• International Journal of Naval Architecture and Ocean Engineering
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• 제12권1호
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• pp.755-767
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• 2020

A significant development has been made on a new fatigue damage model applicable to Gaussian wide band stress response spectra using numerical approximation methods such as data processing, time simulation, and regression analysis. So far, most of the alternative approximate models provide slightly underestimated or overestimated damage results compared with the rain-flow counting distribution. A more reliable approximate model that can minimize the damage differences between exact and approximate solutions is required for the practical design of ships and offshore structures. The present paper provides a detailed description of the development process of a new fatigue damage model. Based on the principle of the Gaussian wide band model, this study aims to develop the best approximate fatigue damage model. To obtain highly accurate damage distributions, this study deals with some prominent research findings, i.e., the moment of rain-flow range distribution MRR(n), the special bandwidth parameter μk, the empirical closed form model consisting of four probability density functions, and the correction factor QC. Sequential prerequisite data processes, such as creation of various stress spectra, extraction of stress time history, and the rain-flow counting stress process, are conducted so that these research findings provide much better results. Through comparison studies, the proposed model shows more reliable and accurate damage distributions, very close to those of the rain-flow counting solution. Several significant achievements and findings obtained from this study are suggested. Further work is needed to apply the new developed model to crack growth prediction under a random stress process in view of the engineering critical assessment of offshore structures. The present developed formulation and procedure also need to be extended to non-Gaussian wide band processes.

• Structural Engineering and Mechanics
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• 제81권3호
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• pp.293-303
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• 2022

As infrastructure ages and traffic load increases, serious public concerns have arisen for the well-being of bridges. The current health monitoring practice focuses on large-scale bridges rather than short span bridges. However, it is critical that more attention should be given to these behind-the-scene bridges. The relevant information about the construction methods and as-built properties are most likely missing. Additionally, since the condition of a bridge has unavoidably changed during service, due to weathering and deterioration, the material properties and boundary conditions would also have changed since its construction. Therefore, it is not appropriate to continue using the design values of the bridge parameters when undertaking any analysis to evaluate bridge performance. It is imperative to update the model, using finite element (FE) analysis to reflect the current structural condition. In this study, a FE model is established to simulate a concrete culvert bridge in New South Wales, Australia. That model, however, contains a number of parameter uncertainties that would compromise the accuracy of analytical results. The model is therefore updated with a neural network (NN) optimisation algorithm incorporating Monte Carlo (MC) simulation to minimise the uncertainties in parameters. The modal frequency and strain responses produced by the updated FE model are compared with the frequency and strain values on-site measured by sensors. The outcome indicates that the NN model updating incorporating MC simulation is a feasible and robust optimisation method for updating numerical models so as to minimise the difference between numerical models and their real-world counterparts.

• Geomechanics and Engineering
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• 제33권1호
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• pp.1-9
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• 2023

The unconfined compression strength (UCS) of soils is commonly used either before or during the construction of geo-structures. In the pre-design stage, UCS as a mechanical property is obtained through a laboratory test that requires cumbersome procedures and high costs from in-situ sampling and sample preparation. As an alternative way, the empirical model established from limited testing cases is used to economically estimate the UCS. However, many parameters affecting the 1D soil compression response hinder employing the traditional statistical analysis. In this study, gene expression programming (GEP) is adopted to develop a prediction model of UCS with common affecting soil properties. A total of 79 undisturbed soil samples are collected, of which 54 samples are utilized for the generation of a predictive model and 25 samples are used to validate the proposed model. Experimental studies are conducted to measure the unconfined compression strength and basic soil index properties. A performance assessment of the prediction model is carried out using statistical checks including the correlation coefficient (R), the root mean square error (RMSE), the mean absolute error (MAE), the relatively squared error (RSE), and external criteria checks. The prediction model has achieved excellent accuracy with values of R, RMSE, MAE, and RSE of 0.98, 10.01, 7.94, and 0.03, respectively for the training data and 0.92, 19.82, 14.56, and 0.15, respectively for the testing data. From the sensitivity analysis and parametric study, the liquid limit and fine content are found to be the most sensitive parameters whereas the sand content is the least critical parameter.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.130-130
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• 2021

