Recent publications in Engineering
Solution-processing of organic light-emitting diode films has potential advantages in terms of cost and scalability over vacuum-deposition for large area applications. However, solution processed small molecule films can have lower overall device performance. Here, novel molecular dynamics techniques are developed to enable faster simulation of solvent evaporation that occurs during solution processing and give films of thicknesses relevant to real devices. All-atom molecular dynamics simulations are then used in combination with kinetic Monte Carlo transport modeling to examine how differences in morphology stemming from solution or vacuum film deposition affect charge transport and exciton dynamics in films consisting of light-emitting bis(2-phenylpyridine)(acetylacetonate)iridium(III) [Ir(ppy)2(acac)] guest molecules in a 4,4′-bis(N-carbazolyl)biphenyl host. While the structures of the films deposited from vacuum and solution were found to differ, critically, only minor variations in the transport properties were predicted by the simulations even if trapped solvent was present.
Wheatley, Greg, and Zaeimi, Mohammad (2022) Front impact attenuator design for a race car. International Journal of Crashworthiness, 27 (2). pp. 466-475.
This paper outlines the design, analysis and physical testing of the material for the front impact attenuator for the Motorsports car. The material selected was a 20 mm thick panel of veiled polypropylene honeycomb. The design programme Solidworks was used to produce three impact attenuator geometries for analysis – a solid block, a three-tier pyramid and a four-tier pyramid. The impact attenuator was simplified to solid blocks specified with the honeycomb material strengths and tested in ANSYS 14.0. The simulations did indicate that the four-tier pyramid had the greatest energy absorption behaviours. Two full size prototypes were manufactured and destructively tested. The force-displacement curve determined that prototype MKII achieved total energy absorption of 7591 J. The average deceleration was 17.66 g and maximum deceleration was 30.58 g. The polypropylene honeycomb energy absorption and deceleration values comply with FSAE requirements..
Malekzadeh, M., Sivakugan, Siva, and Clark, M.W. (2022) Effect of aqueous environment on sedimentation of dredged mud and kaolinite. Marine Georesources & Geotechnology, 40 (2). pp. 171-180.
Port development results in production of large quantities of dredged marine sediments. Once dredged, sediments often have high water contents and are pumped to near-shore or in-water bunded marine impoundments for port expansion. However, dredged material disposal to freshwater onshore or empty impoundments typically changes sedimentation conditions that may change the effective grain-size distribution, mineral specific surface areas, settling particle orientations, resulting in settling rate changes. Salinity, temperature, water content, mineralogy, filling rate, and organic matter content may also influence sediment settlement and resulting consolidation. This study investigates the effect of salinity and sediment mineralogy on sediment settlement behaviour when deposited in saltwater, freshwater, or to empty ponds. For this purpose, slurries of dredged mud and kaolinite with water contents of 1.7 times their liquid limit were prepared and disposed into a series of 1000 mm long, 50 mm wide, and 500 mm high settlement columns. Result shows that the sediments settle faster in saltwater than freshwater or air, through divalent surface complexation and flocculation provided by seawater Ca and Mg. However, this is true if the salinity remains below 10 PSU, but the mixed mineral dredge spoil ultimately provides the densest sediment and lowest water sediment interface.
Sedighkia, Mahdi, Datta, Bithin, and Abdoli, Asghar (2022) Utilizing classic evolutionary algorithms to assess the Brown trout (Salmo trutta) habitats by ANFIS-based physical habitat model. Modeling Earth Systems and Environment, 8. pp. 857-869.
