• Structural Engineering and Mechanics
• /
• v.66 no.3
• /
• pp.379-386
• /
• 2018

The present study addresses the direct and indirect methods of determining the mode-I fracture toughness of concrete using experimental tests and particle flow code. The direct method used is compaction tensile test and the indirect methods are notched Brazilian disc test, semi-circular bend specimen test, and hollow center cracked disc. The experiments were carried out to determine which indirect method yields the fracture toughness closer to the one obtained by the direct method. In the numerical analysis, the PFC model was first calibrated with respect to the data obtained from the Brazilian laboratory test. The crack paths observed in the simulated tests were in reasonable accordance with experimental results. The discrete element simulations demonstrated that the macro fractures in the models are caused by microscopic tensile breakages on large numbers of bonded particles. The mode-I fracture toughness in the direct tensile test was smaller than the indirect testing results. The fracture toughness obtained from the SCB test was closer to the direct test results. Hence, the semi-circular bend test is recommended as a proper experiment for determination of mode-I fracture toughness of concrete in the absence of direct tests.

• Geomechanics and Engineering
• /
• v.23 no.2
• /
• pp.127-137
• /
• 2020

To study the reverse fault formation process and the stress evolution feature, a simulation test system of reverse fault formation is developed based on the analysis of reverse fault formation mechanism. The system mainly consists of simulation laboratory module, operation console and horizontal loading control system, and data monitoring system. It can represent the fault formation process, induce fault crack initiation and simulate faults of different throws. Simulation tests on reverse fault formation process are conducted by using the simulation test system: horizontal loading is added to one side of the model. the bottom rock layer cracks under the effect of the induction device. The crack dip angle is about 29°. A reverse fault is formed with the expansion of the crack dip angle towards the upper right along the fracture surface and the slippage of the hanging wall over the foot wall. Its formation process unfolds five stages: compressive deformation of rock, local crack initiation, reverse fault penetration, slippage of the hanging wall over the foot wall and compaction of fault plane. There is residual structural stress inside rock after fault formation. The study methods and results have guiding and referential significance for further study on reverse fault formation mechanism and rock stress evolution.

• Journal of Ocean Engineering and Technology
• /
• v.30 no.6
• /
• pp.526-534
• /
• 2016

This study had the goal of designing onboard structures for a pre-pilot mining test (PPMT), which is required for the commercialization of the deep-sea mining industry. This PPMT is planned to validate the performance of a hydraulic lifting system and verify the concept of operating through a moon-pool in the east sea, Korea. All of the onboard equipment and facility were designed by KRISO. Because the test was performed at the first development, it is difficult to determine what risk will occur in the facility. Therefore, risk-based design is required in the facility for the PPMT, which includes the facility layout, failure mode and effect analysis (FMEA), and risk reduction plan. All of the expected performances of the lifting system itself and the onboard facilities were qualitatively validated using the risk-based design.

• Journal of Intelligence and Information Systems
• /
• v.11 no.3
• /
• pp.57-67
• /
• 2005

Data mining techniques enable us to generate useful information for decision support from the data sources which are generated and accumulated in the process of routine organizational management activities. College administration system is a typical example that produces a warehouse of student records as each and every student enters a college and undertakes the curricular and extracurricular activities. So far, these data have been utilized to a very limited student service purposes, such as issuance of transcripts, graduation evaluation, GPA calculation, etc. In this paper, we utilized Holland career search test results, TOEIC score, course work list and GPA score as the input for data mining, and we were able to generate knowledge and rules with regard to the college road map advisory service. Factor analysis and AHP(Analytic Hierarchy Process) were the primary techniques deployed in the data mining process. Since these data mining techniques are very powerful in processing and discovering useful knowledge and information from large scale student databases, we can expect a highly sophisticated student advisory knowledge and services which may not be obtained from the human student advice experts.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.14 no.6
• /
• pp.289-294
• /
• 2014

In the recent enterprises and are utilizing the CRM using data mining techniques and new marketing plan. However, data mining techniques are necessary expertise, general public access is difficult, it will be subject to constraints of time and space. in this paper, in order to solve this problem, we have proposed a Mobile CRM applying the data mining method. Thus, to analyze the structure of an existing CRM system, and defines the data flow and format. Also, define the process of the system, was designed sales trend analysis algorithm and customer sales recommendation algorithm using data mining techniques. Evaluation of the proposed system, through the test scenario to ensure proper operation, it was carried out the comparison and verification with the existing system. Results of the test, the value of existing programs and data matches to verify the reliability and use queries the proposed statistical tables to reduce the analysis time of data, it was verified rapidity.

