• Wind and Structures
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• v.22 no.2
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• pp.235-252
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• 2016

A wind velocity power spectrum (WVPS) with high fidelity is extremely important for accurate prediction of structural buffeting response. WVPS heavily depends on the geographical locations, local terrains and topographies. Hence, field measurement of wind characteristics may be the unique way to obtain the accurate WVPS for a specific region. In this paper, a systematic analysis and discussions of existing WVPSs were performed. Six recorded strong wind data from the structural health monitoring systems (SHMS) of Runyang Suspension Bridge (RSB) and Sutong Cable-stayed Bridge (SCB) in Jiangsu Province of China were selected for analysis. The measured and pre-processed wind velocity data was first transformed from time domain to frequency domain to obtain the measured spectrum. The spectrum for each strong wind was then fitted using the nonlinear least square method and compared with both the fitted spectrum from statistical analysis and the recommended spectrum in specifications. The modified Kaimal spectrum was proved to be the "best" choice for the coastal area of East Jiangsu Province. Finally, a suitable WVPS formula fit for the coastal area of East Jiangsu Province was presented based on the modified Kaimal spectrum. Results in this study provide a more accurate and reliable WVPS for wind-resistant design of engineering structures in the coastal area of East Jiangsu Province.

• Journal of the Society of Naval Architects of Korea
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• v.35 no.1
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• pp.1-14
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• 1998

A two-dimensional numerical model for tidal currents based on the depth averaged equation is developed. The mode1 employs a rectangular grid system for its simplicity in the application of complicate coastal shore lines. To raise computing efficiency, implicit approximate factorization scheme is implemented in solving governing equations. An upwind-differencing is used to discretize convective terms, which provides a numerical dissipation automatically and suppresses any oscillations caused by nonlinear instabilities. Some numerical tests are made against the analytic solutions of a linearized shallow water equation to validate the developed numerical scheme, and comparisons of the model prediction with the analytic solution are satisfactory. As a real application, the tidal currents are computed on the Inchon area where the tidal currents are important for the design of new canal which is under construction.

• Proceedings of the Korea Concrete Institute Conference
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• 2004.11a
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• pp.501-504
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• 2004

This paper proposes degree of hydration based shrinkage prediction model of 40MPa HPC. This model shows degree of hydration which is defined as the ratio between the hydrated cement mass and the initial mass of cement is very closely related to shrinkage deformation. In this study, degree of hydration was determined by CEMHYD-3D program of NIST. Verification of the predicted degree of hydration is performed by comparison between test results of compressive strength and estimated one by CEMHYD-3D. Proposed model is determined by statistical nonlinear analysis using the program Origin of Origin Lab. Co. To get coefficients of the model, drying shrinkage tests of four specimen series were followed with basic material tests. Testes were performed in constant temperature /humidity chamber, with difference moisture curing ages to know initial curing time effect. Verification with another specimen, collected construction field of FCM bridge, was given in the same condition as pre-tested specimens. Finally, all test results were compared to propose degree of hydration based model and other code models; AASHTO, ACI, CEB-FIP, JSCE, etc.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2009.10a
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• pp.187-188
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• 2009

On this study, the artificial neural network, one of the nonlinear forecasting methods, is compared with ARIMA model through performing a forecast of container traffic. The existing studies have been used the rule of thumb in topology design for network which had a great effect on forecasting performance of the artificial neural network. However, this study applied the genetic algorithm, known as the effectively optimal algorithm in the huge and complex sample space, as the alternative.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.7 no.1
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• pp.108-115
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• 1995

An efficient model for the dynamic analysis of caisson breakwaters under impulsive wave loadings is presented. The caisson structure is. regarded as a rigid body, and the rubble mound foundation is idealized as virtual added masses, springs, and dampers using the elastic half-space theory. The frequency-dependent hydrodynamic added mass and damping coefficients are considered by using the time memory functions and added mass at infinite frequency. To simulate the permanent sliding phenomenon of the caisson, the horizontal spring is modeled as a nonlinear spring with plastic behaviors. Comparisons with experimental results show that the present model gives fairly good results. Sensitivity analysis is performed for the relevant parameters affecting the dynamic responses of a caisson breakwater. Numerical experiments are also carried out to investigate the applicability to the prediction of permanent sliding distance and critical weight of the caisson.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.367-367
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• 2012

