• Geomechanics and Engineering
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• v.21 no.6
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• pp.551-564
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• 2020

High-stress and complex geological conditions impose great challenges to maintain excavation stability during deep underground mining. In this research, large anisotropic deformation and its management by support system at a deep underground mine in Western Australia were simulated through three-dimensional finite-difference model. The ubiquitous-joint model was used and calibrated in FLAC3D to reproduce the deformation and failure characteristics of the excavation based on the field monitoring results. After modeling verification, the roles of mining depth also the intercept angle between excavation axis and foliation orientation on the deformation and damage were studied. Based on the results, quantitative relationships between key factors and damage classifications were presented, which can be used as an engineering tool. Subsequently, the performance of support system installation sequences was simulated and compared at four different scenarios. The results show that, first surface support and then reinforcement installation can obtain a better controlling effect. Finally, the influence of bolt spacing and ring spacing were also discussed. The outcomes obtained in this research may play a meaningful reference for facing the challenges in thin-bedded or foliated ground conditions.

• Geomechanics and Engineering
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• v.14 no.5
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• pp.429-437
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• 2018

In order to reduce deformation of roadway floor heave in deep underground soft rockmass, four support design patterns were analyzed using the Fast Lagrangian Analysis of Continua (FLAC)3D, including the traditional bolting (Design 1), the bolting with the backbreak in floor (Design 2), the full anchorage bolting with the backbreak in floor (Design 3) and the full anchorage bolting with the bolt-grouting backbreak in floor (Design 4). Results show that the design pattern 4, the full anchorage bolting with the bolt-grouting backbreak in floor, was the best one to reduce the deformation and failure of the roadway, the floor deformation was reduced at 88.38% than the design 1, and these parameters, maximum vertical stress, maximum horizontal displacement and maximum horizontal stress, were greater than 1.69%, 5.96% and 9.97%. However, it was perfectly acceptable with the floor heave results. The optimized design pattern 4 provided a meaningful and reliable support for the roadway in deep underground coal mine.

• Ocean and Polar Research
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• v.36 no.4
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• pp.475-485
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• 2014

Seawater flow characteristics around a manganese nodule mining device in deep sea were analyzed through numerical investigation. The mining device influences the seawater flow field with complicated velocity distributions, and they are largely dependent on the seawater flow speed, device moving speed, and injection velocity from the collecting part. The flow velocity and turbulent kinetic energy distributions are compared at several positions from the device rear, side, and top, and it is possible to predict the distance from which the mining device affects the seawater flow field through the variation of turbulent kinetic energy. With the operation of the collecting device the turbulent kinetic energy remarkably increases, and it gradually decreases along the seawater flow direction. Turbulent kinetic energy behind the mining system increases with the seawater flow velocity. The transient behavior of nodule particles, which are not collected, is also predicted. This study will be helpful in creating an optimal design for a manganese nodule collecting device that can operate efficiently and which is eco-friendly.

• Journal of Ocean Engineering and Technology
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• v.13 no.3B
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• pp.65-72
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• 1999

A deep-ocean mining riser pipe is subjected to floating vessel motion as well as environmental forces arising from currents and waves. The dynamic analysis is carried out for a deep-ocean mining riser pipe by using a finite element method. The vortex shedding which excites risers in a direction perpendicular to the flow and induces transverse response is considered. It is demonstrated that transverse displacements due to vortex shedding is greatly increased in lock-in regions. The result of this study is compared with other results having good agreements.

• International Journal of Advanced Culture Technology
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• v.7 no.4
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• pp.295-301
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• 2019

Since the third artificial intelligence boom was triggered by deep learning, it has been 10 years. It is time to analyze and discuss the research trends of deep learning for the stable development of AI. In this regard, this study systematically analyzes the trends of research on deep learning over the past 10 years. We collected research literature on deep learning and performed LDA based topic modeling analysis. We analyzed trends by topic over 10 years. We have also identified differences among the major research countries, China, the United States, South Korea, and United Kingdom. The results of this study will provide insights into research direction on deep learning in the future, and provide implications for the stable development strategy of deep learning.

• Geomechanics and Engineering
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• v.19 no.5
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• pp.421-434
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• 2019

It is of great significance to study the mechanical properties and failure mechanism of the defected rock for geological engineering. The defected sandstone modeling with power-law distribution of pre-cracks was built in this paper by Particle Flow Code software. Then the mechanical properties of sandstone and the corresponding failure process were meticulously analyzed by changing the power-law index (PLI) and the number of pre-cracks (NPC). The results show that (1) With the increase of the PLI, the proportion of prefabricated long cracks gradually decreases. (2) When the NPC is the same, the uniaxial compressive strength (UCS) of sandstone increases with the PLI; while when the PLI is the same, the UCS decreases with the NPC. (3) The damage model of rock strength is established based on the Mori-Tanaka method, which can be used to better describe the strength evolution of damaged rock. (4) The failure mode of the specimen is closely related to the total length of the pre-crack. As the total length of the pre-crack increases, the failure intensity of the specimen gradually becomes weaker. In addition, for the specimens with the total pre-crack length between 0.2-0.55 m, significant lateral expansion occurred during their failure process. (5) For the specimens with smaller PLI in the pre-peak loading process, the concentration of the force field inside is more serious than that of the specimens with larger PLI.