Present study evaluates the application of coupled evolutionary algorithm- adaptive neuro-fuzzy inference system in the Brown trout riverrine habitats. We implemented the proposed method in the Lar national park as one of the most important Brown trout habitats in the southern Caspian Sea basin. Two classic evolutionary algorithms including the genetic algorithm and the particle swarm optimization were coupled with adaptive neuro-fuzzy inference system. Moreover, two conventional training methods including backpropagation and hybrid algorithm were utilized. Evaluation of developed models was carried out in two stages including assessment of habitat suitability index in observed habitats and using practical hydraulic simulation in a representative reach. Measurement indices consisting of root mean square error, mean absolute error, Nash–Sutcliffe model efficiency coefficient, reliability and vulnerability indices and fuzzy technique of order preference similarity to the ideal solution as decision-making system were used. Results demonstrate the efficiency of the coupled evolutionary algorithm- adaptive neuro-fuzzy inference system to simulate hydraulic habitats of the Brown trout. The first stage of evaluation indicates particle swarm optimization is the best method. However, practical hydraulic simulation corroborates GA is the best method for the training process. Evaluations demonstrate that backpropagation is not an appropriate method for ANFIS-based hydraulic habitat simulation.
Yang, Shuangming, Wang, Jiang, Zhang, Nan, Deng, Bin, Pang, Yanwei, and Rahimi Azghadi, Mostafa (2022) CerebelluMorphic: large-scale neuromorphic model and architecture for supervised motor learning. IEEE Transactions on Neural Networks and Learning Systems. (In Press)
The cerebellum plays a vital role in motor learning and control with supervised learning capability, while neuromorphic engineering devises diverse approaches to high-performance computation inspired by biological neural systems. This article presents a large-scale cerebellar network model for supervised learning, as well as a cerebellum-inspired neuromorphic architecture to map the cerebellar anatomical structure into the large-scale model. Our multinucleus model and its underpinning architecture contain approximately 3.5 million neurons, upscaling state-of-the-art neuromorphic designs by over 34 times. Besides, the proposed model and architecture incorporate 3411k granule cells, introducing a 284 times increase compared to a previous study including only 12k cells. This large scaling induces more biologically plausible cerebellar divergence/convergence ratios, which results in better mimicking biology. In order to verify the functionality of our proposed model and demonstrate its strong biomimicry, a reconfigurable neuromorphic system is used, on which our developed architecture is realized to replicate cerebellar dynamics during the optokinetic response. In addition, our neuromorphic architecture is used to analyze the dynamical synchronization within the Purkinje cells, revealing the effects of firing rates of mossy fibers on the resonance dynamics of Purkinje cells. Our experiments show that real-time operation can be realized, with a system throughput of up to 4.70 times larger than previous works with high synaptic event rate. These results suggest that the proposed work provides both a theoretical basis and a neuromorphic engineering perspective for brain-inspired computing and the further exploration of cerebellar learning.
Yang, Shuangming, Wang, Jiang, Deng, Bin, Rahimi Azghadi, Mostafa, and Linares-Barranco, Bernabe (2022) Neuromorphic context-dependent learning framework with fault-tolerant spike routing. IEEE Transactions on Neural Networks and Learning Systems. (In Press)
Neuromorphic computing is a promising technology that realizes computation based on event-based spiking neural networks (SNNs). However, fault-tolerant on-chip learning remains a challenge in neuromorphic systems. This study presents the first scalable neuromorphic fault-tolerant context-dependent learning (FCL) hardware framework. We show how this system can learn associations between stimulation and response in two context-dependent learning tasks from experimental neuroscience, despite possible faults in the hardware nodes. Furthermore, we demonstrate how our novel fault-tolerant neuromorphic spike routing scheme can avoid multiple fault nodes successfully and can enhance the maximum throughput of the neuromorphic network by 0.9%-16.1% in comparison with previous studies. By utilizing the real-time computational capabilities and multiple-fault-tolerant property of the proposed system, the neuronal mechanisms underlying the spiking activities of neuromorphic networks can be readily explored. In addition, the proposed system can be applied in real-time learning and decision-making applications, brain-machine integration, and the investigation of brain cognition during learning.
