• Proceedings of the KIEE Conference
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• 2004.11b
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• pp.69-71
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• 2004

This paper presents a method to predict failure probability related to aging. To calculate failure probability, the Weibull distribution is used due to age-related reliability. The Weibull distribution has shape and scale parameters. Each estimated parameter is obtained from Data Analytic Method (Type II Censoring) which is relatively simpler and faster than the traditional calculation ways for estimating parameters. Also, this paper shows the calculation procedures of a probabilistic failure prediction through a stochastic data analysis. Consequently, the proposed methods would be likely to permit that the new deregulated environment forces utilities to reduce overall costs while maintaining an age-related reliability index.

• Journal of the Korean Solar Energy Society
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• v.39 no.4
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• pp.79-91
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• 2019

The capacity credit (CC) is a key metric for mid- to long-term power system capacity planning. The purpose of this study is to estimate the CCs of domestic wind turbines. Based on hourly capacity factor (CF) data during the seven years from 2011 to 2017, the new so-called probabilistic CF scheme is introduced to effectively reflect the variability of CFs on CC estimation. The CCs are then estimated through the CF-based method and the ELCC (Effective Load Carrying Capability) method reflecting the probabilistic CF scheme, and the results are compared. The results show that the CC value 0.019 for domestic wind turbines proposed in the $8^{th}$ Basic Plan for Electricity Supply and Demand corresponds to the CC with a confidence level slightly lower than 95%.

• Nuclear Engineering and Technology
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• v.50 no.8
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• pp.1210-1216
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• 2018

Site or multi-unit (MU) risk assessment has been a major issue in the field of nuclear safety study since the Fukushima accident in 2011. There have been few methods or experiences for MU risk assessment because the Fukushima accident was the first real MU accident and before the accident, there was little expectation of the possibility that an MU accident will occur. In addition to the lack of experience of MU risk assessment, since an MU nuclear power plant site is usually very complex to analyze as a whole, it was considered that a systematic method such as probabilistic safety assessment (PSA) is difficult to apply to MU risk assessment. This paper proposes a new MU risk assessment methodology by using the conventional PSA methodology which is widely used in nuclear power plant risk assessment. The logical failure structure of a site with multiple units is suggested from the definition of site risk, and a decomposition method is applied to identify specific MU failure scenarios.

• Structural Engineering and Mechanics
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• v.92 no.1
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• pp.65-79
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• 2024

This work aims to present an analysis of the structural reliability of reinforced concrete (RC) columns designed according to the general method outlined in Eurocode 2 (EN 1992-1-1 2004). Probabilistic analyses are conducted by integrating the Monte Carlo method with metamodels (or surrogate models) generated using Kriging and some machine learning techniques. The study was developed based on an algorithm that verifies the columns subject to biaxial bending, considering the physical and geometric nonlinearities. Columns were analyzed assuming sign inversion of end bending moments (with reverse curvature), which portray the typical situations in conventional structures of RC buildings. The probabilistic results reveal that the typical RC columns in buildings designed according to the design procedures of the studied standard, whether they are located at the center, corner, or edge, exhibit reliability levels surpassing those deemed acceptable within the technical community. Furthermore, the integration of surrogate models proves beneficial by alleviating the computational burden associated with evaluations while preserving accuracy.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.4
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• pp.273-282
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• 2023

Probabilistic safety assessment (PSA) has been widely used to evaluate the seismic risk of nuclear power plants (NPPs). However, studies on seismic PSA for process plants, such as gas plants, oil refineries, and chemical plants, have been scarce. This is because the major disasters to which these process plants are vulnerable include explosions, fires, and release (or dispersion) of toxic chemicals. However, seismic PSA is essential for the plants located in regions with significant earthquake risks. Seismic PSA entails probabilistic seismic hazard analysis (PSHA), event tree analysis (ETA), fault tree analysis (FTA), and fragility analysis for the structures and essential equipment items. Among those analyses, ETA can depict the accident sequence for core damage, which is the worst disaster and top event concerning NPPs. However, there is no general top event with regard to process plants. Therefore, PSA cannot be directly applied to process plants. Moreover, there is a paucity of studies on developing fragility curves for various equipment. This paper introduces PSA for gas plants based on FTA, which is then transformed into Bayesian network, that is, a probabilistic graph model that can aid risk-informed decision-making. Finally, the proposed method is applied to a gas plant, and several decision-making cases are demonstrated.

