• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2019년도 학술발표회
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• pp.147-147
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• 2019

The Numerical Weather Prediction (NWP) models determine the future state of the weather by forcing current weather conditions into the atmospheric models. The NWP models approximate mathematically the physical dynamics by nonlinear differential equations; however these approximations include uncertainties. The errors of the NWP estimations can be related to the initial and boundary conditions and model parameterization. Development in the meteorological forecast models did not solve the issues related to the inevitable biases. In spite of the efforts to incorporate all sources of uncertainty into the forecast, and regardless of the methodologies applied to generate the forecast ensembles, they are still subject to errors and systematic biases. The statistical post-processing increases the accuracy of the forecast data by decreasing the errors. Error prediction of the NWP models which is updating the NWP model outputs or model output statistics is one of the ways to improve the model forecast. The regression methods (including linear, polynomial and scaling regression) are applied to the present study to improve the real time forecast skill. Such post-processing consists of two main steps. Firstly, regression is built between forecast and measurement, available during a certain training period, and secondly, the regression is applied to new forecasts. In this study, the WRF real-time forecast data, in comparison with the observed data, had systematic biases; the errors related to the NWP model forecasts were reflected in the underestimation of the meteorological data forecast by the WRF model. The promising results will indicate that the post-processing techniques applied in this study improved the meteorological forecast data provided by WRF model. A comparison between various bias correction methods will show the strength and weakness of the each methods.

• Smart Structures and Systems
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• 제20권4호
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• pp.427-440
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• 2017

One of the suitable structural damage detection methods using vibrational characteristics are damage-index-based methods. In this study, a damage index for identifying damages in plate structures using frequency response function (FRF) data has been provided. One of the significant challenges of identifying the damages in plate structures is high number of degrees of freedom resulting in decreased damage identifying accuracy. On the other hand, FRF data are of high volume and this dramatically decreases the computing speed and increases the memory necessary to store the data, which makes the use of this method difficult. In this study, FRF data are compressed using two-dimensional principal component analysis (2D-PCA), and then converted into damage index vectors. The damage indices, each of which represents a specific condition of intact or damaged structures are stored in a database. After computing damage index of structure with unknown damage and using algorithm of lookup tables, the structural damage including the severity and location of the damage will be identified. In this study, damage detection accuracy using the proposed damage index in square-shaped structural plates with dimensions of 3, 7 and 10 meters and with boundary conditions of four simply supported edges (4S), three clamped edges (3C), and four clamped edges (4C) under various single and multiple-element damage scenarios have been studied. Furthermore, in order to model uncertainties of measurement, insensitivity of this method to noises in the data measured by applying values of 5, 10, 15 and 20 percent of normal Gaussian noise to FRF values is discussed.

• 디지털융복합연구
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• 제11권10호
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• pp.387-394
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• 2013

데이터 마이닝은 예측이나 분석을 위해서 많은 양의 데이터에 존재하는 여러 가지의 관계를 추출하는 과정이라고 할 수 있다. 그러한 데이터에는 매우 많은 변수로 인한 차원의 증가로 인하여 계산상의 어려움이 수반되어지고 변수의 중복성과 중요도에 있어서 다양한 통계적 관계가 존재한다. 따라서 동일하거나 유사한 데이터를 같은 그룹으로 형성하는 클러스터 해석은 데이터 마이닝에서 필수적인 요소이다. 본 연구는 범주형 데이터의 분류에서 발생하는 불확실성의 처리를 위해 러프집합을 이용하여 정보 엔트로피를 이용한 새로운 척도를 정의하고 연구 대상에 대한 유사행동을 분석하는 시스템 구현에 그 의의가 있다. 데이터는 평택공업고등학교에서 채집되었고 이를 토대로 제안된 방법이 학생들의 유사행동에 대한 보다 정확한 결과를 보임을 알 수 있었다. 또한 속성의 개수가 10개 이상인 경우에 기본 방법과의 차이를 보이며 취업의사결정에서 학생들의 의식을 기존 방법보다 효과적으로 반영하였다.