Jahanbakht, Mohammad, Xiang, Wei, and Rahimi Azghadi, Mostafa (2022) Sea surface temperature forecasting with ensemble of stacked deep neural networks. IEEE Geoscience and Remote Sensing Letters, 19. 1502605.
Oceanic temperature has a great impact on global climate and worldwide ecosystems, as its anomalies have been shown to have a direct impact on atmospheric anomalies. The major parameter for measuring the thermal energy of oceans is the sea surface temperature (SST). SST prediction plays an essential role in climatology and ocean-related studies. However, SST prediction is challenging due to the involvement of complex and nonlinear sea thermodynamic factors. To address this challenge, we design a novel ensemble of two stacked deep neural networks (DNNs) that uses air temperature, in addition to water temperature, to improve the SST prediction accuracy. To train our model and compare its accuracy with the state-of-the-art, we employ two well-known datasets from the national oceanic and atmospheric administration as well as the international Argo project. Using DNNs, our proposed method is capable of automatically extracting required features from the input time series and utilizing them internally to provide a highly accurate SST prediction that outperforms state-of-the-art models.
Gu, Shushi, Sun, Xinyi, Yang, Zhihua, Huang, Tao, Xiang, Wei, and Yu, Keping (2022) Energy-aware coded caching strategy design with resource optimization for satellite-UAV-vehicle integrated networks. IEEE Internet of Things Journal, 9 (8). pp. 5799-5811.
The Internet of Vehicles (IoV) can offer safe and comfortable driving experience, by the enhanced advantages of space-air-ground integrated networks (SAGINs), i.e., global seamless access, wide-area coverage and flexible traffic scheduling. However, due to the huge popular traffic volume, the limited cache/power resources and the heterogeneous network infrastructures, the burden of backhaul link will be seriously enlarged, degrading the energy efficient of the IoV in SAGIN. In this paper, to implement the popular content severing multiple vehicle users (VUs), we consider a Cache-enabled Satellite-UAV-Vehicle Integrated Network (CSUVIN), where geosynchronous earth orbit (GEO) satellite is regard as a cloud server, unmanned aerial vehicles (UAVs) are deployed as edge caching servers. Then, we propose an energy-aware coded caching strategy employed in our system model to provide more multicast opportunities, and to reduce the backhaul transmission volume, considering the effects of file popularity, cache size, request frequency, and mobility in different road sections (RSs). Furthermore, we derive the closed-form expressions of total energy consumption both in single-RS and multi-RSs scenarios with asynchronous and synchronous services schemes, respectively. An optimization problem is formulated to minimize the total energy consumption, and the optimal content placement matrix, power allocation vector and coverage deployment vector are obtained by well-designed algorithms. We finally show, numerically, our coded caching strategy can greatly improve energy efficient performance in CSUVINs, compared with other benchmarked caching schemes under the heterogeneous network conditions.
Tan, Nathan chinag Ping, Miller, Catherine M., Dos Santos Antunes, Elsa, and Sharma, Dileep (2022) Impact of physical decontamination methods on zirconia implant surface and subsequent bacterial adhesion: an in-vitro study. Clinical and Experimental Dental Research, 8 (1). pp. 313-321. (In Press)
Objective: To evaluate the effect of routinely used physical decontamination methods on the surface characteristics of zirconia implants and subsequent ability of bacteria to adhere in vitro. Background: Physical decontamination methods commonly used in peri-implantitis therapy and routine implant maintenance can potentially alter zirconia implant surfaces. Methods: Acid-etched zirconia discs were instrumented with titanium curette (TC), plastic curette, air abrasive device, ultrasonic scaler (US) with stainless steel tip. Following instrumentation, surface topography, and surface elemental composition was analyzed using 3D-laser scanning microscopy and energy-dispersive X-ray spectroscopy, respectively. Subsequently, plaque biofilm was cultured on zirconia discs for 48 h and bacterial adhesion assessed using a turbidity test and scanning electron microscopy. Results: A significant difference in surface roughness was observed between the US and control group (p < 0.05). The US and TC caused gray surface discolouration on zirconia discs due to deposition of metallic residue as confirmed by X-ray spectroscopy. No significant difference in bacterial adhesion was noted among all treatment groups (p > 0.05). Conclusion: TC and US with stainless steel tips should be used with caution due to deposition of metallic residue on the surface. Air abrasive devices and plastic curettes caused minimal surface alterations and are, therefore, safer for zirconia implant decontamination.
