• Journal of Korea Water Resources Association
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• v.53 no.10
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• pp.917-928
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• 2020

The purpose of this study is to find the appropriate probability distribution representing the size distribution of suspended cohesive sediment. Based on goodness-of-fit test for a significance level of 5% using the Kolmogorov-Smirnov test, it is found that the floc size distributions measured in laboratory experiment and field study show different results. In the case of sample data collected from field experiments, the Gamma distribution is the best fitting form. In the case of laboratory experiment results, the sample data shows the positively-skewed distribution and the GEV distribution is the best fitted. The lognormal distribution, which is generally assumed to be a floc size distribution, is not suitable for both field and laboratory results. By using 3-parameter lognormal distribution, it is shown that similar size distribution with floc size distribution can be simulated.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.6
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• pp.133-142
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• 2012

The objective of this study was to correct the bias of the Representative Concentration Pathways (RCP)-based future precipitation data using a quantile mapping method. This method was adopted to correct extreme values because it was designed to adjust simulated data using probability distribution function. The Generalized Extreme Value (GEV) distribution was used to fit distribution for precipitation data obtained from the Korea Meteorological Administration (KMA). The resolutions of precipitation data was 12.5 km in space and 3-hour in time. As the results of bias correction over the past 30 years (1976~2005), the annual precipitation was increased 16.3 % overall. And the results for 90 years (divided into 2011~2040, 2041~2070, 2071~2100) were that the future annual precipitation were increased 8.8 %, 9.6 %, 11.3 % respectively. It also had stronger correction effects on high value than low value. It was concluded that a quantile mapping appeared a good method of correcting extreme value.

• Wind and Structures
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• v.38 no.4
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• pp.309-323
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• 2024

The gust factor and turbulence intensity are two crucial parameters that characterize the properties of turbulence. In tropical cyclones (TCs), these parameters exhibit significant variability, yet there is a lack of established formulas to account for their probabilistic characteristics with consideration of their inherent connection. On this condition, a probabilistic analysis of gust factors and turbulence intensities of TCs is conducted based on fourteen sets of wind data collected at the Sutong Cable-stayed Bridge site. Initially, the turbulence intensities and gust factors of recorded data are computed, followed by an analysis of their probability densities across different ranges categorized by mean wind speed. The Gaussian, lognormal, and generalized extreme value (GEV) distributions are employed to fit the measured probability densities, with subsequent evaluation of their effectiveness. The Gumbel distribution, which is a specific instance of the GEV distribution, has been identified as an optimal choice for probabilistic characterizations of turbulence intensity and gust factor in TCs. The corresponding empirical models are then established through curve fitting. By utilizing the Gumbel distribution as a template, the nexus between the probability density functions of turbulence intensity and gust factor is built, leading to the development of a generalized probabilistic model that statistically describe turbulence intensity and gust factor in TCs. Finally, these empirical models are validated using measured data and compared with suggestions recommended by specifications.

• International Journal of Naval Architecture and Ocean Engineering
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• v.7 no.6
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• pp.1056-1063
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• 2015

Waves can be expressed in terms of a spectrum; that is, the energy density distribution of a representative wave can be determined using statistical analysis. The JONSWAP, PM and BM spectra have been widely used for the specific target wave data set during storms. In this case, the extracted wave data are usually discontinuous and independent and cover a very short period of the total data-recording period. Previous studies on the continuous wave spectrum have focused on wave deformation in shallow water conditions and cannot be generalized for deep water conditions. In this study, the Generalized Extreme Value (GEV) function is proposed as a more-optimal function for the fitting of the continuous wave spectral shape based on long-term monitored point wave data in deep waters. The GEV function was found to be able to accurately reproduce the wave spectral shape, except for discontinuous waves of greater than 4 m in height.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2001.10a
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• pp.310-313
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• 2001

This study was conducted to derive optimal design floods by Gumbel, GEV, GLO and GPA distributions for the annual maximum series at sixteen watersheds. Adequacy for the analysis of flood data used in This study was established by the tests of Independence, Homogeneity, detection of Outliers. Parameters were estimated by the Methods of L, L1 and L2-moments. Design floods obtained by Methods of L, L1 and L2-moments using Gringorten methods for plotting positions in GEV distribution were compared by the Relative Mean Errors and Relative Absolute Errors.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.635-639
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• 2004

댐과 같은 구조물의 설계시 큰 강우량에 내한 분포함수의 적합성을 놀일 필요가 있다. 이에 대해 Wang (1997a and b)은 큰 설계량에 내한 적합성을 놀이기 위해 LH 모멘트와 고차 PWM(higher Probability Weighted Moments)방법을 제안하였다. 따라서 본 연구에서는 우리나라의 자 지역별로 대표적인 4개 지점의 일 강우량 자료를 사용하여 제안된 고차 PWM 방법의 적용성을 살펴보았다. 그 과정으로 가장 낮은 차수인 일반적인 PWM 방법과 더 높은 차수의 PWM 방법을 이용하여, GEV(Generalized Extreme Value) 분포와 Gumbel 분포에 대한 매개변수를 추정한 후 이 추정치를 확률지에 실측치와 함께 도시하여 결과를 비교하였다. 그리고 PPCC(Probability Plot Correlation Coefficient) 적합도 검정결과를 통해 추정된 매개변수의 적합성을 확인하였다.