기후변화로 인해 기온, 강수량, 습도 등의 기후를 예측하고 변화하는 환경에 적응해가며 생활하고 있다. 또한 여러 가지 외부적인 요인들의 영향을 받아 상수도 시설에서의 에너지 사용량도 영향을 많이 받는다. 하지만 이러한 상수도 시설의 사용량 변화로 인해 상수도 수요량의 변화량을 예측하는데 있어서 국내 연구 및 방법이 많이 부족한 상황이다. 이에 본 연구에서는 다중스케일을 기반으로 하는 비선형 예측 모형을 개발하고자 한다. 다중스케일 분석에서도 가장 우수한 분해 능력을 가지는 Wavelet Transform을 적용하여 시계열을 분해한 후 패턴인식 기반의 비선형 예측모형인 Support Vector Machine(SVM)을 적용하였다. 상수도 수요량의 예측 과정은 다음과 같다. 첫째, 상수도 수요량 자료를 Wavelet Transform 기법을 통하여 단순화 시킨다. 둘째, Global Wavelet Spectrum을 통하여 통계적으로 의미 있는 성분만을 추출하고 이를 해석 대상으로 한다. 셋째, 특정 주기를 갖는 유의한 독립성분들에 대해서 최적 지체시간을 결정한 후 SVM모형을 통해 예측 모형을 구축한다. 넷째, 나머지 성분에 대해서도 SVM 모형을 적용하여 예측을 실시한 후 앞서 예측된 성분과 모두 결합하여 최종적으로 예측시계열을 구성한다.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.4
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• pp.352-361
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• 2004

One important element of the Leak-Before-Break analysis of nuclear piping is how to determine relevant fracture toughness (or the J-resistance curve) for nonlinear fracture mechanics analysis. The practice to use fracture toughness from a standard C(T) specimen is known to often give conservative estimates of toughness. To improve the accuracy, this paper proposes a new method to determine fracture toughness using a nonstandard testing specimen, curved wide-plate in tension. To show validity of the proposed curved wide-plate test, the J-resistance curve from the full-scale pipe test is compared with that from the curved wide-plate test and that from the C(T) specimen. It is shown that the J-resistance curve form the curved wide-plate tension test is similar to, but that from the C(T) specimen is lower than, the J-resistance curve from the full-scale pipe test. Further validation is performed by investigating crack-tip constraint conditions via detailed 3-D FE analyses, which shows that the crack-tip constraint condition in the curved wide-plate tension specimen is indeed similar to that in the full-scale pipe under bending.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.1
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• pp.87-93
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• 2008

Seismic control performance of MR dampers, which have severe nonlinearity, varies with respect to the dynamic characteristics of an earthquake such as magnitude, frequency and duration. In this study, the effects of excitation characteristics on the equivalent linear system of a building structure with the MR damper are investigated through numerical analysis for artificial ground motions generated from different response spectrums. The equivalent damping ratio of the structure with the MR damper is calculated using Newmark and Hall's equations for ground motion amplification factors. It is found that the equivalent damping ratio of the structure with the MR damper is dependent on the ratio of the maximum friction force of the MR damper over excitation magnitude. Frequency contents of the earthquake ground motion affects the equivalent damping ratio of long-period structures considerably. Also, additional damping effect caused by interaction between the viscousity and friction of the MR damper is observed. Finally. response reduction factors for equivalent linear systems are proposed in order to improve accuracy in the prediction of the actual nonlinear response.

• Smart Structures and Systems
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• v.24 no.6
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• pp.733-744
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• 2019

Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.150-150
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• 2018

The Numerical Weather Prediction (NWP) models provide information for weather forecasts. The highly nonlinear and complex interactions in the atmosphere are simplified in meteorological models through approximations and parameterization. Therefore, the simplifications may lead to biases and errors in model results. Although the models have improved over time, the biased outputs of these models are still a matter of concern in meteorological and hydrological studies. Thus, bias removal is an essential step prior to using outputs of atmospheric models. The main idea of statistical bias correction methods is to develop a statistical relationship between modeled and observed variables over the same historical period. The Model Output Statistics (MOS) would be desirable to better match the real time forecast data with observation records. Statistical post-processing methods relate model outputs to the observed values at the sites of interest. In this study three methods are used to remove the possible biases of the real-time outputs of the Weather Research and Forecast (WRF) model in Imjin basin (North and South Korea). The post-processing techniques include the Linear Regression (LR), Linear Scaling (LS) and Power Scaling (PS) methods. The MOS techniques used in this study include three main steps: preprocessing of the historical data in training set, development of the equations, and application of the equations for the validation set. The expected results show the accuracy improvement of the real-time forecast data before and after bias correction. The comparison of the different methods will clarify the best method for the purpose of the forecast skill enhancement in a real-time case study.