• Proceedings of the Korean Institute of Building Construction Conference
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• 2008.05a
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• pp.209-213
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• 2008

Recently, it increases in use of CFT(Concrete filled steel tube, below CFT) because material and method are required to be diversification and High-Performance according to increase the super-high structure. But, CFT column lose bearing capacity under fire because steel tube is exposed to outside. As a result, structure is collapsed and then it cause much damage. In case of the Europe, Japan and America, they have studied the fire-resistance performance of CFT under fire for a long time. However, it would have hardly studied it in domestic because it is much difficulty about experiment machine and cost. So it is needed base on fire-resist performance of CFT under fire. Therefore, this study dynamic specificity of stub column which made tester of stub column based on facts of strength and mixing fiber evaluated used heating and load testing machine. As a result, it is willing to propose fundamental data for quick and accurate diagnosis of deteriorated concrete structure by fire damage with experiment according to the design high strength concrete.

• Journal of Biosystems Engineering
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• v.6 no.1
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• pp.48-59
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• 1981

The major limiting factor on the determination of combine capacity is the frequent occurence of clogging over the some parts of machine when the crop is wet in the case of Japanese self-feeding type combine. And in the case of American conventional combine having big separating parts, the great grain loss and damage occur when the machine is used for rice harvesting. This experiment was carried out to develop the new type threshing and separating equipment. Proto-type thresher which consist of a conical threshing drum and a conical separating sieve rotating around the threshing cone was constructed and tested. In the case of 800 rpm of threshing cone speed, average threshing loss was below 1 percent, separating loss was about 1 percent, grain damage was about 0.4 percent, and average total power required was about 2.6 PS. This design has some problems such as higher power required or wrapping problems under the conditions of feeding long damp straw. But, compared with the conventional combine or thresher, this machine certainly has some potentials for this approach to combine development. The crop feed rate must be increased through improvement of the feeding portion of the threshing cone. And it is required to investigate further about some parameters causing wrapping phenomena.

• Earthquakes and Structures
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• v.25 no.6
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• pp.469-479
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• 2023

A probabilistic seismic damage analysis is an essential procedure to identify seismically vulnerable structures, prioritize the seismic retrofit, and ultimately minimize the overall seismic risk. To assess the seismic risk of multiple structures within a region, a large number of nonlinear time-history structural analyses must be conducted and studied. As a result, each assessment requires high computing resources. To overcome this limitation, we explore a deep learning-based metamodel to enable the prediction of the mean and the standard deviation of the seismic damage distribution of track-on steel-plate girder railway bridges in Korea considering the geometric variation. For machine learning training, nonlinear dynamic time-history analyses are performed to generate 800 high-fidelity datasets on the seismic response. Through intensive trial and error, the study is concentrated on developing an optimal machine learning architecture with the pre-identified variables of the physical configuration of the bridge. Additionally, the prediction performance of the proposed method is compared with a previous, well-defined, response surface model. Finally, the statistical testing results indicate that the overall performance of the deep-learning model is improved compared to the response surface model, as its errors are reduced by as much as 61%. In conclusion, the model proposed in this study can be effectively deployed for the seismic fragility and risk assessment of a region with a large number of structures.

• Geomechanics and Engineering
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• v.37 no.4
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• pp.395-418
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• 2024

The epicentral region of earthquakes is typically where liquefaction-related damage takes place. To determine the maximum distance, such as maximum epicentral distance (R_e), maximum fault distance (R_f), or maximum hypocentral distance (R_h), at which an earthquake can inflict damage, given its magnitude, this study, using a recently updated global liquefaction database, multiple ML models are built to predict the limiting distances (R_e, R_f, or R_h) required for an earthquake of a given magnitude to cause damage. Four machine learning models LSTM (Long Short-Term Memory), BiLSTM (Bidirectional Long Short-Term Memory), CNN (Convolutional Neural Network), and XGB (Extreme Gradient Boosting) are developed using the Python programming language. All four proposed ML models performed better than empirical models for limiting distance assessment. Among these models, the XGB model outperformed all the models. In order to determine how well the suggested models can predict limiting distances, a number of statistical parameters have been studied. To compare the accuracy of the proposed models, rank analysis, error matrix, and Taylor diagram have been developed. The ML models proposed in this paper are more robust than other current models and may be used to assess the minimal energy of a liquefaction disaster caused by an earthquake or to estimate the maximum distance of a liquefied site provided an earthquake in rapid disaster mapping.

