• Geomechanics and Engineering
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• v.34 no.1
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• pp.103-113
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• 2023

This study focused on understanding the relationship between the design of a tunnel boring machine disc cutter ring and its rock-breaking efficiency, as well as the applicable conditions of different cutter ring types. The discrete element method was used to establish a numerical model of the rock-breaking process using disc cutters with different ring types to reveal the development of rock damage cracks and variation in cutter penetration load. The calculation results indicate that a sharp-edged (V-shaped) disc cutter penetrates a rock mass to a given depth with the lowest load, resulting in more intermediate cracks and few lateral cracks, which leads to difficulty in crack combination. Furthermore, the poor wear resistance of a conventional V-shaped cutter can lead to an exponential increase in the penetration load after cutter ring wear. In contrast, constant-cross-section (CCS) disc cutters have the highest quantity of crack extensions after penetrating rock, but also require the highest penetration loads. An arch-edged (U-shaped) disc cutter is more moderate than the aforementioned types with sufficient intermediate and lateral crack propagation after cutting into rock under a suitable penetration load. Additionally, we found that the cutter ring wedge angle and edge width heavily influence cutter rock-breaking efficiency and that a disc cutter with a 16 to 22 mm edge width and 20° to 30° wedge angle exhibits high performance. Compared to V-shaped and U-shaped cutters, the CCS cutter is more suitable for soft or medium-strength rocks, where the penetration load is relatively small. Additionally, two typical case studies were selected to verify that replacing a CCS cutter with a U-shaped or optimized V-shaped disc cutter can increase cutting efficiency when encountering hard rocks.

• Geomechanics and Engineering
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• v.37 no.1
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• pp.9-19
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• 2024

This study presents an energy analysis for large-strain cavity expansion problem based on the general strength criterion and energy theory. This study focuses on the energy dissipation problem during the cavity expansion process, dividing the soil mass around the cavity into an elastic region and a plastic region. Assuming compliance with the small deformation theory in the elastic region and the large deformation theory in the plastic region, combined with the general strength criterion of soil mass and energy theory, the energy dissipation solution for cavity expansion problem is derived. Firstly, from an energy perspective, the process of cavity expansion in soil mass is described as an energy conversion process. The energy dissipation mechanism is introduced into the traditional analysis of cavity expansion, and a general analytical solution for cavity expansion related to energy is derived. Subsequently, based on this general analytical solution of cavity expansion, the influence of different strength criterion, large-strain, expansion radius, cavity shape and characteristics of soil mass on the stress distribution, displacement field and energy evolution around the cavity is studied. Finally, the effectiveness and reliability of theoretical solution is verified by comparing the results of typical pressure-expansion curves with existing literature algorithms. The results indicate that different strength criterion have a relatively small impact on the displacement and strain field around the cavity, but a significant impact on the stress distribution and energy evolution around the cavity.

• Geomechanics and Engineering
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• v.38 no.3
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• pp.215-229
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• 2024

Because of the various patterns of deep-water inrush and complicated mechanisms, accurately predicting mine water inflows is always a difficult problem for coal mine geologists. In study presented in this paper, the water inrush channels were divided into four basic water diversion structures: aquifer, rock fracture zone, fracture zone and goaf. The fluid flow characteristics in each water-conducting structure were investigated by laboratory tests, and multistructure and multisystem coupling flow analysis models of different water-conducting structures were established to describe the entire water inrush process. Based on the research of the water inrush flow paths, the analysis model of different water inrush space structures was established and applied to the prediction of mine water inrush inflow. The results prove that the conduction sequence of different water-conducting structures and the changing rule of permeability caused by stress changes before and after the peak have important influences on the characteristics of mine water-gushing. Influenced by the differences in geological structure and combined with rock mass RQD and fault conductivity characteristics and other mine exploration data, the prediction of mine water inflow can be realized accurately. Taking the water transmitting path in the multistructure as the research object of water inrush, breaking through the limitation of traditional stratigraphic structure division, the prediction of water inflow and the estimation of potentially flooded area was realized, and water bursting intensity was predicted. It is of great significance in making reasonable emergency plans.

• Geomechanics and Engineering
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• v.23 no.1
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• pp.51-59
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• 2020

In this paper, the WEKA platform was used to mine and analyze measured data of floor failure depth and a prediction system of floor failure depth was developed with Java. Based on the standardization and discretization of 35-set measured data of floor failure depth in China, the grey correlation degree analysis on five factors affecting the floor failure depth was carried out. The correlation order from big to small is: mining depth, working face length, floor failure resistance, mining thickness, dip angle of coal seams. Naive Bayes model, neural network model and decision tree model were used for learning and training, and the accuracy of the confusion matrix, detailed accuracy and node error rate were analyzed. Finally, artificial neural network was concluded to be the optimal model. Based on Java language, a prediction system of floor failure depth was developed. With the easy operation in the system, the prediction from measured data and error analyses were performed for nine sets of data. The results show that the WEKA prediction formula has the smallest relative error and the best prediction effect. Besides, the applicability of WEKA prediction formula was analyzed. The results show that WEKA prediction has a better applicability under the coal seam mining depth of 110 m~550 m, dip angle of coal seams of 0°~15° and working face length of 30 m~135 m.