Wang, Bing, Peng, Qiang, Wang, Eric, Xiang, Wei, and Wu, Xiao (2022) User-dependent interactive light field video streaming system. Multimedia Tools and Applications, 81. pp. 1893-1918.
The sheer size and complex structure of light field (LF) videos bring new challenges to their compression and transmission. There have been numerous LF video compression algorithms reported in the literature to date. All of these algorithms compress and transmit all the views of an LF video. However, in some interactive or selective applications where users can choose the area of interest to be displayed, these algorithms generate a significant computational load and enormous data redundancies. In this paper, we propose an interaUser-dependent Interactive light field video streaming system eaming system based on a user-dependent view selection scheme and an LF video coding method, which streams only the required data. Specifically, by predicting trajectories and using projection models, the viewing area of users in a limited consecutive number of time slots is firstly calculated, and then a user-dependent view selection method is proposed to determine the selected views of users for streaming. Finally, with the novel LF video sequence formed by only the selected sets of views, an adaptive coding method is presented for different LF video sequences based on users’ gestures. Experimental results illustrate that the proposed interactive LF video streaming system can achieve the best performance compared with other comparison methods.
Han, Kang, Xiang, Wei, Wang, Eric, and Huang, Tao (2022) A novel occlusion-aware vote cost for light field depth estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence. (In Press)
Conventional light field depth estimation methods build a cost volume that measures the photo-consistency of pixels refocused to a range of depths, which works well in most regions but usually generates blurry edges in the estimated depth map due to occlusions. Existing occlusion handling methods rely on complex edge-aided processing and post-refinement, and this reliance limits the resultant depth accuracy and impacts on the computational performance. In this paper, we propose a novel occlusion-aware vote cost (OAVC) which is able to accurately preserve edges in the depth map. Instead of using photo-consistency as an indicator of the correct depth, we construct a novel cost from a new perspective that counts the number of refocused pixels whose deviations from the central-view pixel is less than a small threshold, and utilizes that number to select the correct depth. The pixels from occluders are thus excluded in determining the correct depth. Without the use of any explicit occlusion handling methods, the proposed method can inherently preserve edges and produces high-quality depth estimates. Experimental results show that the proposed OAVC outperforms state-of-the-art light field depth estimation methods in terms of depth estimation accuracy and the computational performance.
Vuppaladadiyam, Arun K., Antunes, Elsa, Vuppaladadiyam, Varsha S.S., Shehzad, Farrukh, Somasundaram, Murugavelh, Memon, Muhammad Z., Song, Qingbin, Dong, Weiguo, and Duan, Hubao (2022) Discernment of synergy during the co-pyrolysis of lipid-extracted microalgae and digested municipal solid waste: a thermogravimetric–mass spectrometric study. Journal of Chemical Technology and Biotechnology, 97 (2). pp. 490-500.
