• Smart Structures and Systems
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• v.15 no.3
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• pp.751-767
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• 2015

A new procedure is proposed for assessing probabilistic condition of structures considering effect of measured data uncertainty. In this procedure, multiple Finite Element (FE) models are identified by using weighting vectors that represent the uncertainty conditions of measured data. The distribution of structural parameters is analysed using a Principal Component Analysis (PCA) in relation to uncertainty conditions, and the identified models are classified into groups according to their similarity by using a K-means method. The condition of a structure is then assessed probabilistically using FE models in the classified groups, each of which represents specific uncertainty condition of measured data. Yeondae bridge, a steel-box girder expressway bridge in Korea, is used as an illustrative example. Probabilistic condition of the bridge is evaluated by the distribution of load rating factors obtained using multiple FE models. The numerical example shows that the proposed method can quantify uncertainty of measured data and subsequently evaluate efficiently the probabilistic condition of bridges.

• Architectural research
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• v.15 no.3
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• pp.151-158
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• 2013

Offshore oil and gas process plants are exposed to hazardous accidents such as explosion and fire, so that the structural components should resist such accidental loads. Given the possibilities of thousands of different scenarios for the occurrence of an accidental hazard, the best way to predict a reasonable size of a specific accidental load would be the employment of a probabilistic approach. Having the fact that a specific procedure for probabilistic accidental hazard analysis has not yet been established especially for explosion and fire hazards, it is widely accepted that engineers usually take simple and conservative figures in assuming uncertainties inherent in the procedure, resulting either in underestimation or more likely in overestimation in the topside structural design for offshore plants. The variation in the results of a probabilistic approach is determined by the assumptions accepted in the procedures of explosion probability computation, explosion analysis, and structural analysis. A design overpressure load for a sample offshore plant is determined according to the proposed probabilistic approach in this study. CFD analysis results using a Flame Acceleration Simulator, FLACS_v9.1, are utilized to create an overpressure hazard curve. Moreover, the negative impulse and frequency contents of a blast wave are considerably influencing structural responses, but those are completely ignored in a widely used triangular form of blast wave. An idealistic blast wave profile deploying both negative and positive pulses is proposed in this study. A topside process module and piperack with blast wall are 3D FE modeled for structural analysis using LS-DYNA. Three different types of blast wave profiles are applied, two of typical triangular forms having different impulse and the proposed load profile. In conclusion, it is found that a typical triangular blast load leads to overestimation in structural design.

• Journal of Environmental Impact Assessment
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• v.9 no.3
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• pp.203-214
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• 2000

Health risk assessment is applied to streamlining LCA(Life Cycle Assessment) using Monte carlo simulation for probabilistic/stochastic exposure and risk distribution analysis caused by data variability and uncertainty. A case study was carried out to find benefits of this application. BTC(Benzene, Trichloroethylene, Carbon tetrachloride mixture alias) personal exposure cases were assumed as production worker(in workplace), manager(in office) and business man(outdoor). These cases were different from occupational retention time and exposure concentration for BTC consumption pattern. The result of cancer risk in these 3 scenario cases were estimated as $1.72E-4{\pm}1.2E+0$(production worker; case A), $9.62E-5{\pm}1.44E-5$(manger; case B), $6.90E-5{\pm}1.16E+0$(business man; case C), respectively. Portions of over acceptable risk 1.00E-4(assumed standard) were 99.85%, 38.89% and 0.61%, respectively. Estimated BTC risk was log-normal pattern, but some of distributions did not have any formal patterns. Except first impact factor(BTC emission quantity), sensitivity analysis showed that main effective factor was retention time in their occupational exposure sites. This case study is a good example to cover that LCA with probabilistic risk analysis tool can supply various significant information such as statistical distribution including personal/environmental exposure level, daily time activity pattern and individual susceptibility. Further research is needed for investigating real data of these input variables and personal exposure concentration and application of this study methodology.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1991.10a
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• pp.57-62
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• 1991

For the purpose of developing the method for efficiently calculating the design sensitivity and the reliability for the complicated structure such as ship structure, the probabilistic finite element method is introduced to formulate the deterministic design sensitivity analysis method and incorporated with the second moment reliability methods such as MVFOSM, AFOSM and SORM. Also, the probabilistic design sensitivity analysis needed in the reliability-based design is performed. The reliability analysis is carried out for the initial yielding failure, in which the derivative derived in the deterministic desin sensitivity is used. The present PFEM-based reliability method shows good agreement with Monte Carlo method in terms with the variance of response and the associated probability of failure even at the first or first few iteration steps. The probabilistic design sensitivity analysis evaluates explicitly the contribution of each random variable to probability of failure. Further, the reliability index variation can be easily predicted by the variation of the mean and the variance of the random variables.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.3
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• pp.632-638
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• 2009

Probabilistic Latent Semantic Analysis has many applications in information retrieval and filtering, natural language processing, machine learning from text, and in related areas. In this paper, we propose an algorithm using weighted Probabilistic Latent Semantic Analysis Model to find the contextual phrases and opinions from documents. The traditional keyword search is unable to find the semantic relations of phrases, Overcoming these obstacles requires the development of techniques for automatically classifying semantic relations of phrases. Through experiments, we show that the proposed algorithm works well to discover semantic relations of phrases and presents the semantic relations of phrases to the vector-space model. The proposed algorithm is able to perform a variety of analyses, including such as document classification, online reputation, and collaborative recommendation.

