• Proceedings of the Computational Structural Engineering Institute Conference
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• 1997.10a
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• pp.145-154
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• 1997

The results of optimum design by the deterministic approach adopted in the current design codes depend upon the safety levels of the applied code. But, it is now generally recognized that structural problems are nondeterministic and, consequently, that engineering optimum design must cope with uncertainties. Therefore, it is not an overstatement to affirm that the combination of reliability-based design procedures and optimization techniques is the only means of providing a powerful tool to obtain a practical optimum design solution. In the paper, reliability based optimum design procedure as a rational approach to optimum structural design is presented. The design constraints are formulated based on the ASD, LRFD and reliability theories. The reliability analysis is based on an advanced first-order second moment approach. Uncertainties in the structural strength and loading due to inherent variability as well as modeling and prediction errors are included in failure due to combined bending and shear. For the realistic reliability-based optimization of continuous steel box girder bridges, interactive non-linear limit state model is formulated based on the von Mises's combined stress yield criterion. Comparative results are presented when the ASD criteria are used for the optimum design of a structure under reliability constraints. In addition, this study comparatively shows the results of the optimum design for various criteria of design codes.

• Structural Engineering and Mechanics
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• v.83 no.2
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• pp.167-178
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• 2022

Surrogate models aim to approximate the performance function with an active-learning design of experiments (DoE) to obtain a sufficiently accurate prediction of the performance function's sign for an inexpensive computational demand in reliability analysis. Nevertheless, many existing active-learning methods are limited to the Kriging model, while the uncertainties of the Kriging itself affect the reliability analysis results. Moreover, the existing general active-learning methods may not achieve a fully satisfactory balance between accuracy and efficiency. Therefore, a novel active-learning method GLM-CM is constructed to yield the issues, which conciliates several merits of existing methods. To demonstrate the performance of the proposed method, four examples, concerning both mathematical and engineering problems, were selected. By benchmarking obtained results with literature findings, various surrogate models combined with the proposed method not only provide an accurate reliability evaluation while highly alleviating the computational burden, but also provides a satisfactory balance between accuracy and efficiency compared to the other reliability methods.

• Advances in aircraft and spacecraft science
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• v.9 no.3
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• pp.263-278
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• 2022

The time-varying structural reliability of an aeroelastic launch vehicle subjected to stochastic parameters is investigated. The launch vehicle structure is under the combined action of several stochastic loads that include aerodynamics, thrust as well as internal combustion pressure. The launch vehicle's main body structural flexibility is modeled via the normal mode shapes of a free-free Euler beam, where the aerodynamic loadings on the vehicle are due to force on each incremental section of the vehicle. The rigid and elastic coupled nonlinear equations of motion are derived following the Lagrangian approach that results in a complete aeroelastic simulation for the prediction of the instantaneous launch vehicle rigid-body motion as well as the body elastic deformations. Reliability analysis has been performed based on two distinct limit state functions, defined as the maximum launch vehicle tip elastic deformation and also the maximum allowable stress occurring along the launch vehicle total length. In this fashion, the time-dependent reliability problem can be converted into an equivalent time-invariant reliability problem. Subsequently, the first-order reliability method, as well as the Monte Carlo simulation schemes, are employed to determine and verify the aeroelastic launch vehicle dynamic failure probability for a given flight time.

• Journal of the Korean Society of Safety
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• v.14 no.2
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• pp.11-18
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• 1999

Proper fatigue life prediction procedures are needed for mechanical structures which requires high durability and reliability. In this paper, a fatigue life prediction procedure has been developed for predicting fatigue life of moving structure under variable loadings. The developed procedure was efficiently applied for a fatigue life calculation of a container crane. Especially, the procedure is useful for safety assessment by computer simulation. A computer program was developed for fatigue life assessment by adopting the forementioned procedure.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.715-716
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• 2008

A dead zone prediction system for vehicles are implemented in this paper. To improve performance reliability and stability, we import two method to get a information between car and car, and car and road. One is traffic lane detection method, another is vecle recognition. In this paper, we explain the methods and whole structure about this system except for details.

• Proceedings of the Korean Reliability Society Conference
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• 2002.06a
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• pp.403-410
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• 2002

This paper presents the effect of internal corrosion, external corrosion, material properties, operation condition, earthquake, traffic load and design thickness in pipeline on the failure prediction using a failure probability model. A nonlinear corrosion is used to represent the loss of pipe wall thickness with time. The effects of environmental, operational, and design random variables such as a pipe diameter, earthquake, fluid pressure, a corrosion rate, a material yield stress and a pipe thickness on the failure probability are systematically investigated using a failure probability model for the corrosion pipeline.

• Journal of the Korean Society for Railway
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• v.8 no.5
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• pp.439-443
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• 2005

The biggest challenge bridge agencies face is the maintenance of bridges, keeping them safe and serviceable, with limited funds. To maintain the bridges effectively, there is and urgent need to predict their remaining life from a system reliability viewpoint. In this paper, a model using lifetime functions to evaluate the overall system probability of survival of a rail road bridge is proposed. In this model, the rail load bridge is modeled as a system. Using the model, the lifetime of the rail road bridge is predicted.

• Proceedings of the Korean Reliability Society Conference
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• 2004.07a
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• pp.161-166
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• 2004

A method of estimating B$_{\alpha}$ life of crack growth is proposed based on the linear elastic fracture mechanic model. It is assumed that the coefficients in the Paris-Erdogan equation are random variables and their distributions are estimated by the method of 2-stage estimation from the fatigue crack growth data. A case study is also given. is also given.

• Information and Communications Magazine
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• v.10 no.2
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• pp.29-35
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• 1993

To perform reliability prediction during the development phase of a particular telecommunications equipment or system, it is very important to correctively comprehend the quality characteristics of the applied parts.

• Journal of Mechanical Science and Technology
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• v.17 no.4
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• pp.469-476
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• 2003

Despite of considerable research results or uniaxial tension creep available for superalloys, few studies have been made on high temperature creep using the Initial Stram Method (ISM) In this paper, the real-time prediction of high temperature creep strength and creep lift for the nickel-based superalloy Udimet 720 (high-temperature and high-pressure gas turbine engine materials) was performed on round-bar type specimens under pure static load at the temperatures of 538$^{\circ}C$. 649$^{\circ}C$, and 704$^{\circ}C$. The predictive equation derived from the ISM in creep tests showed better reliability than those from LMP (Larson-Miller Parameter) and LMP-lSM (Larson Miller Parameter-Initial Strain Method) specially for long time creep prediction (10$^3$∼10$\^$5/h).