• 제목/요약/키워드: Collapse prediction

검색결과 140건 처리시간 0.022초

종속고장을 고려한 전력시스템의 신뢰도 평가 (Reliability Analysis of Power System with Dependent Failure)

  • 손현일;권기량;김진오
    • 조명전기설비학회논문지
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    • 제25권9호
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    • pp.62-68
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    • 2011
  • Power system needs to sustain high reliability due to its complexity and security. The reliability prediction method is usually based on independent failure. However, in practice, the Common Cause Failures(CCF) and Cascading failure occur to the facilities in power system as well as independent failures in many cases. The CCF and Cascading failure turn out the system collapse seriously in a wide range. Therefore to improve the reliability of the power system practically, it is required that the analysis is conducted by using the CCF and Cascading failure. This paper describes the CCF and Cascading failure modeling combined with independent failure. The incorporated model of independent failure, CCF and cascading failure is proposed and analyzed, and it is applied to the distribution power system in order to examine this method.

A PARAMETRIC SENSITIVITY STUDY OF GDI SPRAY CHARACTERISTICS USING A 3-D TRANSIENT MODEL

  • Comer, M.A.;Bowen, P.J.;Sapsford, S.M.;Kwon, S.I.
    • International Journal of Automotive Technology
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    • 제5권3호
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    • pp.145-153
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    • 2004
  • Potential fuel economy improvements and environmental legislation have renewed interest in Gasoline Direct Injection (GDI) engines. Computational models of fuel injection and mixing processes pre-ignition are being developed for engine optimisation. These highly transient thermofluid models require verification against temporally and spatially resolved data-sets. The authors have previously established the capability of PDA to provide suitable temporally and spatially resolved spray characteristics such as mean droplet size, velocity components and qualitative mass distribution. This paper utilises this data-set to assess the predictive capability of a numerical model for GDI spray prediction. After a brief description of the two-phase model and discretisation sensitivity, the influence of initial spray conditions is discussed. A minimum of 5 initial global spray characteristics are required to model the downstream spray characteristics adequately under isothermal, atmospheric conditions. Verification of predicted transient spray characteristics such as the hollow-cone, cone collapse, head vortex, stratification and penetration are discussed, and further improvements to modelling GDI sprays proposed.

최신 설계규정에 의한 심해 해저관로 두께의 기계적 설계 (Mechanical Design of Deepwater Pipeline Wall Thickness Using the Recent Rules)

  • Han-Suk Choi
    • 한국해양공학회지
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    • 제16권6호
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    • pp.65-70
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    • 2002
  • This paper presents a mechanical design of the deepwater pipeline wall thickness using the recent design rules. Characteristics and limitations of the new codes were identified through a case study design in the Gulf of Mexico. In addition to the ASME, API, and DVD codes, the code of federal regulations (CFR) was also utilized in the design. It was found that conservatism still exists within the collapse prediction for water depth greater than 1500m. Comparision of the results from DNV and API codes were presented.

변형연화모델을 이용한 미고결 지반의 터널변형 (Tunnel Deformation in Shallow Unconsolidated Ground by Using Strain-Softening Model)

  • 서인식;김병탁
    • 한국산업융합학회 논문집
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    • 제10권2호
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    • pp.81-88
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    • 2007
  • In case of an urban tunnel, the displacement of ground base controls the tunnel design because it is built on shallow and unconsolidated ground many times. There are more insufficiency to describe the ground movement which coincides in the measured result of the situ because the design of an urban tunnel is dependent on the method of numerical analysis used to the existing elastic and elasto-plastic models. We studied about the prediction for the ground movement of a shallow tunnel in unconsolidated ground, mechanism of collapse, and settlement. Also this paper shows comparison with the existing elastic and elasto-plastic model using the unlinear analysis of the strain-softening model. We can model the real ground movement as the increasement of ground surface inclination or occurrence of shear band by using strain-softening model for the result of ground movement of an urban NATM tunnel.

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Accelerated Creep Testing of Geogrids for Slopes and Embankments: Statistical Models and Data Analysis

  • Koo, Hyun-Jin;Kim, You-Kyum;Kim, Dong-Whan
    • 한국신뢰성학회:학술대회논문집
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    • 한국신뢰성학회 2004년도 정기학술대회
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    • pp.227-232
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    • 2004
  • The failure of geogrids can be defined as an excessive creep strain which causes the collapse of slopes and embankments. In this study, the accelerated creep tests were applied to two different types of polyester geogrids, at 75, 80, 85$^{\circ}C$ by applying 50% load of ultimate tensile strengths using a newly designed test equipment which is allowed the creep testing at higher temperatures. And then the creep curves were shifted and superposed in the time axis by applying time-temperature supposition principles. In predicting the lifetimes of geogrids, the underlying distribution for failure times were determined based on identification of the failure mechanism. The results indicate that the conventional procedures with the newly designed test equipment are shown to be effective in prediction of the lifetimes of geogrids with shorter test times. In addition, the predicted lifetimes of geogrids having different structures at various creep strains give guidelines for users to select the proper geogrids in the fields.

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교량시설물 안전관리 네트워크 구축을 위한 기존 시스템 연계방안 연구 (Connection method on pre-installed bridge monitoring system for bridge structure safety network)

  • 박기태;이우상;주봉철;황윤국
    • 한국방재학회:학술대회논문집
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    • 한국방재학회 2008년도 정기총회 및 학술발표대회
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    • pp.469-472
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    • 2008
  • In general, structures in service gradually lose original performance according to time due to initial defects in design and construction, or exposure to unfavorable external conditions such as repeated loading or deteriorating environment, and in extreme cases, may collapse in large disaster. Therefore, in order to maintain the serviceability of structures at optimal level, advanced structure measuring system which can inform optimal time point and method of maintenance is required in addition to accurate prediction of residual life the structure by periodic inspection. To guarantee the safety level of bridge structure and to prevent from disaster, the integration of safety network for bridge structures are needed. Therefore in this study, to enhance the effectiveness of safety network for bridge, the connection methodologies between safety network and pre-installed bridge monitoring system are investigated.

