• Journal of Mechanical Science and Technology
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• v.20 no.10
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• pp.1662-1669
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• 2006

Since randomness and uncertainties of design parameters are inherent, the robust design has gained an ever increasing importance in mechanical engineering. The robustness is assessed by the measure of performance variability around mean value, which is called as standard deviation. Hence, constraints in robust optimization problem can be approached as probability constraints in reliability based optimization. Then, the FOSM (first order second moment) method or the AFOSM (advanced first order second moment) method can be used to calculate the mean values and the standard deviations of functions describing constraints and object. Among two methods, AFOSM method has some advantage over FOSM method in evaluation of probability. Nevertheless, it is difficult to obtain the mean value and the standard deviation of objective function using AFOSM method, because it requires that the mean value of function is always positive. This paper presented a special technique to overcome this weakness of AFOSM method. The mean value and the standard deviation of objective function by the proposed method are reliable as shown in examples compared with results by FOSM method.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.10a
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• pp.427-435
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• 2008

A reliability based slope stability assessment method is proposed and examined considering the variation of matric suction which is measured by a real time slope monitoring system. Mean value first order reliability method and advanced first order reliability method are used to calculate reliability indices of a slope. The applicability of methods is compared by applying them to the range of matric suctions measured by the real-time monitoring system. Sensitivity analysis is also performed to examine the contribution of random variables to the reliability index of slope. Finally, the proposed method is applied to a model slope. The results show that the reliability index of slope can be used for efficient slope management by quantifying the risk of slope in real time.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.5
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• pp.343-350
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• 2016

Remaining lifetime prediction of the underground gas pipeline plays a key role in maintenance planning and public safety. One of main causes in the pipeline failure is metal corrosion. This paper deals with estimating the pipeline reliability in the presence of corrosion defects. Because a pipeline has uncertainty and variability in its operation, probabilistic approximation approaches such as first order second moment (FOSM), first order reliability method (FORM), second order reliability method (SORM), and Monte Carlo simulation (MCS) are widely employed for pipeline reliability predictions. This paper presents a fuzzy inference based reliability method (FIRM). Compared with existing methods, a distinction of our method is to incorporate a fuzzy inference into quantifying degrees of variability in corrosion defects. As metal corrosion depends on the service environment, this feature makes it easier to obtain practical predictions. Numerical experiments are conducted by using a field dataset. The result indicates that the proposed method works well and, in particular, it provides more advisory estimations of the remaining lifetime of the gas pipeline.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.8 s.251
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• pp.941-948
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• 2006

In this study, the new method for reliability estimation is proposed using kriging metamodel. Kriging metamodel can be determined by appropriate sampling range and sampling numbers because there are no random errors in the Design and Analysis of Computer Experiments(DACE) model. The first kriging metamodel is made based on widely ranged sampling points. The Advanced First Order Reliability Method(AFORM) is applied to the first kriging metamodel to estimate the reliability approximately. Then, the second kriging metamodel is constructed using additional sampling points with updated sampling range. The Monte-Carlo Simulation(MCS) is applied to the second kriging metamodel to evaluate the reliability. The proposed method is applied to numerical examples and the results are almost equal to the reference reliability.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.3
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• pp.18-25
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• 2005

Existing methods have performed the reliability analysis using nonlinear optimization techniques. This is mainly due to the fact that they directly apply Multidisciplinary Design Optimization(MDO) frameworks to the reliability analysis formulation. Accordingly, the reliability analysis and the Multidisciplinary Analysis(MDA) are tightly coupled in a single optimizer, which hampers utilizing the recursive and function-approximation based reliability analysis methods such as the Advanced First Order Reliability Method(AFORM). In order to utilize the efficient reliability analysis method under multidisciplinary analysis systems, we propose a new strategy named Sequential Approach on Reliability Analysis under Multidisciplinary analysis systems(SARAM). In this approach, the reliability analysis and the MDA are decomposed and arranged in a sequential manner, making a recursive loop. The efficiency of the SARAM method was verified using three illustrative examples taken from the literatures. Compared with existing methods, it showed the least number of subsystem analyses over other methods while maintaining accuracy.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1991.04a
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• pp.34-42
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• 1991