• Steel and Composite Structures
• /
• v.51 no.2
• /
• pp.117-126
• /
• 2024

There are many challenges in the construction of large-section tunnels, such as extremely soft rock and fractured zones. In order to solve these problems, the confined concrete support technology is proposed to control the surrounding rocks. The large-scale laboratory test is carried out to clarify mechanical behaviours of the combined confined concrete and traditional I-steel arches. The test results show that the bearing capacity of combined confined concrete arch is 3217.5 kN, which is 3.12 times that of the combined I-steel arch. The optimum design method is proposed to select reasonable design parameters for confined concrete arch. The parametric finite element (FE) analysis is carried out to study the effect of the design factors via optimum design method. The steel pipe wall thickness and the longitudinal connection ring spacing have a significant effect on the bearing capacity of the combined confined concrete arch. Based on the above research, the confined concrete support technology is applied on site. The field monitoring results shows that the arch has an excellent control effect on the surrounding rock deformation. The results of this research provide a reference for the support design of surrounding rocks in large-section tunnels.

• Journal of KIISE:Databases
• /
• v.37 no.5
• /
• pp.221-227
• /
• 2010

Web access sequence mining can discover the frequently accessed web pages pursued by users. Utility-based web access sequence mining handles non-binary occurrences of web pages and extracts more useful knowledge from web logs. However, the existing utility-based web access sequence mining approach considers web access sequences from the very beginning of web logs and therefore it is not suitable for mining data streams where the volume of data is huge and unbounded. At the same time, it cannot find the recent change of knowledge in data streams adaptively. The existing approach has many other limitations such as considering only forward references of web access sequences, suffers in the level-wise candidate generation-and-test methodology, needs several database scans, etc. In this paper, we propose a new approach for high utility web access sequence mining over data streams with a sliding window method. Our approach can not only handle large-scale data but also efficiently discover the recently generated information from data streams. Moreover, it can solve the other limitations of the existing algorithm over data streams. Extensive performance analyses show that our approach is very efficient and outperforms the existing algorithm.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.42 no.4
• /
• pp.126-134
• /
• 2019

Nowadays, since there are so many big data available everywhere, those big data can be used to find useful information to improve design and operation by using various analysis methods such as data mining. Especially if we have event log data that has execution history data of an organization such as case_id, event_time, event (activity), performer, etc., then we can apply process mining to discover the main process model in the organization. Once we can find the main process from process mining, we can utilize it to improve current working environment. In this paper we developed a new method to find a final diagnosis of a patient, who needs several procedures (medical test and examination) to diagnose disease of the patient by using process mining approach. Some patients can be diagnosed by only one procedure, but there are certainly some patients who are very difficult to diagnose and need to take several procedures to find exact disease name. We used 2 million procedure log data and there are 397 thousands patients who took 2 and more procedures to find a final disease. These multi-procedure patients are not frequent case, but it is very critical to prevent wrong diagnosis. From those multi-procedure taken patients, 4 procedures were discovered to be a main process model in the hospital. Using this main process model, we can understand the sequence of procedures in the hospital and furthermore the relationship between diagnosis and corresponding procedures.

• Geomechanics and Engineering
• /
• v.20 no.5
• /
• pp.447-459
• /
• 2020

Based on the literature statistical method, the paper publication status of the isolated working face and the distribution of the rockburst coal mine were obtained. The numerical simulation method is used to study the stress distribution law of working face under different mining range. In addition, based on the similar material simulation test, the overlying strata failure modes and the deformation characteristics of coal pillars during the mining process of the isolated working face with thick-hard key strata are analyzed. The research shows that, under the influence of the key strata, the overlying strata formation above the isolated working face is a long arm T-type spatial structure. With the mining of the isolated working face, a series of damages occur in the coal pillars, causing the key strata to break and inducing the rockburst occurs. Combined with the mechanism of rockburst induced by the dynamic and static combined load, the source of dynamic and static load on the isolated working face is analyzed, and the rockburst monitoring methods and the prevention and control measures are proposed. Through the above research, the occurrence probability of rockburst can be effectively reduced, which is of great significance for the safe mining of deep coal mines.

• Journal of Intelligence and Information Systems
• /
• v.6 no.1
• /
• pp.73-82
• /
• 2000

This study examined the characteristicso f the knowledge discovery and data mining algorithms to demonstrate how they can be used to predict health outcomes and provide policy information for hypertension management using the Korea Medical Insurance Corporation database. Specifically this study validated the predictive power of data mining algorithms by comparing the performance of logistic regression and two decision tree algorithms CHAID (Chi-squared Automatic Interaction Detection) and C5.0 (a variant of C4.5) since logistic regression has assumed a major position in the healthcare field as a method for predicting or classifying health outcomes based on the specific characteristics of each individual case. This comparison was performed using the test set of 4,588 beneficiaries and the training set of 13,689 beneficiaries that were used to develop the models. On the contrary to the previous study CHAID algorithm performed better than logistic regression in predicting hypertension but C5.0 had the lowest predictive power. In addition CHAID algorithm and association rule also provided the segment characteristics for the risk factors that may be used in developing hypertension management programs. This showed that data mining approach can be a useful analytic tool for predicting and classifying health outcomes data.