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Crown pillar is a horizontal pillar left to protect the upper level workings when stope advances along the up dip direction and approaches to the upper level in cut and fill method of mining. These horizontal pillars are the main support structures for stopes during excavation. In this study the vertical movement of the pillars at crown level has been analysed by varying the rock mass parameters such as geological strength index (GSI), uniaxial compressive strength (UCS or fãci), modulus of elasticity (E), depth of pillar from surface (D) and thickness of horizontal pillar (T) using finite element method. These analyses have been conducted based on 135 non-linear numerical models considering Drucker-Prager failure criterion in plane strain condition. Results of finite element models are represented in terms of displacements in rock mass of the pillars. Displacement profiles of rock mass along the predefined paths are obtained, presented and analyzed for different models having variation in geo-mining conditions. Finally, the most important parameters affecting the convergence significantly are identified which may be incorporated in the design of the optimum crown pillar thickness. [ABSTRACT FROM AUTHOR]

Traffic congestion, pollution due to vehicular emissions and fuel energy crises are the major problems that have inspired research for the design of efficient and smaller vehicles as a regular mode of transport with a significantly lower carbon footprint. With an increased dependency on certain countries for fuel energy, electric mobility has been encouraged by policy makers so that national interests are not compromised. At the same time, enhanced standards of living have resulted in a demand for safer and comfortable transport. All of these factors have led to a need for a mode of transport that is efficient, compact, safe and comfortable. Electric three-wheelers are seen as one of the promising options that fits all of these requirements. Due to their high propensity to rollover during cornering, however, the use of three-wheeled vehicles (3Ws) is limited to low-speed applications for cheap transport. Recent developments in the field of rollover mitigation through active tilt control mechanisms indicate that the future mode of transport will be more exciting than the existing one. This paper is a review mainly focused on the recent technological developments in active-tilt-controlled three-wheeled vehicles. This paper gives a detailed review of the methodologies chosen, control strategies adopted, types of vehicle chosen for test/simulation, mathematical models applied, key findings and future work proposed to address the dynamics-related issues of tilting 3Ws. [ABSTRACT FROM AUTHOR]

Simultaneous localisation and mapping by using a robot is indeed a challenging and critically important problem for an accurate 3D underground mine mapping. Using 6D pose information of a robot (x,yandzcoordinates and the yaw, pitch and roll angles), the robot vehicle can be tracked, which is very crucial for an accurate mine mapping. The estimation of 6D robot pose on the ground surface is easy and generally accomplished by either the Global Positioning System (GPS) or by putting some artificial visual landmarks. However, in the underground mining environment, the estimation of pose becomes difficult due to the unavailability of the GPS signal and the identification of landmarks (as surface wall of the underground mine is rough). In this paper, an attempt has been made to construct a 3D underground mine map using a prototype mine robot. For this purpose, a strategy for the underground mine robot pose estimation is devised with the help of multi-sensor data fusion techniques. A shaft-encoder (wheel odometry), Inertial Measurement Unit (IMU) and a rotated 2D Laser Scanner is used to improve and rectify the 6D robot pose as well as 3D mine map. An autonomous prototype robot is built up and allowed to navigate in a real underground mine gallery to validate our algorithm. Generated 3D map has been compared with the actual map available. The outputs of this study are highlighted in this paper. [ABSTRACT FROM AUTHOR]

Evaluation of flow and transport processes in a watershed-scale requires that the watershed be divided into homogenous spatial units referred to as hydrologically similar units (HSUs). Although a few discretization schemes are already in use, a universally acceptable method of obtaining HSUs is yet to emerge. In this study, we developed a fuzzy inference system (FIS) to classify the saturated hydraulic conductivity ( K) and two water-retention parameters α and n into fuzzy logic-based soil hydrologic classes (FSHCs). Analysis of these classes showed that soil properties within an FSHC have less variability and those between two FSHCs have large variability. This result suggested that soils belonging to a specific FSHC may be more similar than those across different FSHCs and may be grouped together to represent an HSU. Soils within a specific hydrologic class were aggregated to delineate HSUs within the watershed. For the Dengei Pahad micro-watershed (DPW), this approach showed five distinct regions representing a discretized zone having similar soil hydraulic properties. Application of this approach on a larger international database of soil hydraulic properties revealed that the developed hydrologic classes are quite comparable across different databases. The delineated HSUs based on these FSHCs were also better than the soil series map of the watershed in maintaining the soil heterogeneity of the watershed. Moreover, this new discretization scheme using the SWAT modelling environment showed better performance than the soil series-based discretization approach. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Discontinuum based particle-particle interaction mechanics is applied to the modeling of waste composed of fragmented rock and loose soil produced at the opencast coal mines. The distinct element method considered the discrete nature of geomaterials and represented them with bonded and unbonded matrix assembly. The overburden dump material is studied with laboratory test of synthetic material, similar to physical testing and calibration done through that. The next part focused on the static stability characteristics of the overburden dumps with heavily joint set and performance study was done on the ability to model it. The location of sliding is visualized via numerical code. The last part deals with the performance characteristics of the overburden dumps under seismic loads. The history of the responses at monitoring points is studied for interpretation of the dynamic behavior of the overburden dump mass. The responses are used to determine the natural frequency of the overburden dumps, damping fraction and the phase difference at two sides of the dumps mass. The results obtained match well with fundamental concept of the vibration theory. [ABSTRACT FROM AUTHOR]