• Korean Journal of Construction Engineering and Management
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• v.2 no.2 s.6
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• pp.90-97
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• 2001

The paper considers non-deterministic methods of analysing the risk exposure in a cost estimate. The method(referred to as the 'Monte Carlo simulation' method) interprets cost data indirectly, to generate a probability distribution for total costs from the deficient elemental experience cost distribution. The Monte Carlo method is popular method for incorporating uncertainty relative to parameter values in risk assessment modelling. Non-deterministic methods, they are here presented as possibly effective foundation on which to risk management in cost estimating. The objectives of this research is to develop a computerized algorithms to forecast the probabilistic total construction cost and the elemental work cost at the planning stage.

• Journal of Mechanical Science and Technology
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• v.20 no.12
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• pp.2124-2135
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• 2006

In this paper, the reliability estimation of pipelines is performed by employing the probabilistic method, which accounts for the uncertainties in the load and resistance parameters of the limit state function. The FORM (first order reliability method) and the SORM (second order reliability method) are carried out to estimate the failure probability of pipeline utilizing the FAD (failure assessment diagram). And the reliability of pipeline is assessed by using this failure probability and analyzed in accordance with a target safety level. Furthermore, the MCS (Monte Carlo Simulation) is used to verify the results of the FORM and the SORM. It is noted that the failure probability increases with the increase of dent depth, gouge depth, operating pressure, outside radius, and the decrease of wall thickness. It is found that the FORM utilizing the FAD is a useful and is an efficient method to estimate the failure probability in the reliability assessment of a pipeline. Furthermore, the pipeline safety assessment technique with the deterministic procedure utilizing the FAD only is turned out more conservative than those obtained by using the probability theory together with the FAD. The probabilistic method such as the FORM, the SORM and the MCS can be used by most plant designers regarding the operating condition and design parameters.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.3
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• pp.224-232
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• 2015

In order to develop an active safety system which avoids or mitigates collisions with preceding vehicles such as autonomous emergency braking (AEB), accurate state estimation of the nearby vehicles is very important. In this paper, an algorithm is proposed using 3 dynamic models to better estimate the state of a vehicle which has various dynamic patterns in both longitudinal and lateral direction. In particular, the proposed algorithm is based on the Interacting Multiple Model (IMM) method which employs three different dynamic models, in cruise mode, lateral maneuver mode and longitudinal maneuver mode. In addition, a Probabilistic Data Association Filter (PDAF) is utilized as a data association algorithm which can improve the reliability of the measurement under a clutter environment. In order to verify the performance of the proposed method, it is simulated in comparison with a Kalman filter method which employs a single dynamic model. Finally, the proposed method is validated using radar data obtained from the field test in the proving ground.

• Structural Monitoring and Maintenance
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• v.2 no.2
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• pp.115-132
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• 2015

This paper presents a new method for seismic vulnerability assessment of buildings with reference to their operational limit state. The importance of this kind of evaluation arises from the civil protection necessity that some buildings, considered strategic for seismic emergency management, should retain their functionality also after a destructive earthquake. The method is based on the identification of experimental modal parameters from ambient vibrations measurements. The knowledge of the experimental modes allows to perform a linear spectral analysis computing the maximum structural drifts of the building caused by an assigned earthquake. Operational condition is then evaluated by comparing the maximum building drifts with the reference value assigned by the Italian Technical Code for the operational limit state. The uncertainty about the actual building seismic frequencies, typically significantly lower than the ambient ones, is explicitly taken into account through a probabilistic approach that allows to define for the building the Operational Index together with the Operational Probability Curve. The method is validated with experimental seismic data from a permanently monitored public building: by comparing the probabilistic prediction and the building experimental drifts, resulting from three weak earthquakes, the reliability of the method is confirmed. Finally an application of the method to a strategic building in Italy is presented: all the procedure, from ambient vibrations measurement, to seismic input definition, up to the computation of the Operational Probability Curve is illustrated.

• Proceedings of the Korea Water Resources Association Conference
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• 2001.05b
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• pp.745-750
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• 2001

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