• Journal of the Korean Society of Safety
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• v.37 no.1
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• pp.78-87
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• 2022

In the risk assessment of urban railway systems, a hazard log is created by identifying hazards from accident and failure data. Then, based on a risk matrix, evaluators analyze the frequency and severity of the occurrence of the hazards, conduct the risk assessment, and then establish safety measures for the risk factors prior to risk control. However, because subjective judgments based on the evaluators' experiences affect the risk assessment results, a more objective and automated risk assessment system must be established. In this study, we propose a risk assessment model in which an adaptive neuro-fuzzy inference system (ANFIS), which is combined in artificial neural networks (ANN) and fuzzy inference system (FIS), is applied to the risk assessment of urban railway systems. The newly proposed model is more objective and automated, alleviating the limitations of risk assessments that use a risk matrix. In addition, the reliability of the model was verified by comparing the risk assessment results and risk control priorities between the newly proposed ANFIS-based risk assessment model and the risk assessment using a risk matrix. Results of the comparison indicate that a high level of accuracy was demonstrated in the risk assessment results of the proposed model, and uncertainty and subjectivity were mitigated in the risk control priority.

• Structural Engineering and Mechanics
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• v.69 no.3
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• pp.317-326
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• 2019

This research aims to assess the tight seismic risk curve of the intake tower at Geumgwang reservoir by considering the recorded historical earthquake data in the Korean Peninsula. The seismic fragility, a significant part of risk assessment, is updated by using Bayesian inference to consider the uncertainties and computational efficiency. The reservoir is one of the largest reservoirs in Korea for the supply of agricultural water. The intake tower controls the release of water from the reservoir. The seismic risk assessment of the intake tower plays an important role in the risk management of the reservoir. Site-specific seismic hazard is computed based on the four different seismic source maps of Korea. Probabilistic Seismic Hazard Analysis (PSHA) method is used to estimate the annual exceedance rate of hazard for corresponding Peak Ground Acceleration (PGA). Hazard deaggregation is shown at two customary hazard levels. Multiple dynamic analyses and a nonlinear static pushover analysis are performed for deriving fragility parameters. Thereafter, Bayesian inference with Markov Chain Monte Carlo (MCMC) is used to update the fragility parameters by integrating the results of the analyses. This study proves to reduce the uncertainties associated with fragility and risk curve, and to increase significant statistical and computational efficiency. The range of seismic risk curve of the intake tower is extracted for the reservoir site by considering four different source models and updated fragility function, which can be effectively used for the risk management and mitigation of reservoir.

• Journal of Korean Society of Transportation
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• v.14 no.2
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• pp.119-136
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• 1996

The evaluation of risk level or possibility of traffic accidents is a fundamental task in reducing the dangers associated with current transportation system. However, due to the lack of data and basic researches for identifying such factors, evaluations so far have been undertaken by only the experts who can use their judgements well in this regard. Here comes the motivation this thesis to evaluate such risk level more or less in an automatic manner. The purpose of this thesis is to test the fuzzy-logic theory in evaluating the risk level of traffic accidents. In modeling the process of expert's logical inference of risk level determination, only the geometric features have been considered for the simplicity of the modeling. They are the visibility of road surface, horizontal alignment, vertical grade, diverging point, and the location of pedestrain crossing. At the same time, among some inference methods, fuzzy composition inference method has been employed as a back-bone inference mechanism. In calibration, the proposed model used four sites' data. After that, using calibrated model, six sites' risk levels have been identified. The results of the six sites' outcomes were quite similar to those of real world other than some errors caused by the enforcement of the model's output. But it seems that this kind of errors can be overcome in the future if some other factors such as driver characteristics, traffic environment, and traffic control conditions have been considered. Futhermore, the application of site's specific time series data would produce better results.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.1
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• pp.16-26
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• 1997

China is one of the few countries where natural disaster strike frequently and cause heavy damage. In this paper, we mathematically develop two models to assess fuzzy risk of natural disaster in China. One is to assess the risk based on database of historical disaster effects by using information diffusion method relevant in fuzzy information analysis. In another model, we give an overview over advanced method to calculate the risk of release, exposure and consequence assessent, where information distribution technique is used to calculate basic fuzzy relationships showing historical experience of natural disasters, and fuzzy approximate inference is employed to study loss risk based on these basic relationships. We also present an examples to show how to use the first model. Result show that the model is effective for natural disaster risk assessment.