The increasing effect of urbanization has been more apparent through flooding and downstream water quality especially from heavy rainfalls. In response, stormwater runoff management solutions have focused on runoff volume reduction and treatment through infiltration. However, there are areas with low infiltration soils or are experiencing more dry days and even drought. In this study, a lab-scale infiltration system was used to compare the applicability of two types of soil as base layer in gravel-filled infiltration systems with emphasis on runoff capture and suspended solids removal. The two types of soils used were sandy soil representing a high infiltration system and clayey soil representing a low infiltration system. Findings showed that infiltration rates increased with the water depth above the gravel-soil interface indicating that the available depth for water storage affects this parameter. Runoff capture in the high infiltration system is more affected by rainfall depth and inflow rates as compared to that in the low infiltration system. Based on runoff capture and pollutant removal analysis, a media depth of at least 0.4 m for high infiltration systems and 1 m for low infiltration systems is required to capture and treat a 10-mm rainfall in Korea. A maximum infiltration rate of 200 mm/h was also found to be ideal to provide enough retention time for pollutant removal. Moreover, it was revealed that low infiltration systems are more susceptible to horizontal flows and that the length of the structure may be more critical that the depth in this condition.

• 한국컴퓨터정보학회논문지
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• 제28권12호
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• pp.221-229
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• 2023

최근 전력 계통에 인공 관성, 감쇠, 블랙스타트 기능, 독립 운전 기능 등을 제공할 수 있어 많은 주목을 받고 있는 Grid-forming(GFM) 컨버터는 저전압 Microgrids(MG)에서 낮은 라인 임피던스의 X/R 비율과 작지 않은 전력각으로 인한 유효전력과 무효전력 간의 커플링 현상이 발생한다. 이러한 전력 커플링 현상은 GFM 컨버터의 안정성 및 성능 저하 문제, 부정확한 전력 공유 문제, 제어 파라미터 설계 문제를 유발하고 있다. 따라서 본 논문은 GFM 컨버터와 관련된 제어 방법뿐만 아니라 전력 디커플링 방법에 대한 검토 연구로서, 유망 제어 방법을 소개하고 전력 디커플링 방법에 대한 비판적 검토를 통하여 향후 연구 활동의 접근성을 높이고자 하였다. 이에 따라 전력디커플링 방법 연구를 위해 향후 연구자들이 쉽게 접근할 수 있어 분산 발전원 확대에 기여할 수 있을 것이다.

• 한국데이타베이스학회:학술대회논문집
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• 한국데이타베이스학회 1999년도 춘계공동학술대회: 지식경영과 지식공학
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 1999년도 춘계공동학술대회-지식경영과 지식공학
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• 콘크리트학회논문집
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• 제22권1호
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• pp.41-50
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• 2010

고층건물에 플랫 플레이트의 사용이 증가하면서, 과도한 시공 하중의 작용과 그에 따른 슬래브의 장기 처짐은 콘크리트 슬래브 설계에 큰 영향을 미칠 수 있다. 콘크리트의 균열과 조기재령 슬래브의 처짐을 예측하기 위해서는 시공하중을 정확히 산출할 필요가 있다. 이러한 플랫 플레이트의 시공하중은 다양한 설계 요소에 영향을 받음에도 불구하고, 대부분의 기존 시공하중 산정법의 영향 요소는 시공주기와 콘크리트의 재료적 성질, 동바리 지지층수로 국한되어왔다. 이 연구에서는 이러한 영향을 포함하여, 동바리 강성과 콘크리트 균열의 영향을 이론적으로 연구하였다. 연구결과를 바탕으로 시공하중 산정을 위한 간단한 방법을 개발하였다. 제안법에서 시공하중 산정은 최상층 슬래브 타설과하부 동바리 제거 두 단계로 나누어진다. 각 단계에서 시공하중 증가분만큼 동바리와 슬래브의 강성비에 따라 하부 슬래브로 하중이 전달된다. 제안방법은 기존 시공하중 산정법과 비교되었다. 실제 시공하중 계측결과와 제안법의 비교는 연계된 논문에서 기술된다.

