• Tunnel and Underground Space
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• v.16 no.3 s.62
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• pp.232-240
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• 2006

In this paper, the blasting load has been calculated using Input Identification method and measured data in borehole blasting to reflect the exact blast behavior and soil vibration. The fitness of calculated blasting load is examined by comparing measured data and results of numerical analysis. According to the results, blasting load estimated by Input Identification method was more adequate than proposed blasting pressure equation in the reflection of blast behavior and soil vibration. In addition, it showed more reasonable results at the evaluation of structure's vibration in the 3D finite element method.

• Journal of the Korea institute for structural maintenance and inspection
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• v.7 no.4
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• pp.209-216
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• 2003

Load carrying capacity evaluation is very important element in maintenance of bridge. There are several reasons about differences in deflection caused by loading test and structural analysis. Especially when we do modeling uniformly without considering real structural characteristics of support, this problem can be more deepened. This computes modification factor high so we may evaluate the load carrying capacity more than fact. In this study, we do structural analysis nearing real structure with negative bending moment of support that computes considering structural characteristics of support, and then evaluate load carrying capacity.

• Wind and Structures
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• v.31 no.6
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• pp.575-591
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• 2020

Wind load is typically considered as one of the governing design loads acting on a structure. Understanding its nature is essential in evaluation of its action on the structure. Many codes and standards are founded on state of the art knowledge and include step by step procedures to calculate wind loads for various types of structures. One of the most accepted means for calculating wind load is using Gust Load Factor or base bending Moment Gust Load Factor (MGLF), where codes are adjusted based on local data available. Although local data may differ, the general procedure is the same. In this paper, ASCE 7-16 (2017), which is used as the main reference in the U.S., and Korean Building Code (KBC 2016) are compared in evaluation of wind loads. The primary purpose of this paper is to provide insight on each code from a structural engineering perspective. Herein, discussion focuses on where the two codes are compatible and differ. In evaluating the action of wind loads on a building, knowledge of the dynamic properties of the structure is critical. For this study, the design of four figurative high-rise buildings with dual systems was analyzed.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.58 no.4
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• pp.359-366
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• 2022

In this study, the load fluctuation of the main engine is considered to be a disturbance for the jacket coolant temperature control system of the low-speed two-stroke main diesel engine on the ships. A nonlinear PID temperature control system with satisfactory disturbance rejection performance was designed by rapidly transmitting the load change value to the controller for following the reference set value. The feed-forwarded load fluctuation is considered the set points of the dual loop control system to be changed. Real-coded genetic algorithms were used as an optimization tool to tune the gains for the nonlinear PID controller. ITAE was used as an evaluation function for optimization. For the evaluation function, the engine jacket coolant outlet temperature was considered. As a result of simulating the proposed cascade nonlinear PID control system, it was confirmed that the disturbance caused by the load fluctuation was eliminated with satisfactory performance and that the changed set value was followed.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.26 no.4
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• pp.436-440
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• 2017

This study evaluated the design and structural integrity of a three-axle bogie frame in a railway freight car through a numerical analysis and an experimental evaluation. A three-axle bogie frame, which supports the weight of the car body and load, is required to transport heavier cargo because two-axle vehicles have structural limitations. Therefore, this study performed a structural analysis and static load tests to evaluate the design and structural integrity of a three-axle bogie frame. The results obtained from the numerical analysis were compared to those of the experiments. For the bogie frame used in the experiments, a failure evaluation was performed using non-destructive methods. The numerical analysis and experimental evaluation were satisfactory for the structural integrity evaluation.

• Special Issue of the Society of Naval Architects of Korea
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• 2015.09a
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• pp.69-73
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• 2015

An evaluation of the accidental limit state (ALS) for design of a semi-submersible drilling rig is one of the essential design requirements as well as ultimate limit state (ULS) and fatigue limit state (FLS). This paper describes the ALS evaluation on the explosion accident at shale shaker room of semi-submersible drilling rig. There are three steps for the ALS evaluation such as structural analysis at concept design, risk based safety design and structural analysis at detailed design. For the ALS evaluation at concept design, conceptual explosion overpressure from the Rule guided by the classification society was used in the structural analysis that was carried out using LS-DYNA. To set up the design accidental load (DAL), explosion analysis was carried out using FLACS taking safety barriers into consideration. Then, the structural analysis was carried out applying DAL for the ALS evaluation at detailed design. Through the ALS evaluation on the explosion at shale shaker room, the importance of the risk based safety design was described.

• Journal of Ocean Engineering and Technology
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• v.10 no.2
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• pp.98-105
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• 1996

A method is proposed to evaluate the elastic-plastic fracture resistance curve only with load displacement records without the crack length measurement in CT specimen. This method is based on the idea that the effect of plastic deformation and the crack growth can be measured only by using a load-displacement record. If we know the reference-load curve representing the hardening of specimen, then the crack extension can be calculated by the elastic compliance determined from the load ratio. The results of this proposed method were compared to those of the elastic-plastic fracture resistance curve for the ASTM standard unloading compliance method. The experimental results for two kinds of ductile materials showed that the proposed method well simulates the material J-R curves. This method is currently applied for CT specimens. but it can be extended to the other specimen geometries.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.57 no.2
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• pp.96-101
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• 2008

The field of load modeling has attracted the attention since it plays an important role for improving the accuracy of stability analysis and power flow estimation. Also, load modeling is an essential factor in the simulation and evaluation of power system performance. However, conventional load modeling techniques have some limitations with respect to accuracy for nonlinear and composite loads. Thus, precision load modeling technique and reasonable application method is needed for more accurate power system analysis. In this paper, we develop an intelligent load modeling method based. on neural network and application techniques for power system. The proposed method makes it possible to effectively estimate the load model for nonlinear models as well as linear models. Reasonable application method is also proposed for stability analysis. To demonstrate the validity of the proposed method, various experiments are performed and their results are presented.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.4
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• pp.633-636
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• 2022

The reduction of energy consumption at the base station (BS) has become more important recently. In this paper, we consider the adaptive muting of the antennas based on the predicted future traffic load to reduce the energy consumption where the number of active antennas is adaptively adjusted according to the predicted future traffic load. Given that traffic load is sequential data, three different RNN structures, namely long-short term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (Bi-LSTM) are considered for the future traffic load prediction. Through the performance evaluation based on the actual traffic load collected from the Afghanistan telecom company, we confirm that the traffic load can be estimated accurately and the overall power consumption can also be reduced significantly using the antenna musing.

• KEPCO Journal on Electric Power and Energy
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• v.7 no.1
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• pp.115-123
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• 2021

Through machine learning-based load prediction, it is possible to prevent excessive power generation or unnecessary economic investment by estimating the appropriate amount of facility investment in consideration of the load that will increase in the future or providing basic data for policy establishment to distribute the maximum load. However, in order to secure the reliability of the developed load prediction model in the field, the performance comparison verification between the distribution line load prediction models must be preceded, but a comparative performance verification system between the distribution line load prediction models has not yet been established. As a result, it is not possible to accurately determine the performance excellence of the load prediction model because it is not possible to easily determine the likelihood between the load prediction models. In this paper, we developed a reliability verification system for load prediction models including a method of comparing and verifying the performance reliability between machine learning-based load prediction models that were not previously considered, verification process, and verification result visualization methods. Through the developed load prediction model reliability verification system, the objectivity of the load prediction model performance verification can be improved, and the field application utilization of an excellent load prediction model can be increased.