Effect of pretension on the mechanical behaviour of bolted rock

ABSTRACT: A stepwise pull-and-shear test (SPST) scheme that numerically analyses the mechanical behaviour of bolted rock joints subjected to simultaneous pull-shear loading. The SPST scheme allows us to identify the optimum pretension stress magnitude at which the bolting system exhibits its ultimate shear capacity. The micro-mechanical properties of grout and bolt-grout interface were calibrated against the laboratory data. The micro-mechanical parameters of rock were calibrated against the laboratory data of coal and shale, and the micro-mechanical properties of rock joint interface were identified by reproducing the laboratory behaviour of coal-shale interface under the direct shear test. Then, the SPST scheme was employed to study the effect of pretension stress magnitude on the macroscopic behaviour of bolted coal. The numerical results revealed that at yield pretension stress magnitude (pull-out test) the rock bolting system could exhibit its ultimate shear performance

The Performance of Fully Grouted Rock Bolts Subjected to Combined Pull and Shear Loads Under Constant Normal Stiffness Condition

The natural discontinuities in rock masses, which form unstable rock blocks, have a profound impact on the stability and safety of mining structures. The most commonly used means of rock block reinforcement in the field is fully grouted rock bolt because of its high tension capacity and its efficient anchoring. Rock bolting system forms a self-supporting structure in rock mass through reinforcing loosened rock blocks, improving shear strength of rock joints. According to field observations the failure of rock bolts occurs due to a combination of both pull-out and shear forces. Thus, understanding the failure mechanism of bolted rock joint under such a mixing loading condition is essential for rock support system design. The surface roughness characteristics, Constant Normal Load (CNL) and Constant Normal Stiffness (CNS) conditions, and the presence of infill material within rock joint can significantly influence its shear strength. Moreover, the mechanical and failure behaviour of rock as a heterogeneous material is controlled by various microstructural parameters, such as grain shape and size, type of minerals, and the existence of pre-existing flaws. Any damage due to the mine roof fall (e.g. rock block collapse in roadways and tunnels) or the failure of rock in open pit slopes can hinder mining activities, and results in penalties being imposed on mining companies. Therefore, an appropriate evaluation of rock block instability and response of rock bolting system is critical when designing both surface and underground mining structures. Recent developments in computational mechanics and distinct element numerical method (DEM) enable more efficient and faster design of mining structures. However, a promising DEM framework requires a robust and rigorous contact constitutive model, which is capable of mimicking the failure and mechanical response of material at microscopic scale. The key aspect of DEM contact model is its contact force-displacement law, which is responsible for capturing the essential macroscopic features of material failure and deformation. For rock joints reinforced with fully grouted rock bolts, these macroscopic features include brittle or softening behaviour of rock and grout, the cohesive or non-cohesive behaviour of infill material during shearing, and the failure of bolt-grout interface due to tension load. In the case of polycrystalline rock (e.g. granite), the inter- and intra-granular micro-cracking behaviour should also be taken into considerations. The focus of this study is on development of a DEM-based cohesive contact model for simulating the failure behaviour of rock, cohesive infill material (e.g. clay), grout, and bolt-grout interface. The proposed DEM-based cohesive model couples damage mechanics and plasticity theory in both modes I and II, and features an exponential decay damage function that considers the influence of both normal and shear stresses in reproducing a gradual, post-peak softening response in DEM contacts. Unlike conventional contact models such as Parallel Bond Model (PBM), flat-joint model (FJM), and smooth joint model (SJM), which feature no gradual degradation of contact strength after yield point, the cohesive softening behaviour incorporated in the new contact model inhibits the abrupt contact failure that enhances the macroscopic softening response of the DEM model. The proposed contact model is implemented in DEM code (PFC2D) to develop a cohesive DEM framework. A Stepwise Pull-Shear Test (SPST) scheme is developed to investigate the influence of pretension load, rib angle, and CNS boundary condition on the ultimate shear resistance of rock joints. The SPST approach allows simulation of bolted rock joints subjected to a combined pull-shear load, which is more realistic compared to previous shear testings that neglect the impact of simultaneous pull-out and shear loads. The proposed cohesive contact model is also incorporated into a Grain Based Model (GBM) to develop a cohesive GBM framework for simulating the micro-cracking behaviour of polycrystalline rocks. The numerical validations against a range of laboratory tests demonstrate that the proposed cohesive DEM and GBM frameworks are effective in reproducing the mechanical and failure behaviour of rock and grout materials as well as bolt-grout interface, the cohesive macroscopic response of clay-infilled rock joints, and micro-cracking behaviour of granitic rocks. The proposed modelling method, in conjunction with the SPST scheme, provided an efficient and inexpensive numerical framework that can be used by designers and geotechnical engineers for carrying out realistic experiments (i.e., combined pull–shear loads). Doing so will give them new insights into the mechanical performance of fully grouted rock bolts and failure behaviour of rock mass.Thesis (Ph.D.) -- University of Adelaide, School of Civil, Environmental & Mining Engineering, 201