• 한국신뢰성학회지:신뢰성응용연구
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• 제13권4호
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• pp.241-252
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• 2013

Because of financial and safety concerns, there are needs for more accurate prediction of bridge behavior. Underestimation of the bridge load carrying capacity can have serious economic consequences, as deficient bridges must be repaired or rehabilitated. Therefore, the knowledge of the actual bridge behavior under live load may lead to a more realistic calculation of the load carrying capacity and eventually this may allow for more bridges to remain in service with or without minor repairs. The presented research is focused on the reliability evaluation of the actual load carrying capacity of existing bridges based on the field testing. Seventeen existing bridges were tested under truck load to confirm their adequacy of reliability. The actual response of existing bridge structures under live load is measured. Reliability analysis is performed on the selected representative bridges designed in accordance with AASHTO codes for bridge component (girder). Bridges are first evaluated based on the code specified values and design resistance. However, after the field testing program, it is possible to apply the experimental results into the bridge reliability evaluation procedures. Therefore, the actual response of bridge structures, including unintentional composite action, partial fixity of supports, and contribution of nonstructural members are considered in the bridge reliability evaluation. The girder distribution factors obtained from the tests are also applied in the reliability calculation. The results indicate that the reliability indices of selected bridges can be significantly increased by reducing uncertainties without sacrificing the safety of structures, by including the result of field measurement data into calculation.

• 한국농공학회지
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• 제38권6호
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• pp.35-41
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• 1996

The theory of consolidation has been achieved remarkable development in terms of theory such as finite consolidation theory, two dimensional Rendulic consolidation theory. Though those theories are well defined, the analysis is by no means straightforward, because associated properties are very difficult to determine in the laboratory, Therefore Terzaghi's one dimensional consolidation theory and Barron's cylindrical consolidation theory are still widely used in engineering practice. The theoretical shortcomings of those consolidation theories and uncertainties of associated properties make inevitably some discrepancy between theoretical and field settlements. Field settlement measurement by settlement plate is, therefore, widely used to overcome the discrepancy. Ultimate settlement is one of the most important factor of embankment construction on soft soils. Nowadays the ultimate settlement prediction methods using field settlement data are widely accepted as a helpful tool for field settlement analysis of embankment construction on soft soils. Among the various methods of ultimate settlement prediction, hyperbolic method and Asaoka's method are most commonly used because of their simplicity and ability to give a reasonable estimate of consolidation settlement. In this paper, the reliability of hyperbolic method and Asaoka's method has been examined using analytical methods. It is shown that both hyperbolic method and Asaoka's method are significantly affected by the direction of drainage.

• Nuclear Engineering and Technology
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• 제37권1호
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• pp.79-90
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• 2005

In this paper are reviewed the current status of nuclear fuel management and reactor operational aid tools. In addition, we indicate deficiencies in current capabilities and what future research is judged warranted. For the nuclear fuel management review the focus is on light water reactors and the utilization of stochastic optimization methods applied to the lattice, fuel bundle, core loading pattern, and for BWRs the control rod pattern/core flow design decision making problems. Significant progress in addressing separately each of these design problems on a single cycle basis is noted; however, the outstanding challenge of addressing the integrated design problem over multiple cycles under conditions of uncertainty remains to be addressed. For the reactor operational aid tools review the focus is on core simulators, used to both process core instrumentation signals and as an operator aid to predict future core behaviors under various operational strategies. After briefly reviewing the current status of capabilities, a more in depth review of adaptive core simulation capabilities, where core simulator input data are adjusted within their known uncertainties to improved agreement between prediction and measurement, is presented. This is done in support of the belief that further development of adaptive core simulation capabilities is required to further significantly advance the utility of core simulators in support of reactor operational aid tools.

• Smart Structures and Systems
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• 제6권3호
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• pp.309-333
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• 2010

Civil infrastructure, in both its construction and maintenance, represents the largest societal investment in this country, outside of the health care industry. Despite being the lifeline of US commerce, civil infrastructure has scarcely benefited from the latest sensor technological advances. Our future should focus on harnessing these technologies to enhance the robustness, longevity and economic viability of this vast, societal investment, in light of inherent uncertainties and their exposure to service and even extreme loadings. One of the principal means of insuring the robustness and longevity of infrastructure is to strategically deploy smart sensors in them. Therefore, the objective is to develop novel, durable, smart sensors that are especially applicable to major infrastructure and the facilities to validate their reliability and long-term functionality. In some cases, this implies the development of new sensing elements themselves, while in other cases involves innovative packaging and use of existing sensor technologies. In either case, a parallel focus will be the integration and networking of these smart sensing elements for reliable data acquisition, transmission, and fusion, within a decision-making framework targeting efficient management and maintenance of infrastructure systems. In this paper, prudent and viable sensor and health monitoring technologies have been developed and used in several large structural systems. Discussion will also include several practical bridge health monitoring applications including their design, construction, and operation of the systems.