• 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.

• Structural Engineering and Mechanics
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• v.20 no.5
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• pp.527-542
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• 2005

The main objective of this study is to examine the accuracy of the complete quadratic combination (CQC) rule with the modal responses defined by the ordinates of the uniform hazard spectra (UHS) to evaluate the peak responses of the multi-degree-of-freedom (MDOF) systems subjected to nonstationary seismic excitations. For the probabilistic analysis of the peak responses, it is considered that the seismic excitations can be modeled using evolutionary power spectra density functions with uncertain model parameters. More specifically, a seismological model and the Kanai-Tajimi model with the boxcar or the exponential modulating functions were used to define the evolutionary power spectral density functions in this study. A set of UHS was obtained based on the probabilistic analysis of transient responses of single-degree-of-freedom systems subjected to the seismic excitations. The results of probabilistic analysis of the peak responses of MDOF systems were obtained, and compared with the peak responses calculated by using the CQC rule with the modal responses given by the UHS. The comparison seemed to indicate that the use of the CQC rule with the commonly employed correlation coefficient and the peak modal responses from the UHS could lead to significant under- or over-estimation when contributions from each of the modes are similarly significant.

• Advances in concrete construction
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• v.9 no.4
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• pp.337-343
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• 2020

In the present study, by employing fragility analysis, the seismic vulnerability of a concrete girder bridge, one of the most common existing structural bridge systems, has been performed. To this end, drift demand model as a fundamental ingredient of any probabilistic decision-making analyses is initially developed in terms of the two most common intensity measures, i.e., PGA and Sa (T₁). Developing a probabilistic demand model requires a reliable database that is established in this paper by performing incremental dynamic analysis (IDA) under a set of 20 ground motion records. Next, by employing Bayesian statistical inference drift demand models are developed based on pre-collapse data obtained from IDA. Then, the accuracy and reasonability of the developed models are investigated by plotting diagnosis graphs. This graphical analysis demonstrates probabilistic demand model developed in terms of PGA is more reliable. Afterward, fragility curves according to PGA based-demand model are developed.

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3559-3570
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• 2023

In the decommissioning of nuclear facilities, Derived Concentration Guideline Level (DCGL) derivation is necessary for the release of the facility after the site remediation, which also needs to be implemented in the stage of establishing a decommissioning planning. In order to derive DCGL, the dose assessment for the receptors can be conducted from residual radioactivity by using RESRAD code. When performing sensitivity analysis on probabilistic parameters, secondary evaluation is performed by assigning a single value for parameters classified as sensitive. However, several options may arise in the handling of nonsensitive parameters. Therefore, we compared the results of the first execution of RESRAD applying probabilistic parameters for each scenario with the results of the second execution applying a single value to sensitive parameters among the probabilistic parameters. In addition, we analyzed the effect of setting options for non-sensitive parameters. As a result, the effect on DCGL were different depending on the application scenario, the target radionuclides, and the input parameter selections. In terms of the overall evaluation period, the DCGL graph of the default option was generally shown as the most conservative except for some radionuclides. However, it will not necessarily be given priority in the aspect of the need to reflect site characteristics. The reason for selecting a probabilistic parameter is the availability of the parameter and the uncertainty of applying a single value. Therefore, as an alternative, it can be consistently applied to distribution as an option for non-sensitive parameters after sensitivity analysis.

• Journal of Environmental Science International
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• v.22 no.8
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• pp.965-977
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• 2013

Reliable long-term streamflow forecasting is invaluable for water resource planning and management which allocates water supply according to the demand of water users. It is necessary to get probabilistic forecasts to establish risk-based reservoir operation policies. Probabilistic forecasts may be useful for the users who assess and manage risks according to decision-making responding forecasting results. Probabilistic forecasting of seasonal inflow to Andong dam is performed and assessed using selected predictors from sea surface temperature and 500 hPa geopotential height data. Categorical probability forecast by Piechota's method and logistic regression analysis, and probability forecast by conditional probability density function are used to forecast seasonal inflow. Kernel density function is used in categorical probability forecast by Piechota's method and probability forecast by conditional probability density function. The results of categorical probability forecasts are assessed by Brier skill score. The assessment reveals that the categorical probability forecasts are better than the reference forecasts. The results of forecasts using conditional probability density function are assessed by qualitative approach and transformed categorical probability forecasts. The assessment of the forecasts which are transformed to categorical probability forecasts shows that the results of the forecasts by conditional probability density function are much better than those of the forecasts by Piechota's method and logistic regression analysis except for winter season data.