Differential evolution algorithm for predicting blast induced ground vibrations

1. Introduction One of the most crucial problems in construction blasting is to predict and then mitigate the ground vibration [1]. Blast-induced ground vibration is considered as one of the most important environmental hazards of mining operations and civil engineering projects. Intense vibration can cause critical damage to structures and plants nearby the open-pit mines, dams, and mine slopes, etc [2] and [3]. Researchers who deals with this undesirable phenomenon take into account various range of parameters in order to mitigate the detrimental effects of blasting. Blast influencing parameters can be divided into two categories [2]: (a) Uncontrollable parameters, such as geological and geotechnical characteristics of the rockmass. (b) Controllable parameters, such as burden, spacing, stemming, sub-drilling, delay time, etc

Comparison of Vibration Amplitude in Isfahan Subway Due to Track Structure- An Experimental Study

Increasing the stability of structures and reducing the maintenance cost of slab track superstructures compared to ballasted tracks are among the reasons for the tendency to use this category of superstructures in the railway industry. Vibration reduction methods can be divided into three categories, source, propagation path, and receiver. In general, the slab track structures in Iran are divided into three categories: direct fixation track (DFT), floating slab track (FST), and high resilient fastener (HRF). Although railway tracks are a safe, economical and fast transportation system and can lead to the strengthening of the tourism industry, in the long term, vibrations can damage many historical structures in the city of Isfahan. FST and HRF systems are used in the structure of Isfahan subway track. In this paper, the accelerations (longitudinal, lateral, and vertical) of the Isfahan subway vehicle were measured in 30 stations (15 go stations and 15 return stations). The results showed that the HRF system compared to the FST has a significant effect in reducing the range of vibrations and ultimately the safety of the train and the ride comfort. For example, in the area between Si-O-Se-Pol and Imam Hossein Square, due to the track structure type (HRF), the maximum acceleration and RMS acceleration are in the range of 1.5 and 0.3 m/s2, respectively, while in other stations these values were extracted up to 4 and 0.7 m/s2, respectively

Coupling NCA Dimensionality Reduction with Machine Learning in Multispectral Rock Classification Problems

Though multitudes of industries depend on the mining industry for resources, this industry has taken hits in terms of declining mineral ore grades and its current use of traditional, time-consuming and computationally costly rock and mineral identification methods. Therefore, this paper proposes integrating Hyperspectral Imaging, Neighbourhood Component Analysis (NCA) and Machine Learning (ML) as a combined system that can identify rocks and minerals. Modestly put, hyperspectral imaging gathers electromagnetic signatures of the rocks in hundreds of spectral bands. However, this data suffers from what is termed the \u27dimensionality curse\u27, which led to our employment of NCA as a dimensionality reduction technique. NCA, in turn, highlights the most discriminant feature bands, number of which being dependent on the intended application(s) of this system. Our envisioned application is rock and mineral classification via unmanned aerial vehicle (UAV) drone technology. In this study, we performed a 204-hyperspectral to 5-band multispectral reduction, because current production drones are limited to five multispectral bands sensors. Based on these bands, we applied ML to identify and classify rocks, thereby proving our hypothesis, reducing computational costs, attaining an ML classification accuracy of 71%, and demonstrating the potential mining industry optimisations attainable through this integrated system

Use of a DNN-Based Image Translator with Edge Enhancement Technique to Estimate Correspondence between SAR and Optical Images

In this paper, the local correspondence between synthetic aperture radar (SAR) images and optical images is proposed using an image feature-based keypoint-matching algorithm. To achieve accurate matching, common image features were obtained at the corresponding locations. Since the appearance of SAR and optical images is different, it was difficult to find similar features to account for geometric corrections. In this work, an image translator, which was built with a DNN (deep neural network) and trained by conditional generative adversarial networks (cGANs) with edge enhancement, was employed to find the corresponding locations between SAR and optical images. When using conventional cGANs, many blurs appear in the translated images and they degrade keypoint-matching accuracy. Therefore, a novel method applying an edge enhancement filter in the cGANs structure was proposed to find the corresponding points between SAR and optical images to accurately register images from different sensors. The results suggested that the proposed method could accurately estimate the corresponding points between SAR and optical images

Model Scaling in Smartphone GNSS-Aided Photogrammetry for Fragmentation Size Distribution Estimation

