Strength and failure characteristics of marble spheres subjected to paired point loads

Failure of irregular rock samples may provide implications in the rapid estimation of rock strength, which is imperative in rock engineering practice. In this work, analytical, experimental and numerical investigations were carried out to study the mechanical properties and failure characteristics of rock spheres under paired point loads. Analytical solutions indicted that with the increase in sample size (contact angle) and decrease in Poisson's ratio, the uneven tensile stress in theta direction decreased. Then laboratory experiments were carried out to investigate the load characteristics and failure mode of spherical marble samples with different sizes subjected to a pair of diametral point loads. The discrete element method (DEM) was adopted to study the failure process of rock spheres. The effect of the sphere diameter on the point load contact angle was examined in terms of peak load, crushed zone distribution and energy dissipation. Experimental and numerical results showed that the samples primarily fail in tension, with crushed zones formed at both loading points. With increase in the sample size, the contact angle, crushed area and total work increase. As the specimen diameter increases from 30 mm to 50 mm, the peak load on the specimen increases from 3.6 kN to 8.8 kN, and the percentage of crushed zone (ratio of crushing zone to sample radius, d/r) increased from 0.191 to 0.262. The results of the study have implications for understanding the failure of irregular rock specimens under point loading conditions and their size effects

Development of support vector machine learning algorithm for real time update of resource estimation and grade classification

This paper presents the development and implementation of a theoretical mathematical-statistical framework for sequential updating of the grade control model, based on a support vector machine learning algorithm. Utilising the Zambujal orebody within the Neves-Corvo Cu deposit in Portugal, parameters that can be measured in real time, used in visualisation, modelled for resource estimation, and used for process control visualisation and optimisation are considered. The methodology broadly comprises of three steps. Firstly, the provided dataset is used to develop a virtual asset model (VAM) representing the true 3D grade distribution in order to simulate the mining method. Then ore quality parameters are established simulating real time monitoring sensor installation at: (a) stope development and rock face monitoring (face imaging and drillholes); and (b) transport monitoring (muck pile, LHD/scooptram). Next, the acquired data was assimilated into the models as part of the sequential model update. Two different mining methods and the monitoring information that can be acquired during the ore extraction are analysed: (a) drift and fill mining and (b) bench and fill mining, which are widely implemented at the Neves-Corvo mine. Selected study zones were chosen such as to contrast mining through the high/low grade zones with different degrees of heterogeneity, which demonstrate the performance of resource estimation and classification models developed in heterogeneous mining stopes. The grade accuracy and error in the resource model, and high/low grade ore classification accuracy and error are evaluated as performance metrics for the proposed methods. In drift and fill mining, drillhole and face sampling data collection was simulated in a real-time manner and fed into the support vector machine (SVM) regressor to update the resource estimation model in both a high grade and low grade drift scenarios. In each scenario, six drift and fill mining steps were simulated sequentially and the posterior resource models, after integrating real time mining data, have shown significant improvement of bias correction in both updating planned resources and reconciling extracted ore. In bench and fill mining, grade classification based on random sampling data from muck pile was demonstrated, considering scoop by scoop derived monitoring data. Three different classifiers (mean, median, and Bayesian) were tested and shown very good performance. In the case study presented here, a sequence of 15 blasting steps was simulated with each step requiring 112 scooping operations to transport the blasted ore. Using the real time monitored information, it was shown that at each blasting step over 85% of the scoops can be labelled correctly using the proposed methods and with an accuracy of over 95%

Development of sustainable performance indicators to assess the benefits of real-time monitoring in mechanised underground mining

