• Advances in concrete construction
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• v.11 no.5
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• pp.357-365
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• 2021

High deformable concrete (HDC) elements have compressive strength rates equal to conventional concrete and have got a high compressive strain at about 20% to 50%. These types of concrete elements as prefabricated parts have an abundance of applications in the construction industry which is the most used in the construction of tunnels in squeezing grounds, tunnel passwords from fault zones or swelling soils as soft supports. HDC elements after reaching to compressive yield stress, in nonlinear behavior have hardening combined with increasing strain and compressive strength. The main aim of this laboratory and numerical research is to construct concrete elements with the above properties so the compressive stress-strain behavior of different concrete elements with four categories of mix designs have been discussed and finally one of them has been defined as HDC element mix design. Furthermore, two columns with and without implementing of HDC elements have been made and stress-strain curves of them have been investigated experimentally. An analysis model is presented for columns using finite element method adopted by ABAQUS. The results obtained from the ABAQUS finite element method are compared with experimental data. The main comparison is made for stress-strain curve. The stress-strain curves from the finite element method agree well with experimental results. The results show that the dimension of the HDC samples is significant in the stress-strain behavior. The use of the element greatly increases energy absorption and ductility.

• Tunnel and Underground Space
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• v.32 no.3
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• pp.203-218
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• 2022

A hydro-mechanical study was performed to analyze the relationship between the magnitude of injection-induced seismicity and shut-in. In hydraulic analysis, the suspension of fluid injection makes the pore pressure gradient smaller while the pore pressure at the pressure front can reach the critical value for several hours after shut-in, which leads to the additional slip with wider area than during injection. The hydro-mechanical numerical analysis was performed to model the simplified fault system, and simulated the largest magnitude earthquake during shut-in stage. The effect of the abrupt suspension of fluid injection on the large magnitude earthquake was investigated in comparison with the continuous injection. In addition to the pore pressure distribution, it was found that the geometry of multiple faults and the stress redistribution are also important in evaluating the magnitude of the induced seismicity.

• Smart Structures and Systems
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• v.30 no.6
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• pp.613-626
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• 2022

Guaranteeing the quality and integrity of structural health monitoring (SHM) data is very important for an effective assessment of structural condition. However, sensory system may malfunction due to sensor fault or harsh operational environment, resulting in multiple types of data anomaly existing in the measured data. Efficiently and automatically identifying anomalies from the vast amounts of measured data is significant for assessing the structural conditions and early warning for structural failure in SHM. The major challenges of current automated data anomaly detection methods are the imbalance of dataset categories. In terms of the feature of actual anomalous data, this paper proposes a data anomaly detection method based on data-level and deep learning technique for SHM of civil engineering structures. The proposed method consists of a data balancing phase to prepare a comprehensive training dataset based on data-level technique, and an anomaly detection phase based on a sophisticatedly designed network. The advanced densely connected convolutional network (DenseNet) and Transformer encoder are embedded in the specific network to facilitate extraction of both detail and global features of response data, and to establish the mapping between the highest level of abstractive features and data anomaly class. Numerical studies on a steel frame model are conducted to evaluate the performance and noise immunity of using the proposed network for data anomaly detection. The applicability of the proposed method for data anomaly classification is validated with the measured data of a practical supertall structure. The proposed method presents a remarkable performance on data anomaly detection, which reaches a 95.7% overall accuracy with practical engineering structural monitoring data, which demonstrates the effectiveness of data balancing and the robust classification capability of the proposed network.

• Journal of Korean Society for Quality Management
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• v.50 no.3
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• pp.373-386
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• 2022

Purpose: This study proposes the application of Six Sigma management innovation method for more systematically enhanced execution of Quality by Design (QbD) activities. QbD requires a deeper understanding of the product and process at the design and development stage of the drug, and it is very important to ensure that no fault is fundamentally generated through thorough process control. Methods: Analyzing the background and specific procedures of quality improvement based on the drug design basis, and analyzing the key contents of each step, we have differentated and common points from the 6 Sigma methodology. We propose a new model of Six Sigma management innovation method suitable for pharmaceutical industry. Results: Regulatory agencies are demanding results from statistical analysis as a scientific basis in developing medicines to treat human life through quality improvement activities based on drug design. By utilizing the education system to improve the statistical analysis capacity in the Six Sigma activities and operating the 6 Sigma Belt system in conjunction, it helped systematically strengthen the execution power of quality improvement activities based on pharmaceutical design based on the members of the pharmaceutical industry. Conclusion: By using QbD Six Sigma, which combines quality enhancement based on pharmaceutical design basis and Six Sigma methodology suitable for pharmaceutical industry, it is possible to obtain satisfactory results both by pharmaceutical companies and regulators by using appropriate statistical analysis methods for preparing scientific evidence data required by regulatory.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.84-90
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• 2022

Machine tool state monitoring is a process that automatically detects the states of machine. In the manufacturing process, the efficiency of machining and the quality of the product are affected by the condition of the tool. Wear and broken tools can cause more serious problems in process performance and lower product quality. Therefore, it is necessary to develop a system to prevent tool wear and damage during the process so that the tool can be replaced in a timely manner. This paper proposes a method for diagnosing five tool states using a deep learning-based hierarchical convolutional neural network to change tools at the right time. The one-dimensional acoustic signal generated when the machine cuts the workpiece is converted into a frequency-based power spectral density two-dimensional image and use as an input for a convolutional neural network. The learning model diagnoses five tool states through three hierarchical steps. The proposed method showed high accuracy compared to the conventional method. In addition, it will be able to be utilized in a smart factory fault diagnosis system that can monitor various machine tools through real-time connecting.

