NOVA METODA ZA PROJEKTIRANJE FAZNOGA RAZVOJA KOPA PRI GEOLOŠKIM NESIGURNOSTIMA BAZIRANA NA ALGORITMU OPTIMIZACIJE KOLONIJOM MRAVA

An essential task in the open-pit mine optimizing process is determining the extraction time of material located in the ultimate pit, considering some operational and economic constraints. The proper design of pushbacks has a significant impact on the optimum production planning. On the other hand, some uncertainty sources such as in-situ grade cause both deviations from production and financial goals. This paper presents an extension of a multi-stage formulation for risk-based pushback designing that utilizes the ant colony optimization (ACO) algorithm to solve it. For more detailed studies, two different strategies were developed according to statistical and probabilistic issues. The data of Songun copper mine located in NW Iran was used to evaluate the ability of the proposed approach in controlling the risk of deviation from production targets and increasing the project value. The results indicated the effectiveness of the proposed approach in pushback designing based on geological uncertainty. Examining different strategies showed that the technique based on multiple probability produces better solutions.Uzimajući u obzir neka operativna i ekonomska ograničenja, u procesu optimizacije površinskoga kopa bitan je zadatak određivanje vremena eksploatacije materijala koji se nalazi na najdubljoj etaži. Pravilan dizajn veličine zahvata etaže ima znatan utjecaj na optimalno planiranje proizvodnje. S druge strane, neki izvori nesigurnosti, kao što su terenske nepoznanice, uzrokuju odstupanja od proizvodnih i financijskih ciljeva. Ovaj članak predstavlja proširenje višesegmentnoga modeliranja za projektiranje faznoga razvoja kopa temeljenoga na riziku koji se za rješavanje koristi algoritmom optimizacije kolonijom mrava (eng. ant colony optimization, ACO). Za detaljnije proučavanje razvijene su dvije različite strategije prema statističkim i probabilističkim načelima. Za procjenu sposobnosti predloženoga pristupa u kontroli rizika odstupanja od proizvodnih ciljeva i povećanja troškova projekta korišteni su podatci iz rudnika bakra Songun koji se nalazi u sjeverozapadnome Iranu. Rezultati su pokazali učinkovitost predloženoga pristupa u projektiranju faznoga razvoja kopa kod geološke nesigurnosti. Ispitivanje različitih strategija pokazalo je kako metoda višestruke vjerojatnosti daje bolje rezultate

A New Inversion Method Using a Modified Bat Algorithm for Analysis of Seismic Refraction Data in Dam Site Investigation

Similar to any other geophysical method, seismic refraction method faces non-uniqueness in the estimation of model parameters. Recently, different nonlinear seismic processing techniques have been introduced, particularly for seismic inversion. One of the recently developed metaheuristic algorithms is bat optimization algorithm (BA). Standard BA is usually quick at the exploitation of the solution, while its exploration ability is relatively poor. In order to improve exploration ability of BA, in the current study, a hybrid metaheuristic algorithm by inclusion a mutation operator into BA, so-called mutation based bat algorithm (MBA), is introduced to inversion of seismic refraction data. The efficiency and stability of the proposed inversion algorithm were tested on different synthetic cases. Finally, the MBA inversion algorithm was applied to a real dataset acquired from Leylanchay dam site at East-Azerbaijan province, Iran, to determine alluvium depth. Then, the performance of MBA on both synthetic and real datasets was compared with standard BA. Moreover, the dataset was further processed following a tomographic approach and the results were compared to the results of the proposed MBA inversion method. In general, the MBA inversion results were superior to standard BA inversion and results of MBA were in good agreement with available boreholes data and geological sections at the dam site. The analysis of the seismic data showed that the studied site comprises three distinct layers: a saturated alluvial, an unsaturated alluvial, and a dolomite bedrock. The measured seismic velocity across the dam site has a range of 400 to 3,500 m/s, with alluvium thickness ranging from 5 to 19 m. Findings showed that the proposed metaheuristic inversion framework is a simple, fast, and powerful tool for seismic data processing. </jats:p

Seismic refraction data analysis using machine learning and numerical modeling for characterization of dam construction sites

Seismic refraction is a cost-effective tool to reveal subsurface compressional wave (P-wave) velocity. Inversion of traveltimes for estimating a realistic velocity model is a significant step in the processing of seismic refraction data. The results of the seismic data inversion are stochastic; thus, using prior information or complementary geophysical data can have a significant role in estimating the structural properties based on the observed data. Nevertheless, sufficient prior information or auxiliary data are not available in many geophysical sites. In such situations, developing advanced computational modeling is a vital step in providing primary information and improving the results. To this aim, a new inversion framework through hybrid committee artificial neural networks (CANNs) and the flower pollination (FP) optimization algorithm is introduced for inversion of refracted seismic traveltimes. Synthetic models generated by a forward-modeling approach are used to train the machine-learning model. Then, model parameters, such as the number of layers, thicknesses, and P-wave velocities, are predicted using a committee machine constructed based on several neural networks, which is achieved by averaging and stack generalization methods in which the latter method provides a better result. Then, the CANN results are used in the FP inversion algorithm to estimate the final model because it provides essential prior information on the number of layers and model parameters, which can be used in the FP searching algorithm. Our inversion procedure is tested on different synthetic data sets and applied at a dam site to determine the number of layers and their thicknesses. Our findings indicate a successful performance on synthetic and real data for automatic inversion of seismic refraction data. </jats:p

An ANN-Fuzzy Cognitive Map-Based Z-Number Theory to Predict Flyrock Induced by Blasting in Open-Pit Mines

AbstractBlasting is widely employed as an accepted mechanism for rock breakage in mining and civil activities. As an environmental side effect of blasting, flyrock should be investigated precisely in open-pit mining operations. This paper proposes a novel integration of artificial neural network and fuzzy cognitive map (FCM) with Z-number reliability information to predict flyrock distance in open-pit mine blasting. The developed model is called the artificial causality-weighted neural networks, based on reliability (ACWNNsR). The reliability information of Z-numbers is used to eliminate uncertainty in expert opinions required for the initial matrix of FCM, which is one of the main advantages of this method. FCM calculates weights of input neurons using the integration of nonlinear Hebbian and differential evolution algorithms. Burden, stemming, spacing, powder factor, and charge per delay are used as the input parameters, and flyrock distance is the output parameter. Four hundred sixteen recorded basting rounds are used from a real large-scale lead–zinc mine to design the architecture of the models. The performance of the proposed ACWNNsR model is compared with the Bayesian regularized neural network and multilayer perceptron neural network and is proven to result in more accurate prediction in estimating blast-induced flyrock distance. In addition, the results of a sensitivity analysis conducted on effective parameters determined the spacing as the most significant parameter in controlling flyrock distance. Based on the type of datasets used in this study, the presented model is recommended for flyrock distance prediction in surface mines where buildings are close to the blasting site.</jats:p