• Smart Structures and Systems
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• v.17 no.1
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• pp.135-147
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• 2016

This paper describes a smart structural system, which uses smart materials for real-time monitoring and active control of bolted-joints in steel structures. The goal of this research is to reduce the possibility of failure and the cost of maintenance of steel structures such as bridges, electricity pylons, steel lattice towers and so on. The concept of the smart structural system combines impedance based health monitoring techniques with a shape memory alloy (SMA) washer to restore the tension of the loosened bolt. The impedance-based structural health monitoring (SHM) techniques were used to detect loosened bolts in bolted-joints. By comparing electrical impedance signatures measured from a potentially damage structure with baseline data obtained from the pristine structure, the bolt loosening damage could be detected. An outlier analysis, using generalized extreme value (GEV) distribution, providing optimal decision boundaries, has been carried out for more systematic damage detection. Once the loosening damage was detected in the bolted joint, the external heater, which was bonded to the SMA washer, actuated the washer. Then, the heated SMA washer expanded axially and adjusted the bolt tension to restore the lost torque. Additionally, temperature variation due to the heater was compensated by applying the effective frequency shift (EFS) algorithm to improve the performance of the diagnostic results. An experimental study was conducted by integrating the piezoelectric material based structural health monitoring and the SMA-based active control function on a bolted joint, after which the performance of the smart 'self-monitoring and self-healing bolted joint system' was demonstrated.

• Journal of Korea Water Resources Association
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• v.43 no.4
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• pp.337-351
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• 2010

There is a growing dissatisfaction with use of conventional statistical methods for the prediction of extreme events. Conventional methodology for modeling extreme event consists of adopting an asymptotic model to describe stochastic variation. However asymptotically motivated models remain the centerpiece of our modeling strategy, since without such an asymptotic basis, models have no rational for extrapolation beyond the level of observed data. Also, this asymptotic models ignored or overestimate the uncertainty and finally decrease the reliability of uncertainty. Therefore this article provide the research example of the extreme rainfall event and the methodology to reduce the uncertainty. In this study, the Bayesian MCMC (Bayesian Markov Chain Monte Carlo) and the MLE (Maximum Likelihood Estimation) methods using a quadratic approximation are applied to perform the at-site rainfall frequency analysis. Especially, the GEV distribution and Gumbel distribution which frequently used distribution in the fields of rainfall frequency distribution are used and compared. Also, the results of two distribution are analyzed and compared in the aspect of uncertainty.

• Journal of Ocean Engineering and Technology
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• v.33 no.3
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• pp.222-228
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• 2019

An extreme value analysis (EVA) is essential to obtain a design value for highly nonlinear variables such as long-term environmental data for wind and waves, and slamming or sloshing impact pressures. According to the extreme value theory (EVT), the extreme value distribution is derived by multiplying the initial cumulative distribution functions for independent and identically distributed (IID) random variables. However, in the position mooring of DNVGL, the sampled global maxima of the mooring line tension are assumed to be IID stochastic variables without checking their independence. The ITTC Recommended Procedures and Guidelines for Sloshing Model Tests never deal with the independence of the sampling data. Hence, a design value estimated without the IID check would be under- or over-estimated because of considering observations far away from a Weibull or generalized Pareto distribution (GPD) as outliers. In this study, the IID sampling data are first checked in an EVA. With no IID random variables, an automatic resampling scheme is recommended using the block maxima approach for a generalized extreme value (GEV) distribution and peaks-over-threshold (POT) approach for a GPD. A partial autocorrelation function (PACF) is used to check the IID variables. In this study, only one 5 h sample of sloshing test results was used for a feasibility study of the resampling IID variables approach. Based on this study, the resampling IID variables may reduce the number of outliers, and the statistically more appropriate design value could be achieved with independent samples.

• Wind and Structures
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• v.38 no.3
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• pp.215-229
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• 2024

Guyed mast structures exhibit characteristics such as high flexibility, low mass, small damping ratio, and large aspect ratio, leading to a complex wind-induced vibration response mechanism. This study analyzed the time- and frequency-domain characteristics of the wind-induced response of a guyed mast structure using measured acceleration response data obtained from the Shenzhen Meteorological Gradient Tower (SZMGT). Firstly, 734 sets of 1-hour acceleration samples measured from 0:00 October 1, 2021, to 0:00 November 1, 2021, were selected to study the vibration shapes of the mast and the characteristics of the generalized extreme value (GEV) distribution. Secondly, six sets of typical samples with different vibration intensities were further selected to explore the Gaussian property and modal parameter characteristics of the mast. Finally, the modal parameters of the SZMGT are identified and the identification results are verified by finite element analysis. The findings revealed that the guyed mast vibration shape exhibits remarkable diversity, which increases nonlinearly along the height in most cases and reaches a maximum at the top of the tower. Moreover, the GEV distribution characteristics of the 734 sets of samples are closer to the Weibull distribution. The probability distribution of the structural wind vibration response under strong wind is in good agreement with the Gaussian distribution. The structural response of the mast under wind loading exhibits multiple modes. As the structural response escalates, the first three orders of modal energy in the tower display a gradual increase in proportion.