The active tilt-controlled narrow electric three-wheelers offer an encouraging solution to pressing environmental and urban challenges, including air and noise pollution, parking shortage, and traffic congestion while improving their rollover stability. The previous research on active tilt control established a desired tilt angle neglecting track width which helps to bear unbalanced lateral acceleration that leads to rollover threat. This article proposes a dynamic rollover index based optimal tilting approach, which tilts the vehicle only when rollover index reaches beyond the desired optimal threshold values. Furthermore, the present work introduces an optimal active tilt control strategy, integrating tilt control system with an active steering system. The proposed integrated tilt control system optimizes an active steering gain with the objective to minimise the deviation in the desired trajectory. The proposed control system ensures the manoeuvrability and rollover stability at higher speed up to 100 km/hr with 22° steering angle even when vehicle reaches its tilt limit. Matlab/Simulink has been used in this study to thoroughly investigate the vehicle dynamics model and proposed integrated active tilt control system. The proposed control strategy is found to be more effective than the traditional steering direct tilt control approach. An artificial neural network that avoids the control system's complexity and provides a faster response was also studied. The investigation showcases the efficacy of augmenting the dataset length and increasing neurons in the network's hidden layers. The performance metrics of the trained model tested on the external data imply that the model holds significant potential for practical applications. [ABSTRACT FROM AUTHOR]

Multigaussian kriging technique has many applications in mining, soil science, environmental science and other fields. Particularly, in the local reserve estimation of a mineral deposit, multigaussian kriging is employed to derive panel-wise tonnages by predicting conditional probability of block grades. Additionally, integration of a suitable change of support model is also required to estimate the functions of the variables with larger support than that of the samples. However, under the assumption of strict stationarity, the grade distributions and important recovery functions are estimated by multigaussian kriging using samples within a supposedly spatial homogeneous domain. Conventionally, the underlying random function model is required to be stationary in order to carry out the inference on ore grade distribution and relevant statistics. In reality, conventional stationary model often fails to represent complicated geological structure. Traditionally, the simple stationary model neither considers the obvious changes in local means and variances, nor is it able to replicate spatial continuity of the deposit and hence produces unreliable outcomes. This study deals with the theoretical design of a non-stationary multigaussian kriging model allowing change of support and its application in the mineral reserve estimation scenario. Local multivariate distributions are assumed here to be strictly stationary in the neighborhood of the panels. The local cumulative distribution function and related statistics with respect to the panels are estimated using a distance kernel approach. A rigorous investigation through simulation experiments is performed to analyze the relevance of the developed model followed by a case study on a copper deposit. [ABSTRACT FROM AUTHOR]

[Display omitted] • Different ranks of coal were classified using diffuse reflectance spectroscopy in the Vis-NIR-SWIR range. • LR, RF, and SVM based classification algorithms were used to identify coal ranks. • Good performance of the model indicates an alternative strategy for rapid online coal classification. This research aims at studying the ability of using diffuse reflectance spectroscopy (DRS) for discriminating or classifying coal samples into different ranks. Spectral characteristics such as the shape of the spectral profile, slope, absorption intensity of coal samples of different ranks ranging from lignite A to semi-anthracite were studied in the Vis-NIR-SWIR (350–2500 nm) range. A number of classification algorithms (Logistic Regression, Random Forest, and SVM) were trained using the DRS dataset of coal samples. Class imbalances present in the dataset were handled using different approaches (SMOTE and Oversampling of minority classes), which improved the classification accuracy. Coal samples were initially classified into broad classes viz., lignite, sub-bituminous, bituminous, and anthracite with an accuracy of 0.98 and F1 score of 0.75. Later, the same samples were further classified into sub-class levels. The sub-class level classification also obtained good results with an accuracy of 0.77 and F1 score of 0.64. The results demonstrate the effectiveness of rapid coal classification systems based on DRS dataset in combination with different machine learning-based classification algorithms. [ABSTRACT FROM AUTHOR]

• Coal properties were estimated using diffuse reflectance spectroscopy in the Vis-NIR-SWIR range. • PLS, RF, and XGBoost based regression models were used to build the predictive models for each coal property. • RF gives the best prediction for moisture and fixed carbon, XGBoost for ash, and GCV and PLS for volatile matter. In this work, the reflectance spectroscopy of 212 coal samples of different origins was investigated across the Vis-NIR-SWIR range (wavelength: 350–2500 nm) to estimate their ash, moisture, volatile matter, fixed carbon content and gross calorific value (GCV). Several mathematical pre-treatments were applied to each spectrum for improving the signal-to-noise ratio. Partial-least-square (PLS), random forest (RF), and extreme gradient boosting (XGBoost) based regression methods were used to capture the relationships between coal quality parameters with corresponding spectral responses. The predictive models were generated by taking a combination of a set of differently pre-processed spectra with the above-mentioned regression methods to obtain the optimal prediction performance. The results show that spectral pre-processing improves the prediction accuracy of a model. Excessive pre-processing, however, could reduce the model accuracy due to the loss of information. RF regression model works best for estimating moisture and fixed carbon content, while XGBoost shows the best result for ash content and GCV, and PLS models provide the most accurate prediction for volatile matter content. [ABSTRACT FROM AUTHOR]