• Smart Structures and Systems
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• v.32 no.4
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• pp.267-279
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• 2023

Data modelling and interpretation for structural health monitoring (SHM) field data are critical for evaluating structural performance and quantifying the vulnerability of infrastructure systems. In order to improve the data modelling accuracy, and extend the application range from data regression analysis to out-of-sample forecasting analysis, an improved most likely heteroscedastic Gaussian process (iMLHGP) methodology is proposed in this study by the incorporation of the outof-sample forecasting algorithm. The proposed iMLHGP method overcomes this limitation of constant variance of Gaussian process (GP), and can be used for estimating non-stationary typhoon-induced response statistics with high volatility. The first attempt at performing data regression and forecasting analysis on structural responses using the proposed iMLHGP method has been presented by applying it to real-world filed SHM data from an instrumented cable-stay bridge during typhoon events. Uncertainty quantification and correlation analysis were also carried out to investigate the influence of typhoons on bridge strain data. Results show that the iMLHGP method has high accuracy in both regression and out-of-sample forecasting. The iMLHGP framework takes both data heteroscedasticity and accurate analytical processing of noise variance (replace with a point estimation on the most likely value) into account to avoid the intensive computational effort. According to uncertainty quantification and correlation analysis results, the uncertainties of strain measurements are affected by both traffic and wind speed. The overall change of bridge strain is affected by temperature, and the local fluctuation is greatly affected by wind speed in typhoon conditions.

• International Journal of Naval Architecture and Ocean Engineering
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• v.6 no.3
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• pp.652-669
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• 2014

This paper concerns the kinematic characteristics of a coupling device in a deep-seabed mining system. This coupling device connects the buffer system and the flexible pipe. The motion of the buffer system, flexible pipe and mining robot are affected by the coupling device. So the coupling device should be considered as a major factor when this device is designed. Therefore, we find a stable kinematic device, and apply it to the design coupling device through this study. The kinematic characteristics of the coupling device are analyzed by multi-body dynamics simulation method, and finite element method. The dynamic analysis model was built in the commercial software DAFUL. The Fluid Structure Interaction (FSI) method is applied to build the deep-seabed environment. Hydrodynamic force and moment are applied in the dynamic model for the FSI method. The loads and deformation of flexible pipe are estimated for analysis results of the kinematic characteristics.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.137-142
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• 2021

Machine-learning systems have proven their worth in various industries, including healthcare and banking, by assisting in the extraction of valuable inferences. Information in these crucial sectors is traditionally stored in databases distributed across multiple environments, making accessing and extracting data from them a tough job. To this issue, we must add that these data sources contain sensitive information, implying that the data cannot be shared outside of the head. Using cryptographic techniques, Privacy-Preserving Machine Learning (PPML) helps solve this challenge, enabling information discovery while maintaining data privacy. In this paper, we talk about how to keep your data mining private. Because Data mining has a wide variety of uses, including business intelligence, medical diagnostic systems, image processing, web search, and scientific discoveries, and we discuss privacy-preserving in deep learning because deep learning (DL) exhibits exceptional exactitude in picture detection, Speech recognition, and natural language processing recognition as when compared to other fields of machine learning so that it detects the existence of any error that may occur to the data or access to systems and add data by unauthorized persons.

• Geomechanics and Engineering
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• v.34 no.3
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• pp.303-316
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• 2023

With the development and utilization of urban underground space, the artificial ground freezing technology has been widely used in the construction of underground engineering in soft soil areas. The mechanical properties of soft clay changed greatly after freezing and thawing, which affected the seismic performance of underground structures. In this paper, a series of triaxial tests were carried out to study the dynamic response of the freezing-thawing clay under the seismic load considering different dynamic stress amplitudes and different confining pressures. The reduction factor of dynamic shear stress was determined to correct the amplitude of the seismic load. The deformation development mode, the stress-strain relationship and the energy dissipation behavior of the soft clay under the seismic load were analyzed. An empirical model for predicting accumulative plastic strain was proposed and validated considering the loading times, the confining pressures and the dynamic stress amplitudes. The relevant research results can provide a theoretical reference to the seismic design of underground structures in soft clay areas.