Multivariate Mapping of Heavy Metals Spatial Contamination in a Cu–Ni Exploration Field (Botswana) Using Turning Bands Co-simulation Algorithm

With a mining-driven economy, Botswana has experienced increased geochemical exploration of minerals around existing mining towns. The mining and smelting of copper and nickel around Selibe-Phikwe in the Central Province are capable of releasing heavy metals including Pb, Fe, Mn, Co, Ni and Cu into the soil environments, thereby exposing humans, plants and animals to health risks. In this study, turning bands co-simulation, a multivariate geostatistical algorithm, was presented as a tool for spatial uncertainty quantification and probability mapping of cross-correlated heavy metals (Co, Mn, Fe and Pb) risk assessment in a semiarid Cu–Ni exploration field of Botswana. A total of 1050 soil samples were collected across the field at a depth of 10 cm in a grid sampling design. Rapid elemental concentration analysis was done using an Olympus Delta Sigma portable X-ray fluorescence device. Enrichment factor, geoaccumulation index and pollution load index were used to assess the potential risk of heavy metals contamination in soils. The partially heterotopic nature of the dataset and strong correlations among the heavy metals favors the use of co-simulation instead of independent simulation in the probability mapping of heavy metal risks in the study area. The strong correlation of Co and Mn to iron infers they are of lithogenic origin, unlike Pb which had weak correlation pointing to its source in the area being of anthropogenicsource. Manganese, Co and Fe show low enrichment, whereas Pb had high enrichment suggesting possible lead pollution. We, however, recommend that speciation of Pb in the soils rather than total concentration should be ascertained to infer chances of possible bioaccumulation, and subsequent health risk to human by chronic exposure.Nazarbayev University through Faculty Development Competitive Research Grants for 2018–2020 under Contract No. 090118FD5336

Generating Irregular Models for 3D Spherical-Particle-Based Numerical Methods

The realistic representation of an irregular geological body is essential to the construction of a particle simulation model. A three-dimensional (3D) sphere generator for an irregular model (SGIM), which is based on the platform of Microsoft Foundation Classes (MFC) in VC++, is developed to accurately simulate the inherent discontinuities in geological bodies. OpenGL is employed to visualize the modeling in the SGIM. Three key functions, namely, the basic-model-setup function, the excavating function, and the cutting function, are implemented. An open-pit slope is simulated using the proposed model. The results demonstrate that an extremely irregular 3D model of a geological body can be generated using the SGIM and that various types of discontinuities can be inserted to cut the model. The data structure of the model that is generated by the SGIM is versatile and can be easily modified to match various numerical calculation tools. This can be helpful in the application of particle simulation methods to large-scale geoengineering projects

Bayesian prediction of TBM penetration rate in rock mass

Abstract One of the essential tasks in the excavation of tunnels with TBM is the reliable estimation of its performance needed for the planning, cost control and other decision making on the feasibility of the tunneling project. The current study aims at predicting the rate of penetration (RoP) of TBM on the basis of the rock mass parameters including the uniaxial compressive strength (UCS), intact rock brittleness (BI), the angle between the plane of weakness and the TBM driven direction (α) and the distance between planes of weakness (DPW). To this end, datasets from the Queens Water Tunnel No. 3 project, New York City, are compiled and used to establish the models. The Bayesian inference approach is implemented to identify the most appropriate models for estimating the RoP among eight (8) candidate models that have been proposed. The selected TBM empirical models are fitted to field data. The unknown parameters of the models are considered as random variables. The WinBUGS software which uses Bayesian analysis of complex statistical models and Markov chain Monte Carlo (MCMC) techniques is employed to compute the posterior predictive distributions. The mean values of the model parameters obtained via MCMC simulations are considered for the model prediction performance evaluation. Meanwhile, the deviance information criterion (DIC) is used as the main prediction accuracy indicator and therefore, to rank the models taking into account both their fit and complexity. Overall, the results indicate that the proposed RoP model possesses satisfactory predictive performance

Spatial Mapping of the Rock Quality Designation Using Multi-Gaussian Kriging Method

The rock quality designation is an important input for the analysis and design of rock structures as reliable spatial modeling of the rock quality designation (RQD) can assist in designing and planning mines more efficiently. The aim of this paper is to model the spatial distribution of the RQD using the multi-Gaussian kriging approach as an alternative to the non-linear geostatistical technique which has shown some limitations. To this end, 470 RQD datasets were collected from 9 boreholes pertaining to the Gazestan ore deposit in Iran. The datasets were declustered then transformed into Gaussian distribution. To ensure the model spatial continuity, variogram analysis was first performed. The elevation 150 m with a grid of 5 m × 5 m × 5 m was selected to illustrate the methodology. Surface maps showing the RQD classes (very poor, poor, fair, good, and very good) with their associated probability were established. A cross-validation method was used to check the obtained model. The validation results indicated good prediction of the local variability. In addition, the associated uncertainty was quantified on the basis of the conditional distributions and the accuracy plot agreed with the overall results. It is concluded that the proposed model could be used to produce a reliable RQD map