BACKGROUND: The current study investigated the co-pyrolysis nature of biomasses, gas yields, kinetics and thermodynamics of Chroococcus sp. (CC), digested municipal solid waste (DMSW) and their mixtures using thermogravimetric analysis and mass spectrometry. RESULTS: Thermogravimetric/differential thermogravimetric analysis indicated three major weight loss stages: dehydration (50–150 °C), thermal degradation of structural components (150–550 °C) and char decomposition (550–800 °C). The gases released during the process mainly contained CO, CH4, CO2 and H2 as main components. Also, with an increase in the composition of CC, the hydrogen yields were noticed to increase. Model-free isoconversional methods, the Kissinger–Akahira–Sunose and Friedman methods, were considered to identify the activation energy. The kinetic results showed that an increase in the percentage of CC in the mixture lowered the activation energy. The activation energies recorded for CC, DMSW, CD-1, -2 and -3 were 282.11, 202.55, 210.54, 145.46 and 139.98 kJ mol−1, respectively. CONCLUSIONS: Thermodynamic and kinetic analysis for CC, DMSW and their mixtures can be effectively used for reactor design and process optimization for similar types of waste materials. © 2021 Society of Chemical Industry (SCI).
Dada, Tewodros Kassa, Sheehan, Madoc, Murugavelh, S., and Antunes, Elsa (2022) A review on catalytic pyrolysis for high-quality bio-oil production from biomass. Biomass Conversion and Biorefinery. (In Press)
Biomass is a renewable source and potentially sustainable fossil fuel replacement due to its availability, lower processing cost, high conversion, and lower life cycle carbon emissions. Pyrolysis can be used to convert biomass into bio-oil, but the quality of bio-oil is usually poor exhibiting high viscosity, thermal instability, and corrosiveness. This review article is focused on the application of catalytic pyrolysis towards obtaining high-quality bio-oil and advanced techniques for bio-oil characterization. Structural arrangement (i.e., mesoporous, microporous), number of acid sites (Lewis and Brønsted acid sites), and amount of metal loading play a key role on deoxygenation reactions and selective production of aromatic hydrocarbons. Hierarchical zeolites doped with noble metals favour hydrogenation of C-O or C=O and reduce coke deposition in the production of polycyclic aromatics. Overall reaction mechanisms, aromatic yield and selectivity, the effect of Si/Al ratio, and process challenges of metal loaded zeolites are summarized. The advantages and disadvantages of different types of advanced analytical techniques for bio-oil characterisation are also discussed. The results showed that two-dimensional gas chromatography (2D GC) technique can identify 70% of chromatograph from bio-oil analysis. However, there is need to combine analytical techniques to accurately quantify bio-oil components.
Smith, Calvin, Hill, Blair, Wheatley, Greg, Masoudi Nejad, Reza, and Sina, Nima (2022) Fatigue reliability assessment of the new design of rear suspension system of the JCU motorsport car. Structures, 36. pp. 473-481.
The objective of this paper is to calculate fatigue reliability of the new design of rear suspension system of the James Cook University (JCU) motorsports 2nd generation vehicle for the formula society of automotive engineers (FSAE) competition. Also, this paper entails preliminary and detailed design stages for the rear suspension ensuring coherence with the FSAE rules, and carrying out details theoretical and numerical analysis throughout and iterative design process. The objective of this paper to conceive and design a fully functioning rear suspension system that includes uprights, wishbones, rockers, dampers and shock-absorbers. The fabrication of specific suspension components, if not all, is a major goal of this paper. Then, in order to maintain the minimum amount of fatigue reliability at a certain value, the number of variable components is defined at each time of inspection. The results showed that reducing the time interval between periodic inspections and increasing the number of replaced components at each stage of the inspection can keep the system reliability as expected.
Lammie, Corey, Xiang, Wei, and Rahimiazghadi, Mostafa (2022) Modeling and simulating in-memory memristive deep learning systems: an overview of current efforts. Array, 13. 100116.