• Journal of IKEEE
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• v.26 no.1
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• pp.10-18
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• 2022

Accurate SOC estimation is an important indicator of battery operation strategies, and many studies have been conducted. The simulation method which was mainly used in previous studies, is difficult to conduct real-time SOC estimation like real BMS environment. Therefore, this paper aims to implement a real-time battery SOC estimation embedded system and analyze problems that can arise during the verification process. In environment consisting of two Raspberry Pi boards, SOC estimation with the EKF uses data measured by the Simscape battery model. Considering that the operating characteristics of the battery vary depend on the temperature, the results were analyzed at various ambient temperatures. It was confirmed that accurate SOC estimation was performed even when offset fault and packet loss occurred due to communication or sensing problems. This paper proposes a guide for embedded system strategies that enable real-time SOC estimation with errors within 5%.

• Journal of Korean Society for Quality Management
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• v.51 no.2
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• pp.315-327
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• 2023

Purpose: Recently, research has continued to predict the time of failure of the facility through measurement data obtained by attaching a sensor to the facility. However, depending on the facility, it may be difficult to attach a sensor. The purpose of this study is to propose a power generation maintenance plan system based on failure record data obtained from Continuous Ship Unloader, one of the facilities that is difficult to attach sensors. Methods: This study uses data collected from 2012 to 2022 from the 'CSU-1B' model among Continuous Ship Unloader operated by Korea Midland Power Co., LTD. By fitting fault record data to the Weibull distribution, appropriate maintenance cycles and ranges for each target facility subsystem are derived. In addition, maintenance group between subsystems is selected through Euclidean distance, a metric often used for time series data similarity. Through this, a system for establishing an maintenance plan for power generation facilities is proposed. Results: The results of this study are as follows. For the 17 subsystems of the Continuous Ship Unloader, proper maintenance cycles and ranges were determined, and a total of four maintenance groups were chosen. This resulted in the creation of an power generation maintenance plan system and the establishment of an maintenance plan. Conclusion: This study is a case study of power generation facilities. We proposed a maintenance plan system for Continuous Ship Unloader among power generation facilities.

• Journal of Korean Tunnelling and Underground Space Association
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• v.25 no.6
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• pp.495-507
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• 2023

Considering the continuing discussion about the Korea-Japan undersea tunnel, it is necessary to conduct a scientific investigation into tunnel deformation associated with large ground movements at fault. This paper presents findings obtained from numerical experiments to investigate a seismic lining that adopts rubber-like material. We utilized the user material subroutine to obtain the deformation gradient of the hyperelastic material. Additionally, polar decomposition is used to analyze the results, where the data is displayed on a series of two-dimensional planes using the principal direction, which facilitates a better insight into the deformation. Tunnel engineers could refer to this paper for the procedure to investigate the deformation of hyperelastic material.

• Journal of the Korean earth science society
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• v.39 no.2
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• pp.178-192
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• 2018

The purposes of this study were to develop and apply on learning on geological field trip utilizing the social construction of scientific model. We developed field trip places by considering not only Orion (1993)'s novelty space but also the achievement standards of 2015 national curriculum. The subjects of the study were 8 in the 'G' science gifted education center. We conducted a study using the theme of 'How was formed Mt. Gwanak?' on 5 lessons including a series of 2 field trip lessons and 3 lessons utilizing the social construction of scientific model. Students participated in pre- and post-test on the understanding of scientific knowledge about formation of mountain. Semi-structured interview was used to analyze students' learning about geological field trip in terms of affective domain. Results were as follows. First, there were 2 places of upper-stream valley and down-stream valley separately. They contained outcrops gneiss, granite, joint in the valley, xenolith, fault plane, mineral in the valley. Second, pre- and post-test and semi-structure interview were analyzed in terms of what scientific knowledge students learned about and how Mt. Gwanak was formed. Seven students explained that Mt. Gwanak was volcano during pretest. Seven students described how granite was formed to form Mt. Gwanak. They also understood geological time scale, i.e., metamorphic rock. Third, the geological field trip was effective to low achievement geoscience students as they engaged in the activities of field trip. Using positive responses on affective learning was effective on learning on geological field trip when utilizing the social construction of scientific model. This study suggests that teachers use an example 'model' on geoscience education. This study also suggests that teachers apply the social construction of scientific model to geological field trip.

• The Journal of Engineering Geology
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• v.24 no.2
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• pp.247-260
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• 2014

Rock masses at the site for the $2^{nd}$ step construction of the KAERI Underground Research Tunnel (KURT) are divided into six units to establish a rock mechanics model that is dependent on the geological characteristics and degree of joint development. The site primarily consists of three granitic units (G1, G2, and G3), two dykes (D1 and D3), and a fault zone of poor rock mass quality (F3). The F3 unit crosses the tunnel at the beginning of the site of $2^{nd}$ step construction. The rock masses of each unit are classified by RMR (Rock Mass Rating), Q-system, and RMi (Rock Mass Index), all based on borehole logging data. The deformation modulus, rock mass strength, cohesion, and friction angle for each unit are calculated using established empirical relationships. The representative rock mass classification and geotechnical parameters for the rock mass units are established, and a rock mechanics model for the site is proposed, which will be useful in the design and stability analysis of the $2^{nd}$ step construction of KURT.