• 한국강구조학회 논문집
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• 제20권3호
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• pp.399-407
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• 2008

건축공사 표준시방서에서 규정하는 TS고력볼트는 KS B 2819에 따라 토크관리법에 의한 체결한다. TS고력볼트는 핀 테일이 파단되면 적정축력이 도입되는 것으로 알려져 있지만, 실제로는 온도조건에 따라 토크계수가 변하고 도입축력도 설계기준에 미치지 못하는 경우가 발생한다. 본 논문에서는 현장 온도조건이 볼트의 토크계수 및 도입축력의 영향을 평가하기 위해 ${-10^{\circ}C{\sim}50^{\circ}C}$ 범위에서 3 종류의 고력 볼트에 대해 도입축력, 토크계수, 너트회전각을 실험적으로 비교 분석하였다. 실험결과 TS고력볼트의 경우 핀 테일이 파단될 때 모든 온도조건에서 설계볼트장력과 표준볼트장력을 상회하는 도입축력을 나타냈으며 ${-10^{\circ}C}$에서 ${50^{\circ}C}$까지 온도 상승함에 따라 핀 테일 파단시점의 볼트에 도입되는 평균축력은 20kN 증가되었다. 아연피막 처리한 일반육각형 고력볼트의 경우, $0^{\circ}C$, $20^{\circ}C$, $50^{\circ}C$조건, 토크계수 0.13과 토크 ${462N{\cdot}m} $에서 표준볼트축력을 상회하였지만, 도입축력의 일정한 경향을 찾을 수 없었으며 온도변수 별 평균 도입축력 차는 최대 50kN이었다. 일반 육각고력볼트의 경우, 온도상승에 따라 평균 도입축력도 상승되는 추세였으며 토크 ${462N{\cdot}m} $ 일때 온도변수별 볼트의 도입축력은 최대 33kN 차이를 보였다. 또한, TS볼트를 제외한 육각볼트 종류 군에 대해서는 너트회전각 ${90^{\circ}}$ 경우의 도입축력은 설계볼트장력에 미치지 못했다. 따라서, 기존 너트회전각 ${120^{\circ}{\pm}30^{\circ}}$의 하한치 ${-30^{\circ}}$에 대한 재평가를 고려해 볼 필요가 있다.

• Smart Structures and Systems
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• 제20권5호
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• pp.549-562
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• 2017

The US railroad network carries 40% of the nation's total freight. Railroad bridges are the most critical part of the network infrastructure and, therefore, must be properly maintained for the operational safety. Railroad managers inspect bridges by measuring displacements under train crossing events to assess their structural condition and prioritize bridge management and safety decisions accordingly. The displacement of a railroad bridge under train crossings is one parameter of interest to railroad bridge owners, as it quantifies a bridge's ability to perform safely and addresses its serviceability. Railroad bridges with poor track conditions will have amplified displacements under heavy loads due to impacts between the wheels and rail joints. Under these circumstances, vehicle-track-bridge interactions could cause excessive bridge displacements, and hence, unsafe train crossings. If displacements during train crossings could be measured objectively, owners could repair or replace less safe bridges first. However, data on bridge displacements is difficult to collect in the field as a fixed point of reference is required for measurement. Accelerations can be used to estimate dynamic displacements, but to date, the pseudo-static displacements cannot be measured using reference-free sensors. This study proposes a method to estimate total transverse displacements of a railroad bridge under live train loads using acceleration and tilt data at the top of the exterior pile bent of a standard timber trestle, where train derailment due to excessive lateral movement is the main concern. Researchers used real bridge transverse displacement data under train traffic from varying bridge serviceability levels. This study explores the design of a new bridge deck-pier experimental model that simulates the vibrations of railroad bridges under traffic using a shake table for the input of train crossing data collected from the field into a laboratory model of a standard timber railroad pile bent. Reference-free sensors measured both the inclination angle and accelerations of the pile cap. Various readings are used to estimate the total displacements of the bridge using data filtering. The estimated displacements are then compared to the true responses of the model measured with displacement sensors. An average peak error of 10% and a root mean square error average of 5% resulted, concluding that this method can cost-effectively measure the total displacement of railroad bridges without a fixed reference.