenges posed by small, high-dimensional datasets and complex physical interactions