• Journal of Advanced Navigation Technology
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• v.23 no.1
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• pp.84-89
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• 2019

This is a pilot study of machine learning based structural health monitoring system using flight data of composite aircraft. In this study, the most suitable machine learning algorithm for structural health monitoring was selected and dimensionality reduction method for application on the actual flight data was conducted. For these tasks, impact test on the cantilever beam with added mass, which is the simulation of damage in the aircraft wing structure was conducted and classification model for damage states (damage location and level) was trained. Through vibration test of cantilever beam with fiber bragg grating (FBG) sensor, data of normal and 12 damaged states were acquired, and the most suitable algorithm was selected through comparison between algorithms like tree, discriminant, support vector machine (SVM), kNN, ensemble. Besides, through neighborhood component analysis (NCA) feature selection, dimensionality reduction which is necessary to deal with high dimensional flight data was conducted. As a result, quadratic SVMs performed best with 98.7% for without NCA and 95.9% for with NCA. It is also shown that the application of NCA improved prediction speed, training time, and model memory.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.91-98
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• 2023

With the increasing flood damage by frequently localized heavy rains, flood prediction research are being conducted to prevent flooding damage in advance. In this paper, we present a machine-learning scheme for developing a flooding depth and location prediction model using real-time rainfall data. This scheme proposes a dataset configuration method using the data as input, which can robustly configure various rainfall distribution patterns and train the model with less memory. These data are composed of two: valid total data and valid local. The one data that has a significant effect on flooding predicted the flooding location well but tended to have different values for predicting specific rainfall patterns. The other data that means the flood area partially affects flooding refers to valid local data. The valid local data was well learned for the fixed point method, but the flooding location was not accurately indicated for the arbitrary point method. Through this study, it is expected that a lot of damage can be prevented by predicting the depth and location of flooding in a real-time manner.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.5 no.4
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• pp.72-78
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• 2006

Inherent in machine tool structures are the vibrations which are generated by rotating parts such as motors, spindles and chucks. The vibrations not only hurt the precision machining but also damage the structures, and become more serious with time. Many of the old machine tools show severe vibrations for the desired accuracy of the modern industries. It is too much of a waste, however, to get rid of them as scraps. There have been many researches in order to suppress the vibrations of old machine tool structures using the tool post which utilizes actuators to compensate the existing vibrations and using the dampers or absorbers attached to some critical parts. In this paper, the dynamic properties are analyzed to obtain the natural frequencies and mode shapes of a machine tool structure which reflect the main reasons of the biggest vibrations under the given operating conditions. And the feasibility of improving the stability of the structure has been investigated with minor design changes and expenses. The result of the study shows that simple changes based on proper system identification can considerably improve the stability of the machine tool structure.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11c
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• pp.722-728
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• 2000

This study of field control machine in the recycled vinyl rail is gantry crane type and promoting agricultural automatization through self-controlled spraying, harvesting and conveyance. In addition to, that control machine could get a cost and labor reduction effect through automatization and make better environment by preventing farmers from agrichemical damage, accidents and recycling wasted vinyl. That machine is able to be divided as traveling, spraying, harvesting and conveyance sections. In driving section consists of girder frame, carrier, rail, control system, driving system, working machine, rail and loading device for working machine. This machine has following advantages to be able to bring a big innovation in the agricultural industry. I) Accurate performance is able to be done by proper positioning due to based on the rails. 2) The soil is not made hard like heavy tractor 3) The wheel is not sank into the soil and slipped well under rain like heavy tractor. Therefore, weather and soil situation could not affect working condition. 4) Complete unmanned control and 24hours-working are available due to traveling on the rails. 5) It could use various energy resources like not only liquid fuel but also solar, common electronic power due to traveling on the rails.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.52
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• pp.171-180
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• 1999

Industrial equipment reliability improvement and maintenance is gaining attention as the next great opportunity for manufacturing productivity improvement. Reactive maintenance is expensive because of extensive unplanned downtime and damage to machinery. To avoid such an unplanned machine downtime, it is needed to use proactive maintenance approach by either using historical maintenance data or by sensing machine conditions. This paper discusses failure diagonosis and prediction based on the condition-based maintenance and reliability technique. Thus, by enabling such a framework, it can bring us more efficient planning and execution of maintenance to reduce costs and/or increase profits.

• Journal of Biosystems Engineering
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• v.22 no.3
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• pp.317-324
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• 1997

This study was conducted to develope a machine vision system for a robotic transplanter for bedding plants. Specific objectives of this study were 1) to get coordinates of the healthy seedlings in high-density plug tray, and 2) to get the angle of the leaves of the healthy seedlings to avoid the damage to seedlings by gripper. Results of this study were summarized as follows. (1) The machine vision system of a robotic transplanter was developed. (2) Success rates of detecting empty cell and bad seedlings for 72-cell and 128-cell plug-trays were 98.8% and 94, 9% respectively. (3) Success rates of calculating the angle of leaves fer 72-cell and 128-cell plug-trays were 93.5% and 91.0% respectively.