Deep Learning (DL) systems have demonstrated unparalleled performance in many challenging engineering applications. As the complexity of these systems inevitably increase, they require increased processing capabilities and consume larger amounts of power, which are not readily available in resource-constrained processors, such as Internet of Things (IoT) edge devices. Memristive In-Memory Computing (IMC) systems for DL, entitled Memristive Deep Learning Systems (MDLSs), that perform the computation and storage of repetitive operations in the same physical location using emerging memory devices, can be used to augment the performance of traditional DL architectures; massively reducing their power consumption and latency. However, memristive devices, such as Resistive Random-Access Memory (RRAM) and Phase-Change Memory (PCM), are difficult and cost-prohibitive to fabricate in small quantities, and are prone to various device non-idealities that must be accounted for. Consequently, the popularity of simulation frameworks, used to simulate MDLS prior to circuit-level realization, is burgeoning. In this paper, we provide a survey of existing simulation frameworks and related tools used to model large-scale MDLS. Moreover, we perform direct performance comparisons of modernized open-source simulation frameworks, and provide insights into future modeling and simulation strategies and approaches. We hope that this treatise is beneficial to the large computers and electrical engineering community, and can help readers better understand available tools and techniques for MDLS development.
Faheem, Hafiz Hamza, Abbas, Syed Zaheer, Tabish, Asif Nadeem, Fan, Liyuan, and Maqbool, Fahad (2022) A review on mathematical modelling of Direct Internal Reforming- Solid Oxide Fuel Cells. Journal of Power Sources, 520. 230857.
The Solid Oxide Fuel Cells (SOFCs) anode materials are catalytically active for Direct Internal Reforming (DIR) thus avoiding the need of external reformer. However, practical application of DIR in SOFCs requires careful system design and selection of operating conditions to avoid cell degradation due to carbon depositions and other impurities. In recent years, numerous simulation studies, besides experimental investigations, have been carried out to understand the physical and electrochemical complexities of DIR-based SOFC systems in order to develop viable designs and optimize the operating conditions before conducting costly experiments. The objective of this work is to review the present status of DIR-SOFC modeling efforts and consolidate their findings in order to highlight the unresolved problems for future research in this field. A specific focus of this review has been given to the multiscale mathematical modeling. Pre-reforming techniques, influences of the chemical and electro-chemical reaction kinetics and operational variables along with future prospects of the DIR-SOFC have been also reviewed and discussed.
Singh, Jagtar, Wheatley, Greg, Branco, Ricardo, Ventura Antunes, Fernando, Masoudi Nejad, Reza, and Berto, Filippo (2022) On the low-cycle fatigue behavior of aluminum alloys under influence of tensile pre-strain histories and strain ratio. International Journal of Fatigue, 158. 106747.
The present study attends to the effect of tensile pre-strain histories and strain ratio on cyclic deformation behavior of 6061 aluminum alloy and 2024 aluminum alloy. Low-cycle fatigue tests performed in this study covered two areas; Under fully reversed conditions, with 8% tensile pre-strain, 4% tensile pre-strain, and 0% tensile pre-strain, at strain amplitudes varying in the range 0.6–1.75%; Under strain-controlled circumstances at three different strain ratios [−1, 0, and 0.5] with strain amplitudes varying in the range 0.60–1.75%. The outcomes indicate that the material demonstrates a cyclic strain-softening behavior whose degree rises with rising values of strain ratio and reducing values of strain amplitude. The material demonstrates a non-Masing behavior for higher strain amplitudes amid the interval [1.00%−1.75%]. The material demonstrates a nearly-Masing behavior for lower strain amplitudes amid the interval [0.6%−0.85%]. Under non-zero mean strain, a total relaxation of mean stress is observed at higher strain amplitudes, whereas at lower strain amplitudes, no such behavior is observed.
Peng, Da, Khatamifar, Mehdi, and Lin, Wenxian (2022) Experimental and numerical studies of the orientation effect on the natural convection heat dissipation of composite polymer heat sinks. Journal of Enhanced Heat Transfer, 29 (6). pp. 1-26.