• Geomechanics and Engineering
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• 제36권5호
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• pp.475-487
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• 2024

Many uncertainties affect the stability assessment of rock structures. Some of these factors significantly influence technology decisions. Some of these factors belong to the geological domain, and spatial uncertainty measurements are useful for structural stability analysis. This paper presents an integrated approach to study the stability of rock structures, including spatial factors. This study models two main components: discrete structures (fault zones) and well known geotechnical parameters (rock quality indicators). The geostatistical modeling criterion are used to quantify geographic uncertainty by producing simulated maps and RQD values for multiple equally likely error regions. Slope stability theorem would be demonstrated by modeling local failure zones and RQDs. The approach proided is validated and finally, the slope stability analysis method and fuzzy Laypunov criterion are applied to mining projects with limited measurement data. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage. Simulation results of linear and nonlinear structures show that the proposed method is able to identify structural parameters and their changes due to damage and unknown excitations. Therefore, the goal is believed to achieved in the near future by the ongoing development of AI and fuzzy theory.

• International Journal of Naval Architecture and Ocean Engineering
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• 제6권4호
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• pp.1096-1110
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• 2014

Whipping/springing research started in the 50'ies. In the 60'ies inland water vessels design rules became stricter due to whipping/springing. The research during the 70-90'ies may be regarded as academic. In 2000 a large ore carrier was strengthened due to severe cracking from North Atlantic operation, and whipping/springing contributed to half of the fatigue damage. Measurement campaigns on blunt and slender vessels were initiated. A few blunt ships were designed to account for whipping/springing. Based on the measurements, the focus shifted from fatigue to extreme loading. In 2005 model tests of a 4,400 TEU container vessel included extreme whipping scenarios. In 2007 the 4400 TEU vessel MSC Napoli broke in two under similar conditions. In 2009 model tests of an 8,600 TEU container vessel container vessel included extreme whipping scenarios. In 2013 the 8,100 TEU vessel MOL COMFORT broke in two under similar conditions. Several classification societies have published voluntary guidelines, which have been used to include whipping/springing in the design of several container vessels. This paper covers results from model tests and full scale measurements used as background for the DNV Legacy guideline. Uncertainties are discussed and recommendations are given in order to obtain useful data. Whipping/springing is no longer academic.

• Structural Monitoring and Maintenance
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• 제8권4호
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• pp.379-402
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• 2021

Traditional approaches for structural health monitoring (SHM) seldom take ambient uncertainty (temperature, humidity, ambient vibration) into consideration, while their impacts on structural responses are substantial, leading to a possibility of raising false alarms. A few predictors model-based approaches deal with these uncertainties through complex numerical models running online, rendering the SHM approach to be compute-intensive, slow, and sometimes not practical. Also, with model-based approaches, the imperative need for a precise understanding of the structure often poses a problem for not so well understood complex systems. The present study employs a data-based approach coupled with Empirical mode decomposition (EMD) to correlate recorded response time histories under varying temperature conditions to corresponding damage scenarios. EMD decomposes the response signal into a finite set of intrinsic mode functions (IMFs). A two-dimensional Convolutional Neural Network (2DCNN) is further trained to associate these IMFs to the respective damage cases. The use of IMFs in place of raw signals helps to reduce the impact of sensor noise while preserving the essential spatio-temporal information less-sensitive to thermal effects and thereby stands as a better damage-sensitive feature than the raw signal itself. The proposed algorithm is numerically tested on a single span bridge under varying temperature conditions for different damage severities. The dynamic strain is recorded as the response since they are frame-invariant and cheaper to install. The proposed algorithm has been observed to be damage sensitive as well as sufficiently robust against measurement noise.