This paper presents the development and quantification of a catalogue of Sustainable Performance Indicators (SPIs) for the assessment of the benefits real-time mining can offer in small and complex mechanised underground mining operations. The SPIs investigated in detail include: ‒ grade accuracy and error of the resource model, ‒ high/low grade ore classification accuracy and error, ‒ additional high grade ore identified per unit volume, ‒ profit expected per unit volume, ‒ ore classification accuracy per unit volume assigned to the stockpiles. A case study utilising the Red Lake gold mine located in Northwestern Ontario, Canada, which is owned by Goldcorp Inc., was designed with the aim to assess the effect of real time sensory data acquisition and resource model update on the SPIs. The methodology broadly comprises of three steps. Firstly, the provided dataset was used to develop a virtual asset model (VAM) representing the true 3D grade distribution in order to simulate the ‘sublevel cave and fill’ mining method and the associated grade data acquisition from the development drillholes and face monitoring, the development and production muck pile, LHD/scooptram and conveyor belt transport, taking into account the sensor parameters. Next, the acquired data was assimilated into the models developed for the purpose of detailed statistical assessment of the SPIs, thereby enabling optimised decision-making during the production of ore in order to meet the grade requirements. Finally, an evaluation of the sensor performance was carried out using three additional levels of sensor error and interpretation bias (10, 20 and 30%). The three models used for the quantification of the SPIs include: ‒ resource block model (RBM): which represents the 3D grade distribution in the ore body; ‒ grade control model: which enables selective stope production (drilling, charging and blasting) based on the underlying requirements pertaining to e.g. cut-off grade, time and economic constraints; and ‒ logistics model: which classifies the ore grades for conveyance and stockpiling, in order to eventually facilitate for the mixing of run of mine ore to meet the grade requirements before milling at the processing plant. The improvement of the SPIs when real time monitored data is used in the update of the models has been verified. It is also shown that the noise in the acquired data, which directly reflects both the accuracy and precision of the sensors, has a measurable effect on the values obtained for the SPIs. However, 10 to 20% noise does not appear to reduce significantly the improvements achieved, while 30% noise has a more profound effect on the SPIs and the quality of improvements achieved through real time data assimilation in the models. The work carried out demonstrates that there is a need for robust sensor technologies that allow for minimum bias in grade estimation and maximum classification accuracy. It is also expected that sensor performance is likely to vary from site to site and possibly within the same ore deposit mined due to local geological conditions (heterogeneity), variations in the underground environment were sensors are installed (affecting sensor performance), the mining method used (affecting the access and availability of real time monitored data) as well as the specifics of the sensor technologies used. Thus, it is suggested that sensor performance needs to be evaluated and quantified for the mine and area considered for sensor installation given the local geological, operational and mining method related characteristics and opportunities for monitoring

Monitoring and modelling of microseismicity associated with rock burst and gas outburst hazards in coal mines

This thesis aimed at establishing a better understanding of the mechanisms involved and methods for forecasting, prevention and control of rock bursts and gas outbursts in underground coal mining. After a comprehensive review of relevant literature, the thesis first presents experimental investigations into the seismic response of coal blocks to stress and fracturing under true-triaxial stress conditions. The dynamic response of coal seams to longwall face advance has been investigated through continuous microseismic monitoring of several longwall panels over the research period. A conceptual model has been developed to interpret the recorded microseismicity based on the fracture slip seismicity-generation mechanism. Based upon the monitoring results and the conceptual model, a statistical short-term forecasting methodology was developed to estimate the probability of hazardous microseismicity during longwall coal mining. In addition, a discrete fracture network (DFN) based microseismic modelling methodology was developed to simulate Longwall Top Coal Caving (LTCC) mining induced microseismicity in a probabilistic framework through the combination of deterministic stress and failure analysis and stochastic fracture slip evaluation. The modelling methodology was further employed to investigate the impact of lithological heterogeneity on microseismic characteristics. Further on, rock bursts and coal and gas outbursts are generalised as problems of dynamic instability under excavation unloading conditions. The role of excavation unloading as a source of dynamic stress perturbations in contributing to rock bursts was quantified. A coal and gas outburst model based on fracture mechanics and gas dynamics was formulated and further numerically implemented to simulate coal and gas outbursts during roadway developments. Key factors affecting outburst initiation and its temporal evolution were also identified.Open Acces

Stress-Dependent Deformation and Permeability of a Fractured Coal Subject to Excavation-Related Loading Paths

The deformation and permeability of coal are largely afected by the presence and distribution of natural fractures such as cleats and bedding planes with orthogonal and abutting characteristics, resulting in distinct hydromechanical responses to stress loading during coal mining processes. In this research, a two-dimensional (2D) fracture network is constructed based on a real coal cleat trace data collected from the Fukang mine area, China. Realistic multi-stage stress loading is designed to sequentially mimic an initial equilibrium phase and a mining-induced perturbation phase involving an increase of axial stress and a decrease of confning stress. The geomechanical and hydrological behaviour of the fractured coal under various stress loading conditions is modelled using a fnite element model, which can simulate the deformation of coal matrix, the shearing and dilatancy of coal cleats, the variation of cleat aperture induced by combined efects of closure/opening, and shear and tensile-induced damage. The infuence of diferent excavation stress paths and directions of mining is further investigated. The simulation results illustrate correlated variations among the shear-induced cleat dilation, damage in coal matrix, and equivalent permeability of the fractured coal. Model results are compared with results of previous work based on conventional approaches in which natural fracture networks are not explicitly represented. In particular, the numerical model reproduces the evolution of equivalent permeability under the competing infuence of the efective stress perpendicular to cleats and shear-induced cleat dilation and associated damage. Model results also indicate that coal mining at low stress rates is conducive to the stability of surrounding coal seams, and that coal mining in parallel to cleat directions is desirable. The research fndings of this paper have important implications for efcient and safe exploitation of coal and coalbed methane resources.ISSN:1434-453XISSN:0723-263