Fragmentation size distribution estimation is a critical process in mining operations that employ blasting. In this study, we aim to create a low-cost, efficient system for producing a scaled 3D model without the use of ground truth data, such as GCPs (Ground Control Points), for the purpose of improving fragmentation size distribution measurement using GNSS (Global Navigation Satellite System)-aided photogrammetry. However, the inherent error of GNSS data inhibits a straight-forward application in Structure-from-Motion (SfM). To overcome this, the study proposes that, by increasing the number of photos used in the SfM process, the scale error brought about by the GNSS error will proportionally decrease. Experiments indicated that constraining camera positions to locations, relative or otherwise, improved the accuracy of the generated 3D model. In further experiments, the results showed that the scale error decreased when more images from the same dataset were used. The proposed method is practical and easy to transport as it only requires a smartphone and, optionally, a separate camera. In conclusion, with some modifications to the workflow, technique, and equipment, a muckpile can be accurately recreated in scale in the digital world with the use of positional data

Effect of Stress, Depression and Type D Personality on Immune System in the Incidence of Coronary Artery Disease

BACKGROUND: Psychoneuroimmunology (PNI) is the study of the interaction between psychological processes and the nervous and immune systems of the human body. The impact of psychological factors on the immune system and the role of this system in Coronary Artery Disease (CAD) are confirmed. Coronary Heart Disease (CHD) is arisen due to the failure of blood and oxygen to the heart tissues.AIM: The present study aimed to describe psychoneuroimmunological processes which contribute to CAD and CHD progression.METHOD: Such psychological risk factors like stress, depression and type D personality were investigated here. Psychoneuroimmunological pathways of all three mentioned risk factors were described for CAD.RESULTS: The studies review indicated that stress could be accompanied with myocardial ischemia and help to rupture. The depression involves in the transfer of stable atherosclerotic plaque to unstable, and type D personality is effective in the initial stages of a CAD.CONCLUSION: As more information on cardiovascular immunity becomes available, this will provide a better understanding and thus act as the foundation for the potential development of new treatment strategies for treatment of cardiovascular disorders

Immunomodulatory and Anti-Inflammatory Effects of Scrophularia megalantha Ethanol Extract on an Experimental Model of Multiple Sclerosis

Background and objectives: Scrophularia megalantha is a native Iranian plant. In folk remedies, the species of the genus are used to treat stomach ulcers, goiter, eczema, cancer, psoriasis, and gall; however, there is not much research about S. megalantha. The current study aimed at evaluating the therapeutic effect of Scrophularia megalantha, a medicinal plant of Iran, on myelin oligodendrocyte glycoprotein 35-55 (MOG)-induced experimental autoimmune encephalomyelitis (EAE) as a model of multiple sclerosis (MS). Methods: The ethanol 80% extract of S. megalantha aerial parts was prepared by maceration method. The extract (100 mg/kg/day) was administered to C57BL/6 mice immunized with MOG (35-55) for 7 days, 3 weeks after EAE induction. The mice brain was removed and Hematoxylin-Eosin (H&E) was used to stain the sections. Moreover, spleen mononuclear cells from extract-treated or non-treated of EAE model mice were stimulated with MOG peptide and then culture supernatants were evaluated for IFN-ɣ, IL-17 and IL-10 cytokines using Enzyme-Linked Immuno Sorbent Assay (ELISA) kits. Results: Based on the obtained results, treatment with Scrophularia megalantha areal part extract significantly reduced inflammatory cells infiltration in the central nervous system (CNS) and also the disease severity in the experimental model of MS. Also, findings of the current study indicated that treatment with this medicinal plant in EAE mice model significantly decreased inflammatory cytokines including IFN-ɣ and IL-17 and vice versa significantly increased IL-10 as anti-inflammatory cytokine compared with non-treated of EAE model mice group. Conclusion: Scrophularia megalantha attenuated EAE by suppressing IFN-ɣ and IL-17 production and also increasing IL-10 cytokine. These findings suggested that this medicinal plant has the anti-inflammatory and immunomodulatory effects

Lupus and the Nervous System: A Neuroimmunoloigcal Update on Pathogenesis and Management of Systemic Lupus Erythematosus with Focus on Neuropsychiatric SLE

An autoimmune condition is characterized by a misdirected immunological system that interacts with host antigens. Excess activation of T- and B-lymphocytes, autoantibody generation, immune complex deposition, and multi-organ injury are found in systemic lupus erythematosus (SLE), an early autoimmune condition with a substantial hereditary element. A number of environmental factors and lifestyle changes also play a role in the development of SLE. The imbalanced immunity could take part in the dysfunction and injury of different biological organs, including the central and peripheral nervous systems. Neuropsychiatric SLE (NPSLE) can present with focal and diffuse involvements. Clinical manifestations of NPSLE vary from mild cognitive deficits to changed mental status, psychosis, and seizure disorders. Headaches, mood, and cognitive problems are the most common neuropsychiatric presentations associated with SLE. NPSLE could be found in 40% of all people who have SLE. The diagnostic inference of NPSLE can be made solely following these secondary causes have been ruled out. The present chapter provides an updated discussion of the clinical presentation, molecular processes, diagnosis, management, and therapy of SLE with emphasizing on NPSLE