• Geomechanics and Engineering
• /
• v.23 no.1
• /
• pp.61-69
• /
• 2020

The uniaxial compressive strength (UCS) of rock is a basic parameter in underground engineering design. The disadvantages of this commonly employed laboratory testing method are untimely testing, difficulty in performing core testing of broken rock mass and long and complicated onsite testing processes. Therefore, the development of a fast and simple in situ rock UCS testing method for field use is urgent. In this study, a multi-function digital rock drilling and testing system and a digital core bit dedicated to the system are independently developed and employed in digital drilling tests on rock specimens with different strengths. The energy analysis is performed during rock cutting to estimate the energy consumed by the drill bit to remove a unit volume of rock. Two quantitative relationship models of energy analysis-based core drilling parameters (ECD) and rock UCS (ECD-UCS models) are established in this manuscript by the methods of regression analysis and support vector machine (SVM). The predictive abilities of the two models are comparatively analysed. The results show that the mean value of relative difference between the predicted rock UCS values and the UCS values measured by the laboratory uniaxial compression test in the prediction set are 3.76 MPa and 4.30 MPa, respectively, and the standard deviations are 2.08 MPa and 4.14 MPa, respectively. The regression analysis-based ECD-UCS model has a more stable predictive ability. The energy analysis-based rock drilling method for the prediction of UCS is proposed. This method realized the quick and convenient in situ test of rock UCS.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.41 no.1
• /
• pp.1-10
• /
• 2018

Current evaluation practices for IT projects suffer from several problems, which include the difficulty of self-explanation for the evaluation results and the improperly scaled scoring system. This study aims to develop a methodology of opinion mining to extract key factors for the causal relationship analysis and to assess the feasibility of quantifying evaluation scores from text comments using opinion mining based on big data analysis. The research has been performed on the domain of publicly procured IT proposal evaluations, which are managed by the National Procurement Service. Around 10,000 sets of comments and evaluation scores have been gathered, most of which are in the form of digital data but some in paper documents. Thus, more refined form of text has been prepared using various tools. From them, keywords for factors and polarity indicators have been extracted, and experts on this domain have selected some of them as the key factors and indicators. Also, those keywords have been grouped into into dimensions. Causal relationship between keyword or dimension factors and evaluation scores were analyzed based on the two research models-a keyword-based model and a dimension-based model, using the correlation analysis and the regression analysis. The results show that keyword factors such as planning, strategy, technology and PM mostly affects the evaluation result and that the keywords are more appropriate forms of factors for causal relationship analysis than the dimensions. Also, it can be asserted from the analysis that evaluation scores can be composed or calculated from the unstructured text comments using opinion mining, when a comprehensive dictionary of polarity for Korean language can be provided. This study may contribute to the area of big data-based evaluation methodology and opinion mining for IT proposal evaluation, leading to a more reliable and effective IT proposal evaluation method.

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

• International conference on construction engineering and project management
• /
• 2015.10a
• /
• pp.627-628
• /
• 2015

Construction defect which can causes economic damage such as schedule delay, cost overrun is a considerably important factor in construction industry. In general, a construction defect features a difficulty to find out causes precisely because it occurs when several interrelated causes combine. Yet, studies have tried to understand the interrelationships between factors are limited. In addition, despite of a tremendous amount of construction data, it's not still enough to analyze them, but tends to depend on experience or know-how of practitioners. Thus, it is necessary to identify underlying causes in influential factors by utilizing related data. This paper analyses Interrelationships between causal factors using Association Rule Mining to discover root causes of construction defects. Confidence and Lift that can be used for presenting the interrelationships of the causes were extracted from 1241 cases in 30 projects in Korea. It is expected that this paper allows the construction managers to discover key factors and make right decisions to reduce occurrence of construction defects. Furthermore, analysis of interrelationships can improve understanding of structural patterns of construction defects.

• The Journal of Information Technology and Database
• /
• v.8 no.1
• /
• pp.117-128
• /
• 2001

Recently there are many researches of text mining to find interesting patterns or association rules from mass textual documents. However, the words extracted from informal documents are tend to be irregular and there are too many general words, so if we use pre-exist method, we would have difficulty in retrieving knowledge information effectively. In this paper, we propose a new feature extraction method to classify mass documents using association rule based on unsupervised learning technique. In experiment, we show the efficiency of suggested method by extracting features and classifying of documents.

• Tunnel and Underground Space
• /
• v.28 no.2
• /
• pp.125-141
• /
• 2018

For a long-term mine development plan, the determination and design of mine tunnel size are very important because it is the basis of plans for equipment, transportation and operation. The ${\bigcirc}{\bigcirc}$ mine has had a difficulty in changing the mining plan due to the design of the tunnels with an emphasis on productivity improvement, and much effort was needed to maintain the mine tunnel. In this study, we designed the mine tunnel with optimized tunnel span considering the mechanical properties of rockmass and established the support plan. To do this, the estimation of the mechanical parameters(Swelling pressure, deformation coefficient and earth coefficient), field investigations and various analyses were carried out. As a result, it was necessary to consider the downsizing of the tunnel section in order to maintain the tunnel stability and dimension by using the roof bolt and analyzed that various functional constructions of the support material and method would be required to maintain the current tunnel size.

