• Proceedings of the Korea Concrete Institute Conference
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• 2001.11a
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• pp.257-262
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• 2001

There are only a few tests to ensure concrete quality before placing in domestic situ; One is slump test for workability, the other is air content test for durability, the concrete compressive strength which is one of important factors to influence on concrete Quality has been tested after 28 days placing. Methods on early judgement of concrete strength have been introduced for concrete quality management, but such methods are time consuming, expensive, and required special expertise. Therefore, these have difficulty in situ application for concrete management. This study aimed at reviewing application of estimated equation of compressive strength as means for ready-mixed concrete, making an estimated equation which enables to estimate 28 days compressive strength by using regression formula analysis on basis of mixing designs of ready mixed concrete and results of compressive strength.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.10
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• pp.1636-1643
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• 2003

In this study, bolted joint made of Glass-Mat Reinforced Thermoplastics (GMT) specimen was under tensile loading to investigate the relation between joint strength and glass-fiber weight fraction of the flat plate specimen. The effect of molding conditions such as the initial size of a GMT charge and molding temperatures was investigated under plane strain condition. In consideration of the specimen geometry, minimum end distance and width of the specimen to induce the bearing fracture mode of the bolted joint were determined. And finally, the effect of the outer diameter of washer and clamping pressure on joint strength was also investigated. Since joint strength is dependent on the local glass-fiber weight fraction, experimentally measured strength was modified, considering its irregular values of the specimen molded under various processing conditions in order to obtain a reasonable correlation between the two.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2017.05a
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• pp.152-153
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• 2017

In the construction site, it is necessary to estimate the compressive strength of concrete in order to adjust the demolding time of the form, and establish and adjust the construction schedule. The compressive strength of concrete is determined by various influencing factors. However, the conventional method for estimating the compressive strength of concrete has been suggested by considering only 1 to 3 specific influential factors as variables. In this study, six influential factors (Water, Cement, Fly ash, Blast furnace slag, Curing temperature, and humidity) of papers opened for 10 years were collected at three conferences in order to know the various correlations among data and the tendency of data. After using algorithm of various methods of machine learning techniques, we selected the most suitable regression analysis model for estimating the compressive strength.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2018.05a
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• pp.130-131
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• 2018

The compressive strength of concrete is determined by various influencing factors. However, the conventional method for estimating the compressive strength of concrete has been suggested by considering only 1 to 3 specific influential factors as variables. In this study, nine influential factors (W/B ratio, Water, Cement, Aggregate(Coarse, Fine), Fly ash, Blast furnace slag, Curing temperature, and humidity) of papers opened for 10 years were collected at 4 conferences in order to know the various correlations among data and the tendency of data. The selected mixture and compressive strength data were learned using the Deep Learning Algorithm to derive an estimated function model. The purpose of this study is to investigate the effect of the number of hidden layers on the prediction performance in the process of estimating the compressive strength for an arbitrary combination.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.6
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• pp.780-785
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• 2014

To assess the deterioration condition of stator insulation, diagnostic and AC dielectric strength tests were performed on five high voltage (HV) motors (2,000 HP, 6.6 kV) for boiler feed-water pump (BFP). Two HV motors for BFP were installed per unit. Following the long term rewinding program, the diagnostic test was performed on five 6.6 kV motors during the planning maintenance period. After completing diagnostic test, AC dielectric strength test was done on the stator windings of HV motors. The AC dielectric strength test was conducted at 15 kV for one minute. Dielectric strength test and diagnostics test results confirmed that the stator insulation was judged to be in serviceable condition in the five 6.6 kV motors.

• Advances in concrete construction
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• v.6 no.5
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• pp.527-543
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• 2018

Crack control of precast members is crucial for durability. However, there is no clear provision to check the crack width of precast joints. This study presents an experimental investigation of loop joint details for use in a precast bridge deck system. High strength concrete of 130 MPa was chosen for durability and closer joint spacing. Static tests were conducted to investigate the cracking and ultimate behavior of test specimens. The experimental results indicate that current design codes provide reasonable estimation of the flexural strength and cracking load of precast elements with loop joint of high strength concrete. However, the crack width control of the loop joints with high strength concrete by the current design practices was not appropriate. Some recommendations to improve crack control of the loop joint were derived.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.04a
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• pp.61-68
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• 2002

Tubular members have been applied in a wide range of frame structures including offshore structures. For the efficient load flow in tubular-member structures, the joints of tubular members are usually reinforced using internal ring stiffener for the steel tubular joint having a large diameter. The objective of this paper is to numerically assess the behavior of X-joints with an internal ring stiffener, and to evaluate the reinforcement effect of a ring stiffener, and to establish the strength formulae. Nonlinear finite element analysis is used to compute the static strength of axially loaded tubular joints. From the numerical results, internal ring stiffener is found to be efficient in improving static strength of tubular X-joints. Maximum strength ratios are calculated as 1.5~3.5, and the effective dimensions of ring stiffener are found. Regression analyses are performed considering practical size of ring stiffener and strength estimation formulae are proposed.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2017.11a
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• pp.83-84
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• 2017

In the construction site, it is necessary to estimate the compressive strength of concrete in order to adjust the demolding time of the form, and establish and adjust the construction schedule. The compressive strength of concrete is determined by various influencing factors. However, the conventional method for estimating the compressive strength of concrete has been suggested by considering only 1 to 3 specific influential factors as variables. In this study, seven influential factors (W/B ratio, Water, Cement, Fly ash, Blast furnace slag, Curing temperature, and humidity) of papers opened for 10 years were collected at three conferences in order to know the various correlations among data and the tendency of data. The purpose of this paper is to estimate compressive strength more accurately by applying it to algorithm of the Deep learning.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.06a
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• pp.25-26
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• 2020

In this study, by applying the concrete compressive strength estimation system Concrete IoT Management System (hereinafter referred to as CIMS) to the concrete slab concrete in the domestic field, the purpose of this study is to confirm the practical use of CIMS and to verify the accuracy of estimating the initial strength of concrete. As a result, it shows a high correlation when the compressive strength and CIMS estimated strength of the specimen for structural management are converted and compared with the integrated temperature. However, in order to determine a more accurate experimental constant, it is necessary to consider the results up to 28 days.

• ETRI Journal
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• v.40 no.1
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• pp.122-132
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• 2018

This paper proposes a hierarchical dual filtering (HDF) algorithm to estimate the spatial region between a Cloud of Things (CoT) gateway and an Internet of Things (IoT) device. The accuracy of the spatial region estimation is important for autonomous CoT clustering. We conduct spatial region estimation using a hidden Markov model (HMM) with a raw Bluetooth received signal strength indicator (RSSI). However, the accuracy of the region estimation using the validation data is only 53.8%. To increase the accuracy of the spatial region estimation, the HDF algorithm removes the high-frequency signals hierarchically, and alters the parameters according to whether the IoT device moves. The accuracy of spatial region estimation using a raw RSSI, Kalman filter, and HDF are compared to evaluate the effectiveness of the HDF algorithm. The success rate and root mean square error (RMSE) of all regions are 0.538, 0.622, and 0.75, and 0.997, 0.812, and 0.5 when raw RSSI, a Kalman filter, and HDF are used, respectively. The HDF algorithm attains the best results in terms of the success rate and RMSE of spatial region estimation using HMM.