• Economic and Environmental Geology
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• v.57 no.3
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• pp.293-303
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• 2024

River mining, the extraction of sand and gravel from riverbeds, is rising at an alarming rate to keep pace with the increasing demand for construction materials worldwide. The far-reaching deleterious effects of river mining include the lowering of water levels, the augmentation of turbidity, and the erosion of riverbanks, i.e., the disruption of water flow and alteration of river morphology. Aggregates demand, geolocation, and the economy of Bangladesh accelerated illegal extraction. However, limited research has been carried out in this region, despite the severe impact on aquatic and terrestrial ecosystems. To address the corresponding consequences and direct the scope for further research, it is required to evaluate existing studies of other countries having similarities in river morphology, climate, economy, and other related parameters. In this respect, based on previous studies, the effects of sand extraction are particularly prominent in India, having 54 cross-boundary rivers with Bangladesh. The geological profile of numerous rivers in the past decades has been altered due to natural aggregate mining in the Indian subcontinent. Hence, this study focused on relevant research in this region. However, the existing research only focuses on the regional portion of the aforementioned international rivers, which lacks proper assessments of these rivers, taking into account especially the mining effects. Moreover, several global rivers that have similarities with Bangladeshi rivers, considering different parameters, are also included in this study. The findings of this article underline the pressing need for more efficacious measures to address the adverse effects of river mining and safeguard ecosystems and communities globally, especially in the Indian subcontinent, where the situation is particularly vulnerable. For this reason, targeting the aforementioned region, this review highlights the global evidence in assessing the future effects of river mining and the need for further research in this field.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.125-133
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• 1999

The objective of knowledge discovery and data mining lies in the generation of useful insights from a store of data. This paper presents a framework for knowledge mining to provide a systematic approach to the selection and deployment of tools for automated learning. Every methodology has its strengths and limitations. Consequently, a multistrategy approach may be required to take advantage of the strengths of disparate technique while circumventing their individual limitations. For concreteness, the general framework for data mining in marketing is examined in the context of developing agents for optimizing a supply chain network.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.101-106
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• 2005

Recently, as the size of genetic knowledge grows faster, automated analysis and systemization into high-throughput database has become hot issue. One essential task is to recognize and identify genomic entities and discover their relations. However, ambiguity of name entities is a serious problem because of their multiplicity of meanings and types. So far, many effective techniques have been proposed to analyze documents. Yet, accuracy is high when the data fits the model well. The purpose of this paper is to design and implement a document classification system for identifying entity problems using text/data mining combination, supplemented by rich data mining algorithms to enhance its performance. we propose RTP ost system of different style from any traditional method, which takes fault tolerant system approach and data mining strategy. This feedback cycle can enhance the performance of the text mining in terms of accuracy. We experimented our system for classifying RB-related documents on PubMed abstracts to verify the feasibility.

• Journal of Korea Society of Industrial Information Systems
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• v.10 no.4
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• pp.128-136
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• 2005

The use of technology is increasing within the service industry, but there is some doubt as to whether the benefits of employing this technology have been efficiently harnessed such as data mining. Data mining is the process of extracting certain predictive information from databases that can evolve from currently used restaurant management systems. The potential of harnessing this predictive information can have an enormous impact on the restaurant's operation on the whole, particularly in the area customer retention and competition. Since there is insufficient literature on the use of data mining in the restaurant industry, this study is both seminal and investigative, done via a Delphi survey to explore and describe the current and future applications of this process.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.125-133
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• 1999

The objective of knowledge discovery and data mining lies in the generation of useful insights from a store of data. This paper presents a framework for knowledge mining to provide a systematic approach to the selection and deployment of tools for automated learning. Every methodology has its strengths and limitations. Consequently, a multistrategy approach may be required to take advantage of the strengths of disparate technique while circumventing their individual limitations. For concreteness, the general framework for data mining in marketing is examined in the context of developing agents for optimizing a supply chain network.

• Journal of Information Processing Systems
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• v.2 no.2
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• pp.124-129
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• 2006

With the explosive increase in the generation and utilization of spatiotemporal data sets, many research efforts have been focused on the efficient handling of the large volume of spatiotemporal sets. With the remarkable growth of ubiquitous computing technology, mining from the huge volume of spatiotemporal data sets is regarded as a core technology which can provide real world applications with intelligence. In this paper, we propose a 3-tier knowledge discovery framework for spatiotemporal data mining. This framework provides a foundation model not only to define the problem of spatiotemporal knowledge discovery but also to represent new knowledge and its relationships. Using the proposed knowledge discovery framework, we can easily formalize spatiotemporal data mining problems. The representation model is very useful in modeling the basic elements and the relationships between the objects in spatiotemporal data sets, information and knowledge.