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Advanced in Algorithms, Security, and Systems for ICT Convergence

  • Park, Ji Su;Park, Jong Hyuk
    • Journal of Information Processing Systems
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    • 제16권3호
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    • pp.523-529
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    • 2020
  • Future information and communication technology (ICT) is constantly evolving and converging in diverse fields depending on the wireless environment, and the trend is being further developed to increase the speed of wireless networks. Future ICT is needed in many areas such as active senior & solo-economy, hyper-connected society, intelligent machine, industrial boundary collapse, secured self, and the sharing economy. However, a lot of research is needed to solve problems such as machine learning, security, prediction, unmanned technology, etc. Therefore, this paper describes some technologies developed in the areas of blockchain, fault diagnosis, security, agricultural ICT, cloud, life safety and care, and climate monitoring in order to provide insights into the future paradigm.

Effect of tension stiffening on the behaviour of square RC column under torsion

  • Mondal, T. Ghosh;Prakash, S. Suriya
    • Structural Engineering and Mechanics
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    • 제54권3호
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    • pp.501-520
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    • 2015
  • Presence of torsional loadings can significantly affect the flow of internal forces and deformation capacity of reinforced concrete (RC) columns. It increases the possibility of brittle shear failure leading to catastrophic collapse of structural members. This necessitates accurate prediction of the torsional behaviour of RC members for their safe design. However, a review of previously published studies indicates that the torsional behaviour of RC members has not been studied in as much depth as the behaviour under flexure and shear in spite of its frequent occurrence in bridge columns. Very few analytical models are available to predict the response of RC members under torsional loads. Softened truss model (STM) developed in the University of Houston is one of them, which is widely used for this purpose. The present study shows that STM prediction is not sufficiently accurate particularly in the post cracking region when compared to test results. An improved analytical model for RC square columns subjected to torsion with and without axial compression is developed. Since concrete is weak in tension, its contribution to torsional capacity of RC members was neglected in the original STM. The present investigation revealed that, disregard to tensile strength of concrete is the main reason behind the discrepancies in the STM predictions. The existing STM is extended in this paper to include the effect of tension stiffening for better prediction of behaviour of square RC columns under torsion. Three different tension stiffening models comprising a linear, a quadratic and an exponential relationship have been considered in this study. The predictions of these models are validated through comparison with test data on local and global behaviour. It was observed that tension stiffening has significant influence on torsional behaviour of square RC members. The exponential and parabolic tension stiffening models were found to yield the most accurate predictions.

CNN 기법을 활용한 터널 암판정 예측기술 개발 (Rock Classification Prediction in Tunnel Excavation Using CNN)

  • 김하영;조래훈;김규선
    • 한국지반공학회논문집
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    • 제35권9호
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    • pp.37-45
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    • 2019
  • 터널 굴착 시 신속한 막장면 상태 파악 및 적절한 지보패턴 결정은 터널 붕락사고의 예방 및 안정적인 굴진에 매우 중요하다. 본 연구에서는 딥러닝 기법을 활용하여 막장면 상태에 따른 암반상태 분류를 신속하게 결정할 수 있는 기술을 개발하였으며, CNN 기법을 이용한 암반상태 분류방법 및 예측 정확도 개선 방법 등을 제시하고 있다. 수 만개의 이미지가 사전 학습된 VGG16 모델을 알고리즘으로 적용하였고, 1,469개의 터널 막장면 이미지에 대한 학습을 통하여 5개 등급으로 암반상태를 분류하였다. 본 연구에서의 예측 정확도는 최대 83.9% 수준을 나타내었으며, 향후 추가적인 이미지 축적을 통해 암반상태 평가자에 따른 편차를 줄인 객관적이고 정량적 암반상태 분류방법으로 활용 가능할 것으로 판단된다.

TSP탐사를 이용한 터널 굴착면 전방 지질상태 및 함수대 분석 (Analysis of geological conditions and water bearing zones in front of tunnel face using TSP)

  • 임경학;박연준
    • 한국터널지하공간학회 논문집
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    • 제25권5호
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    • pp.373-386
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    • 2023
  • 터널 굴착면 전방의 지질상태 및 함수대 예측을 분석하기 위하여 단층파쇄대 붕락구간에서 TSP탐사를 수행하였다. TSP탐사 결과는 예측구간의 막장면 관찰 결과와 비교하여 검증하였다. TSP탐사의 암질 예측 결과는 막장면 관찰 결과와 비교하여 약 3~10 m의 오차가 발생하였으나 전반적인 암질 변화 및 지반상태는 비교적 유사한 것으로 분석되었다. 막장면 관찰의 함수대에서 탄성파 속도비는 1.79~2.37, 포아송비는 0.27~0.39의 범위를 보인다. 함수대 이외 구간(젖은상태(wet))의 탄성파 속도비는 1.61~1.89, 포아송비는 0.19~0.3의 값을 보인다. 탄성파 속도비와 포아송비 분포를 분석하면 탄성파 속도비는 2.0 이상, 포아송비는 0.3 이상에서 함수대 가능성이 높은 구간으로 분석된다.