Predicting Blast-Induced Ground Vibrations in Some Indian Tunnels: a Comparison of Decision Tree, Artificial Neural Network and Multivariate Regression Methods

The present study compares three different techniques (decision tree, artificial neural network and multivariate regression analysis) for predicting blast-induced ground vibrations in some Indian tunnelling projects. The performance of these models was also compared to site-specific conventional predictor equations. A database consisting of 137 vibration records was randomly divided into training and testing sets for model generation. Eight input parameters (total charge, tunnel cross-section, maximum charge per delay, number of holes, hole diameter, distance from blasting face, hole depth and charge per hole) were selected for model development using bivariate correlation analysis. Results indicated that the decision tree is best suited for predicting vibrations. The decision tree further suggested that the intensity of near-field ground vibrations is mainly affected by total charge fired in a round, whereas the intensity of far-field vibrations is governed by maximum charge per delay and charge per hole. Conventional ground vibration predictors and machine learning techniques such as neural networks do not depict the relationship between input and output parameters. However, the present study substantiates that the decision tree can be a good tool for precise prediction of ground vibrations. Further, the decision tree can classify and relate different blast design parametersfor refining blast designs to control ground vibrations on site

Mathematical Problems in Rock Mechanics and Rock Engineering

With increasing requirements for energy, resources and space, rock engineering projects are being constructed more often and are operated in large-scale environments with complex geology. Meanwhile, rock failures and rock instabilities occur more frequently, and severely threaten the safety and stability of rock engineering projects. It is well-recognized that rock has multi-scale structures and involves multi-scale fracture processes. Meanwhile, rocks are commonly subjected simultaneously to complex static stress and strong dynamic disturbance, providing a hotbed for the occurrence of rock failures. In addition, there are many multi-physics coupling processes in a rock mass. It is still difficult to understand these rock mechanics and characterize rock behavior during complex stress conditions, multi-physics processes, and multi-scale changes. Therefore, our understanding of rock mechanics and the prevention and control of failure and instability in rock engineering needs to be furthered. The primary aim of this Special Issue “Mathematical Problems in Rock Mechanics and Rock Engineering” is to bring together original research discussing innovative efforts regarding in situ observations, laboratory experiments and theoretical, numerical, and big-data-based methods to overcome the mathematical problems related to rock mechanics and rock engineering. It includes 12 manuscripts that illustrate the valuable efforts for addressing mathematical problems in rock mechanics and rock engineering

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

Prediction of blast-induced ground vibration at a limestone quarry : an artificial intelligence approach

Ground vibration is one of the most unfavourable environmental effects of blasting activities, which can cause serious damage to neighboring homes and structures. As a result, effective forecasting of their severity is critical to controlling and reducing their recurrence. There are several conventional vibration predictor equations available proposed by different researchers but most of them are based on only two parameters, i.e., explosive charge used per delay and distance between blast face to the monitoring point. It is a well-known fact that blasting results are influenced by a number of blast design parameters, such as burden, spacing, powder factor, etc. but these are not being considered in any of the available conventional predictors and due to that they show a high error in predicting blast vibrations. Nowadays, artificial intelligence has been widely used in blast engineering. Thus, three artificial intelligence approaches, namely Gaussian process regression (GPR), extreme learning machine (ELM) and backpropagation neural network (BPNN) were used in this study to estimate ground vibration caused by blasting in Shree Cement Ras Limestone Mine in India. To achieve that aim, 101 blasting datasets with powder factor, average depth, distance, spacing, burden, charge weight, and stemming length as input parameters were collected from the mine site. For comparison purposes, a simple multivariate regression analysis (MVRA) model as well as, a nonparametric regression-based technique known as multivariate adaptive regression splines (MARS) was also constructed using the same datasets. This study serves as a foundational study for the comparison of GPR, BPNN, ELM, MARS and MVRA to ascertain their respective predictive performances. Eighty-one (81) datasets representing 80% of the total blasting datasets were used to construct and train the various predictive models while 20 data samples (20%) were utilized for evaluating the predictive capabilities of the developed predictive models. Using the testing datasets, major indicators of performance, namely mean squared error (MSE), variance accounted for (VAF), correlation coefficient (R) and coefficient of determination (R2) were compared as statistical evaluators of model performance. This study revealed that the GPR model exhibited superior predictive capability in comparison to the MARS, BPNN, ELM and MVRA. The GPR model showed the highest VAF, R and R2 values of 99.1728%, 0.9985 and 0.9971 respectively and the lowest MSE of 0.0903. As a result, the blast engineer can employ GPR as an effective and appropriate method for forecasting blast-induced ground vibration. © 2022 by the authors

Advances in Computational Intelligence Applications in the Mining Industry

This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and advances in the sciences, primarily in image processing applications. Authors in the book include both researchers and industry practitioners

Intelligence prediction of some selected environmental issues of blasting: A review