Theoretical study on dynamic stress redistribution around circular tunnel with different unloading paths

This study presents a solution for stress redistribution induced by dynamic excavation of a rock tunnel in hydrostatic and non-hydrostatic stress states. Considering rock as an elastic medium, the analytical solution of dynamic excavation unloading was derived in the Laplace transform space utilizing the wave function expansion method and mode decomposition, and transformed into the time domain using the approximate numerical inversion method. The effects of the unloading path (linear, cosine, and exponential), unloading rate, and initial stress state on stress redistribution were analyzed. The analytical results indicated that a high unloading rate led to a high stress concentration and oscillations around the tunnel, and instantaneous dynamic unloading caused 20–30% stress concentration. When dynamic unloading is complete, the stress state around the tunnel converges to the Kirsch solution. The 3D numerical results indicated that deformation of the tunnel boring face increased as the initial stress increased. Moreover, the accumulation and release of strain energy and stress state change paths were analyzed and discussed. The results of this study provide a theoretical basis for understanding the stress adjustment and failure of surrounding rock induced by excavation of deep-buried openings in hydrostatic and non-hydrostatic stress states

A Preliminary Study on Microbiota Characteristics of Bronchoalveolar Lavage Fluid in Patients with Pulmonary Nodules Based on Metagenomic Next-Generation Sequencing

Background: The characteristics and roles of microbes in the occurrence and development of pulmonary nodules are still unclear. Methods: We retrospectively analyzed the microbial mNGS results of BALF from 229 patients with pulmonary nodules before surgery, and performed a comparative analysis of lung flora between lung cancer and benign nodules according to postoperative pathology. The analysis also focused on investigating the characteristics of lung microbiota in lung adenocarcinomas with varying histopathology. Results: There were differences in lung microbiota between lung cancer and benign lung nodules. Bacterial diversity was lower in lung cancer than in benign lung nodules. Four species (Porphyromonas somerae, Corynebacterium accolens, Burkholderia cenocepacia and Streptococcus mitis) were enriched in lung cancer compared with the benign lung nodules. The areas under the ROC curves of these four species were all greater than 0.6, and the AUC of Streptococcus mitis was 0.702, which had the highest diagnostic value for differentiating lung cancer from benign lung diseases. The significantly enriched microbiota varied with the different pathological subtypes of lung adenocarcinoma. Streptococcus mitis, Burkholderia oklahomensis and Burkholderia latens displayed a trend of increasing from the benign lung disease group to the AIS group, MIA group and IAC group, whereas Lactobacillus plantarum showed a downward trend. Conclusion: Changes in the abundance of lung microbiota are closely related to the development of infiltrating adenocarcinoma. Our findings provide new insights into the relationship between the changes in lung microbiota and the development of lung cancer

A Stochastic Simulation Model for Monthly River Flow in Dry Season

Streamflow simulation gives the major information on water systems to water resources planning and management. The monthly river flows in dry season often exhibit high autocorrelation. The headwater catchment of the Yellow River basin monthly flow series in dry season exhibits this clearly. However, existing models usually fail to capture the high-dimensional, nonlinear dependence. To address this issue, a stochastic model is developed using canonical vine copulas in combination with nonlinear correlation coefficients. Kendall’s tau values of different pairs of river flows are calculated to measure the mutual correlations so as to select correlated streamflows for every month. Canonical vine copula is used to capture the temporal dependence of every month with its correlated streamflows. Finally, monthly river flow by the conditional joint distribution functions conditioned upon the corresponding river flow records was generated. The model was applied to the simulation of monthly river flows in dry season at Tangnaihai station, which controls the streamflow of headwater catchment of Yellow River basin in the north of China. The results of the proposed method possess a smaller mean absolute error (MAE) than the widely-used seasonal autoregressive integrated moving average model. The performance test on seasonal distribution further verifies the great capacity of the stochastic-statistical method