Unplanned dilution prediction in open stope mining: developing new design charts using Artificial Neural Network classifier

Minimizing dilution is essential in open stope mine design as excessive unplanned dilution can compromise the operation's profitability. One of the main challenges associated with the empirical dilution graph method used to design open stopes is how to determine the boundary of the dilution zones objectively. Hence, this paper explores the implementation of machine learning classifiers to bridge this gap in the conventional dilution graph method. Stope performance data consisting of the stope dilution (unplanned dilution), the modified stability number, and the hydraulic radius were compiled from a mine located in Kazakhstan. First, the conventional dilution graph methods were used to assess the dilution. Next, a Feed-Forward Neural Network (FFNN) classifier was implemented to predict each level of dilution. Overall, the FFNN results indicated that 97% of the stope surfaces were correctly classified, indicating an excellent classification performance, while the conventional dilution graph method did not show a good performance. In addition, the outputs of the FFNN were used to plot new dilution graphs with a probabilistic interpretation illustrating its practicability. It was concluded that the FFNN-based classifier could be a useful tool for open stope design in underground mines

APPLICATION OF SOFT COMPUTING TECHNIQUES TO ESTIMATE CUTTER LIFE INDEX USING MECHANICAL PROPERTIES OF ROCKS

The wear of cutting tools is critical for any engineering applications dealing with mechanical rock excavations, as it directly affects the cost and time of project completion as well as the utilization rate of excavators in various rock masses. The cutting tool wear could be expressed in terms of the life of the tool used to excavate rocks in hours or cutter per unit volume of excavated materials. The aim of this study is to estimate disc cutter wear as a function of common mechanical rock properties including uniaxial compressive strength, Brazilian tensile strength, brittleness, and density. To achieve this goal, a database of cutter life was established by analyzing data from 80 tunneling projects. The data were then utilized for evaluating the relationship between rock properties and cutter consumption by means of cutter life index. The analysis was based on artificial intelligence techniques, namely artificial neural networks (ANN) and fuzzy logic (FL). Furthermore, linear and non-linear regression methods were also used to investigate the relationship between these parameters using a statistical software package. Several alternative models are introduced with different input variables for each model, to identify the best model with the highest accuracy. To develop these models, 70% of the dataset was used for training and the rest, for testing. The estimated cutter life by various models was compared with each other to identify the most reliable model. It appears that the ANN and FL techniques are superior to standard linear and non-linear multiple regression analysis, based on the higher correlation coefficient (R2 ) and lower Mean square error (MSE)

Stability assessment of underground mine stopes subjected to stress relaxation

Stress relaxation plays an important role in the design of underground stopes. The aim of this paper is to assess the stope stability in connection with the stress relaxation using a classification approach. Three types of stress relaxation were clearly defined, namely partial relaxation, tangential relaxation and full relaxation. A neural network classifier was implemented to assess the stability of the stopes on the basis of case histories of stope performances. The results of the classification were compared to existing empirical methods of quantifying the stress relaxation. Overall, the present study shows higher classification accuracies, especially when the stress relaxation was considered. The results suggested that the relaxation type can be a good predictor of stability. Relaxed stope (full and tangential stress relaxation) cases are the most critical in the sense that lower accuracies were obtained and the probability of correct classification is rather erratic.Faculty Development Competitive Research Grant program of Nazarbayev University: 090118FD5338. Advanced Mining Technology Center (AMTC), University of Chile, through the Basal Project: FB-0809

A FEASIBILITY STUDY ON THE IMPLEMENTATION OF NEURAL NETWORK CLASSIFIERS FOR OPEN STOPE DESIGN

Assessing the stability of stopes is essen tial in open stope mine design as unstable hangingwalls and footwalls lead to sloughing, unplanned stope dilution, and safety concerns compromising the prof itability of the mine. Over the past few decades, numerous empirical tools have been developed to dimension open stope in connection with its stability, using the stability graph method. However, one of the principal limitations of the stability graph method is to objectively determine the boundary of the stability zones, and gain a clear probabilistic interpretation of the graph. To overcome this issue, this paper aims to explore the feasibility of artificial neural network (ANN) based classifiers for the design of open stopes. A stope stability database was compiled and included the stope dimensions, rock mass properties, and the stope stability conditions. The main parameters included the modified stability number (N’), and the stope stability conditions (stable, unstable, and failed), and hydraulic radius (HR). A feed-forward neural network (FFNN) classifier containing two hidden layers (110 neurons each) was employed to identify the stope stability conditions. Overall, the outcome of the analysis showed good agreement with the field data; most stope surfaces were correctly predicted with an average accuracy of 91%. This shows an improvement over using the existing stability graph method. In addition, for a better interpretation of the results, the associated probability of occurrence of stable, unstable, or caved stope was determined and shown in iso-probability contour charts which were compared with the stability graph. The proposed FFNN-based classifier outperformed the conventional stability graph method in terms of accuracy and better prabablistic interpretation. It is suggested that the classifier could be a reliable tool that can complement the conventional stability graph for the design of open stopes