• Journal of the Korean Data and Information Science Society
• /
• v.22 no.4
• /
• pp.671-678
• /
• 2011

Data mining searches for interesting relationships among items in a given database. The methods of data mining are decision tree, association rules, clustering, neural network and so on. The decision tree approach is most useful in classification problems and to divide the search space into rectangular regions. Decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, customer classification, etc. When create decision tree model, complicated model by standard of model creation and number of input variable is produced. Specially, there is difficulty in model creation and analysis in case of there are a lot of numbers of input variable. In this study, we study on decision tree using intervening variable. We apply to actuality data to suggest method that remove unnecessary input variable for created model and search the efficiency.

• Human Ecology Research
• /
• v.59 no.1
• /
• pp.23-43
• /
• 2021

This study looked through the text mining analysis to check the status of the virtual assistant service, and explore the needs of consumers, and present consumer-oriented directions. Trendup 4.0 was used to analyze the keywords of AI services in Online News and social media from 2016 to 2020. The R program was used to collect consumer comment data and implement Topic Modeling analysis. According to the analysis, the number of mentions of AI services in mass media and social media has steadily increased. The Sentimental Analysis showed consumers were feeling positive about AI services in terms of useful and convenient functional and emotional aspects such as pleasure and interest. However, consumers were also experiencing complexity and difficulty with AI services and had concerns and fears about the use of AI services in the early stages of their introduction. The results of the consumer review analysis showed that there were topics(Technical Requirements) related to technology and the access process for the AI services to be provided, and topics (Consumer Request) expressed negative feelings about AI services, and topics(Consumer Life Support Area) about specific functions in the use of AI services. Text mining analysis enable this study to confirm consumer expectations or concerns about AI service, and to examine areas of service support that consumers experienced. The review data on each platform also revealed that the potential needs of consumers could be met by expanding the scope of support services and applying platform-specific strengths to provide differentiated services.

• Geomechanics and Engineering
• /
• v.27 no.6
• /
• pp.537-550
• /
• 2021

There are various technical problems need to be solved in the construction process of pre-setting an isolation wall into a double lane in the outburst prone mine. This study presents a methodology that pre-setting an isolation wall into a double lane without a coal pillar. This requires the excavation of two small section roadways to dig a wide section roadway, followed by construction of the separation wall. During this process the connecting lane is reserved. In order to ensure the stability of the separation wall, the required bearing capacity of the isolation wall is 4.66 MN/m and the deformation of the isolation wall is approximately 25 cm. To reduce the difficulty of implementing support the roadway is driven by 5 m/d. After the construction of the separation wall, the left side coal wall is brushed 1.5 m to make the width of the gas roadway reach 2.5 m and the roadway support utilizes anchor rod, ladder beam, anchor cable beam and net configuration. During construction, the concrete pump and removable self-propelled hydraulic wall mold are used to pump and pour the concrete of the isolation wall. In the process of mining, the stress distribution of coal body and isolation wall is detected and measured on site. The results demonstrate that the deformation of the surrounding rock of roadway and separation of roof in the roadway is small. The stress of the bolt and anchor cable is within equipment tolerance validating their selection. The roadway is well supported and the intended goal is achieved. The methodology can be used for reference for similar mine gas control.

• Asian-Australasian Journal of Animal Sciences
• /
• v.31 no.11
• /
• pp.1700-1713
• /
• 2018

Objective: The aim of this study was to verify the usefulness of artificial neural networks (ANN), multivariate adaptive regression splines (MARS), naïve Bayes classifier (NBC), general discriminant analysis (GDA), and logistic regression (LR) for dystocia detection in Polish Holstein-Friesian Black-and-White heifers and cows and to indicate the most influential predictors of calving difficulty. Methods: A total of 1,342 and 1,699 calving records including six categorical and four continuous predictors were used. Calving category (difficult vs easy or difficult, moderate and easy) was the dependent variable. Results: The maximum sensitivity, specificity and accuracy achieved for heifers on the independent test set were 0.855 (for ANN), 0.969 (for NBC), and 0.813 (for GDA), respectively, whereas the values for cows were 0.600 (for ANN), 1.000 and 0.965 (for NBC, GDA, and LR), respectively. With the three categories of calving difficulty, the maximum overall accuracy for heifers and cows was 0.589 (for MARS) and 0.649 (for ANN), respectively. The most influential predictors for heifers were an average calving difficulty score for the dam's sire, calving age and the mean yield of the farm, where the heifer was kept, whereas for cows, these additionally included: calf sex, the difficulty of the preceding calving, and the mean daily milk yield for the preceding lactation. Conclusion: The potential application of the investigated models in dairy cattle farming requires, however, their further improvement in order to reduce the rate of dystocia misdiagnosis and to increase detection reliability.