Recent advances in manufacturing technologies and new composite materials for additive manufacturing created new opportunities for the novel heat sink made of heat-dissipating nonmetallic materials. In this study, two commercially available thermal conductive filaments (copper filled filament and Ice9 Flex filament) from two main groups of metal filled and carbon filled thermal conductive composites were characterised and used for 3D printing of heat sinks. The possibility and the performance of using the selected commercial composite polymers for applications in electronics cooling was experimentally and numerically investigated. Due to the possibility of change in the angle of position of electronics, two different orientation angles (rotation about the x and z-axes) for angles of 0º-90º with 10º increment was studied. It was found that carbon filled filament heat sink at 90º for rotation about the x-axis had the best heat dissipation performance (about 28% higher than 0º). This case also showed the lowest average base temperature of all cases studied. The rotation about the z-axis was showed to weaken the thermal performance of all heat sinks due to limiting airflow between fins.
Jahanbakht, Mohammad, Xiang, Wei, Robson, Barbara, and Rahimi Azghadi, Mostafa (2022) Nitrogen prediction in the Great Barrier Reef using finite element analysis with deep neural networks. Environmental Modelling & Software, 150. 105311.
The corals of the Great Barrier Reef (GBR) in Australia are under pressure from contaminants including nitrogen entering the sea. To provide decision support in reaching target water quality outcomes, development of a nitrogen forecasting model may be useful. Here, we propose a new technique that considers the whole GBR as a frame and treats forecasting of nitrogen as a next-frame prediction task, to produce spatial maps of nitrogen over the whole GBR at forecast time-steps. To achieve this, we design an innovative Deep Neural Network (DNN) inspired by the Finite Element (FE) analysis concept. In our proposed method, the GBR area is meshed into small elements with pre-calculated stiffness matrices first. Next, both the stiffness matrices and the nitrogen values of each element are fed into the designed DNN for element-wise nitrogen prediction. The final result is then gained by attaching separate outputs of each element. Unlike other next-frame prediction models, our FE-DNN model generates accurate forecasts with unblurred prediction frames. We demonstrate that our model is the first to provide nitrogen forecasts for the entire GBR with low Mean Square Error (MSE), while generating a high-resolution prediction frame. The proposed model is applicable to other environmental modelling applications that are governed by Partial Differential Equations (PDE), e.g., sea temperature prediction and sediment distribution forecasting. Nonetheless, no knowledge of the underlying PDEs is required to use our DNN-based model. Our method can produce accurate forecasting predictions by leveraging existing hindcasting simulation models.
El Kamash, Walid, El Naggar, Hany, and Nagaratnam, Sivakugan (2022) Novel adaptation of Marston's stress solution for inclined backfilled stope. Alexandria Engineering Journal, 61 (10). pp. 8221-8239.
In underground mining, it is crucial to consider the arching phenomenon, especially for inclined backfilled trenches and mine stopes. That phenomenon decreases the vertical stress of the fill material, so, the in-site stress has already redistributed itself to the hanging- and foot-walls when the stope was excavated. In such cases, the mobilized resistance due to friction between the granular backfill material and the inclined walls can substantially reduce the pressure at the bottom of the stope, which could have a major impact on the stability of the backfill medium and consequently also on economic aspects. Most of researchers used numerical analysis or Lab. tests to predict both of vertical and lateral stresses in inclined stopes. However, there is a need to investigate analytical solution to describe the behaviour of those stresses in inclined stopes. Based on Marston’s formula, this research provides a new approach to predicting vertical stresses at any depth in inclined backfilled stopes. The proposed approach introduces a new parameter, η, to account for the contribution of backfill arching. This parameter specifies the ratio of normal stresses on the hanging wall and foot wall of the inclined backfilled stope. This differs from previous approaches, which assumed that the normal stress on the inclined backfilled stope's hanging wall and foot wall was equal. To validate the proposed approach, results obtained are compared with numerical, analytical, and experimental results from previous research. It is found that if the proposed parameter, η, is modified to 0.2 for the lateral earth pressure coefficient at rest with an angle of inclination of 60° to 80°, good agreement with experimental data is achieved.
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