This study is directed for the development of an efficient system-level Importance Sampling Technique for system reliability analysis of bridge structures Many methods have been proposed for structural reliability assessment purposes, such as the First-order Second-Moment Method, the Advanced Second-Moment Method, Computer Simulation, etc. The Importance Sampling Technique can be employed to obtain accurate estimates of the required probability with reasonable computation effort. Based on the observation and the results of application, it nay be concluded that Importance Sampling Method is a very effective tool for the system reliability analysis.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.1016-1021
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• 2008

In this study, effective methods for reliability estimation and reliability-based design optimization(RBDO) are proposed using kriging metamodel and genetic algorithm. In our previous study, we proposed the accurate method for reliability estimation using two-staged kriging metamodel and genetic algorithm. In this study, the possibility of applying the previously proposed method to RBDO is examined. The accuracy of that method is much improved than the first order reliability method with similar efficiency. Finally, the effective method for RBDO is proposed and applied to numerical examples. The results are compared to the existing RBDO methods and shown to be very effective and accurate.

• Proceedings of the Korea Concrete Institute Conference
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• 2004.11a
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• pp.433-436
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• 2004

The safety of buildings is generally estimated by analyzing a plane frame ignoring a minor bending moment. In this paper, uncertainties of reinforced concrete shear wall subjected to a biaxial bending are considered. First, major parameters are selected from all parameters of general shear wall design to perform a reliability analysis in their practical ranges, means and standard derivations of selected design parameters for the reliability analysis are calculated by a data mining as a simulation method. The bi-section method is used to find inclined neutral axis and its limit state using MATLAB subjected to the concept on strength design method. The reliability index $\beta$ as a safety index is calculated based on AFOSM(Advanced First-Order Second Moment) method. Also, if target reliability index $\beta_T$ is decided by an engineer an amount of reinforcement can be calculated by subtracting the reliability index $\beta$ from the target reliability index $\beta_T$.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.9 s.252
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• pp.1116-1123
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• 2006

In this study, the effective method for reliability estimation is proposed using tow-staged kriging metamodel and genetic algorithm. Kriging metamodel can be determined by appropriate sampling range and the number of sampling points. The first kriging metamodel is made based on the proposed sampling points. The advanced f'=rst order reliability method is applied to the first kriging metamodel to determine the reliability and most probable failure point(MPFP) approximately. Then, the second kriging metamodel is constructed using additional sampling points near the MPFP. These points are selected using genetic algorithm that have the maximum mean squared error. The Monte-Carlo simulation is applied to the second kriging metamodel to estimate the reliability. The proposed method is applied to numerical examples and the results are almost equal to the reference reliability.

• Smart Structures and Systems
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• v.7 no.4
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• pp.263-274
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• 2011

A reliability-based slope stability assessment method considering fluctuations in the monitored matric suction was proposed for real-time identification of slope risk. The assessment model was based on the limit equilibrium model for infinite slope failure. The first-order reliability method (FORM) was adopted to calculate the probability of slope failure, and results of the model were compared with Monte-Carlo Simulation (MCS) results to validate the accuracy and efficiency of the model. The analysis shows that a model based on Advanced First-Order Reliability Method (AFORM) generates results that are in relatively good agreement with those of the MCS, using a relatively small number of function calls. The contribution of random variables to the slope reliability index was also examined using sensitivity analysis. The results of sensitivity analysis indicate that the effective cohesion c' is a significant variable at low values of mean matric suction, whereas matric suction ($u_a-u_w$) is the most influential factor at high mean suction values. Finally, the reliability indices of an unsaturated model soil slope, which was monitored by a wireless matric suction measurement system, were illustrated as 2D images using the suggested probabilistic model.