Background: Blasting is commonly used for loosening hard rock during excavation for generating the desired rock fragmentation required for optimizing the productivity of downstream operations. The environmental impacts resulting from such blasting operations include the generation of flyrock, ground vibrations, air over pressure (AOp) and rock fragmentation. Objective: The purpose of this research is to evaluate the suitability of different computational techniques for the prediction of these environmental effects and to determine the key factors which contribute to each of these effects. This paper also identifies future research needs for the prediction of the environmental effects of blasting operations in hard rock. Methods: The various computational techniques utilized by the researchers in predicting blasting environmental issues such as artificial neural network (ANN), fuzzy interface system (FIS), imperialist competitive algorithm (ICA), and particle swarm optimization (PSO), were reviewed. Results: The results indicated that ANN, FIS and ANN-ICA were the best models for prediction of flyrock distance. FIS model was the best technique for the prediction of AOp and ground vibration. On the other hand, ANN was found to be the best for the assessment of fragmentation. Conclusion and Recommendation: It can be concluded that FIS, ANN-PSO, ANN-ICA models perform better than ANN models for the prediction of environmental issues of blasting using the same database. This paper further discusses how some of these techniques can be implemented by mining engineers and blasting team members at operating mines for predicting blast performance

Numerical modeling and optimization of waterjet based surface decontamination

The mission of this study is to investigate the high-pressure waterjet based surface decontamination. Our specific objective is to develop a practical procedure for selection of process conditions at given constraints and available knowledge. This investigation is expected to improve information processing in the course of material decontamination and assist in the implementation of the waterjet decontamination technology into practice. The development of a realistic procedure for processing of a chaotic and non-accurate information constitutes the main accomplishment of this study. The research involved acquisition of representative information about removal of brittle, elastic and viscous deposits. As a result an extended database representing jet based decoating has been compiled and feasibility of the damage free decontamination of various surfaces including highly sensitive ones is demonstrated. Artificial Intelligence techniques (Fuzzy Logic, Artificial Neural Networks, Genetic Computing) have been applied for processing of the acquired information and a realistic procedure of such an application has been developed and demonstrated. This procedure enables us to integrate available information about surface in question and existing numerical models. The developed procedure allows a user to incorporate both qualitative (linguistic) and quantitative (crisp) information into a process model and to predict operational conditions for treatment of an unknown surface using a readily detectable single experimental parameter that characterizes a deposit/substrata combination. The suggested technique is shown to perform reliably in the case of incomplete and chaotic information, where the traditional regression based methods fail. Numerical simulations of the two-phase flow inside a waterjet nozzle are conducted. Numerical solutions of the partial differential equations of the two-phase turbulent jet flow are obtained using FLUENT package. The numerical prediction of jet velocity profiles and the interface between the two phases (water - air) inside a nozzle are in good agreement with experimental data available in the literature. Thus the current problem setup and the results of simulations can be applied to improvement in the nozzle design. A realistic procedure for the design of the jet based surfaces decontamination developed, as a result of this study, is applied for optimization of the removal of the paint, rust, tar and rubber from the steel surface

Capturing and characterising pre-failure strain on failing slopes

Effective management of slope hazards requires an understanding of the likely triggers, geometry, failure dynamics, mechanism and timing; of these the last two remain most problematic. Reducing the epistemic uncertainty of these elements is crucial, particularly for landslides that are not easily mitigated. The ‘inverse-velocity method’ utilises the linearity in inverse-strain-rate change through time in brittle materials to forecast the timing of final slope collapse. A significant body of published deformation data is available, yet to date there has been no attempt to collate a catalogue of landslide deformations from a large number of sites to examine emergent behaviour; notably variations in and controls on movement prior to failure. This thesis collates thirty-one examples of tertiary creep and related attributes from a broad literature search of over 6,000 peer-reviewed journals. Results show that tertiary creep operates over durations ranging from ~37 minutes to 3,171 days. Patterns of acceleration corroborated with published parameterisations of brittle failure; namely Voight’s (1989) model. Most examples (86%) were best-fit with hyperbolic curves, described by an α coefficient within the 1.7 and 2.2 range; indicative of deformation driven by crack growth. No significant relationships between slope and creep characteristics were found within the database of examples, however the lack of standard reporting of slope failures, particularly between industry documents and academic papers, limits the analysis. The database validates the ‘inverse-velocity method’ as a robust forecasting technique. Iterative a priori analysis of data has shown that slopes deforming in a brittle manner are more likely to predict slope collapse ‘too soon’ as a false positive prediction. Analysis has also shown that tertiary creep is typically delimited (87% of examples) within the first 25% of the total creep duration. Recommendations towards monitoring specifically highlight the need for instruments to deliver spatial accuracies to ~10mm, surface based capture and continuous measurement. Developing processing procedures for point cloud data derived from a permanent terrestrial laser scanning system is recommended as the best approach to small-scale deformation monitoring

Volume II: Mining Innovation

Contemporary exploitation of natural raw materials by borehole, opencast, underground, seabed, and anthropogenic deposits is closely related to, among others, geomechanics, automation, computer science, and numerical methods. More and more often, individual fields of science coexist and complement each other, contributing to lowering exploitation costs, increasing production, and reduction of the time needed to prepare and exploit the deposit. The continuous development of national economies is related to the increasing demand for energy, metal, rock, and chemical resources. Very often, exploitation is carried out in complex geological and mining conditions, which are accompanied by natural hazards such as rock bursts, methane, coal dust explosion, spontaneous combustion, water, gas, and temperature. In order to conduct a safe and economically justified operation, modern construction materials are being used more and more often in mining to support excavations, both under static and dynamic loads. The individual production stages are supported by specialized computer programs for cutting the deposit as well as for modeling the behavior of the rock mass after excavation in it. Currently, the automation and monitoring of the mining works play a very important role, which will significantly contribute to the improvement of safety conditions. In this Special Issue of Energies, we focus on innovative laboratory, numerical, and industrial research that has a positive impact on the development of safety and exploitation in mining