• Structural Engineering and Mechanics
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• v.63 no.1
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• pp.47-53
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• 2017

The fundamental goal of this study is to minimize the uncertainty of the median fragility curve and to assess the structural vulnerability under earthquake excitation. Bayesian Inference with Markov Chain Monte Carlo (MCMC) simulation has been presented for efficient collapse response assessment of the independent intake water tower. The intake tower is significantly used as a diversion type of the hydropower station for maintaining power plant, reservoir and spillway tunnel. Therefore, the seismic fragility assessment of the intake tower is a pivotal component for estimating total system risk of the reservoir. In this investigation, an asymmetrical independent slender reinforced concrete structure is considered. The Bayesian Inference method provides the flexibility to integrate the prior information of collapse response data with the numerical analysis results. The preliminary information of risk data can be obtained from various sources like experiments, existing studies, and simplified linear dynamic analysis or nonlinear static analysis. The conventional lognormal model is used for plotting the fragility curve using the data from time history simulation and nonlinear static pushover analysis respectively. The Bayesian Inference approach is applied for integrating the data from both analyses with the help of MCMC simulation. The method achieves meaningful improvement of uncertainty associated with the fragility curve, and provides significant statistical and computational efficiency.

• Journal of the Korean Society of Safety
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• v.29 no.4
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• pp.184-190
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• 2014

Decommissioning process of nuclear facilities consist of a sequence of problem solving activities, because there may exist not only working environments contaminated by radiological exposure but also industrial hazards such as fire, explosions, toxic materials, and electrical and physical hazards. Therefore, not a few countries in the world have been trying to develop appropriate counter techniques in order to guarantee safety and efficiency of the process. In spite of that, there still exists neither domestic nor international standard. Unfortunately, however, there are few workers who experienced decommissioning operations a lot in the past. As a solution, it is quite necessary to utilize experts' opinions for risk assessment in decommissioning process. As for an individual hazard factor, risk assessment techniques are getting known to industrial workers with advance of safety technology, but the way how to integrate those results is not yet. This paper aimed to find out an appropriate technique to integrate individual risk assessment results from the viewpoint of experts. Thus, on one hand the whole risk assessment activity for decommissioning operations was modeled as a sequence of individual risk assessment steps which can be classified into two activities, decontamination and dismantling, and on the other, a risk assessment structure was introduced. The whole model was inferred with Fuzzy theory and techniques, and a numerical example was appended for comprehension.

• Proceedings of the Korean Geotechical Society Conference
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• 1999.03a
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• pp.269-276
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• 1999

The program ESRAS Ver 0.5 that can assess the risk of slopes by means of fuzzy inference is developed in this paper. The results of assessment involve the degree of stability of slopes, the possible travel distance of the soil mass being failed, and anticipated loss of life and properties. With this program, vulnerable slopes can be managed most effectively and the fuzzy inference is used to express quantitatively the judgement of an expert and the uncertainty of slope stability. The fuzzy rule base is composed of an evaluation list for slope stability together with the experience of an expert. This program has been examined for 88 slopes which have been failed or shown a possibility of failure. With this examination, the standards to assess the stability of slopes can be presented and it is proven that this is particularly useful in determining the priority of investment for remedial works of slopes.

• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.685-699
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• 2006

Commercial banks and other related areas have developed internal models to better quantify their financial risks. Since an appropriate credit risk model plays a very important role in the risk management at financial institutions, it needs more accurate model which forecasts the credit losses, and statistical inference on that model is required. In this paper, we propose a new method for estimating a default rate. It is a Bayesian approach using the power prior which allows for incorporating of historical data to estimate the default rate. Inference on current data could be more reliable if there exist similar data based on previous studies. Ibrahim and Chen (2000) utilize these data to characterize the power prior. It allows for incorporating of historical data to estimate the parameters in the models. We demonstrate our methodologies with a real data set regarding SOHO data and also perform a simulation study.

• Korean Journal of Cognitive Science
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• v.20 no.3
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• pp.335-355
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• 2009

We examined risk judgment and the accuracy of inference based on two kinds of probabilities in a Bayesian inference task: the death probability from a disease (base rates) and the probability of having a disease with positive results in the screening test (posterior probabilities). Risk information were presented in either a probability or a frequency format. In Study 1, we found a numerical format effect for both base rate and posterior probability. Participants rated information as riskier and inferred more accurately in the frequency condition than in the probability condition for both base rate and posterior probability. However, there was no frequency range effect, which suggested that the ranges of frequency format did not influence risk ratings. In order to find out how the analytic thought system influences risk ratings, we compared the ratings of a computation condition and those of a no-computation condition and still found the numerical format effect in computation condition. In Study 2, we examined the numerical format effect and frequency range effect in a high and a low probability condition and found the numerical format effect at each probability level. This result suggests that people feel riskier in the frequency format than in the probability format regardless of the base rates and the posterior probability. We also found a frequency range effect only for the low base rate condition. Our results were discussed in terms of the dual process theories.

• Journal of Korea Society of Industrial Information Systems
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• v.14 no.4
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• pp.65-74
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• 2009

The risk of health impairment due to poor ventilation, fire and explosion by inflammable materials, and other unintended occurrences is always present in dangerous workplaces such as manholes, underground septic tanks, storage tanks and confined areas. Therefore, it a system which can monitor harmful working environment through sensors in workplace on a realtime basis and keep workers safe from the risk is needed. This paper has attempted to design an inference engine to monitor harmful environments in the workplace. The proposed inference engine has a rule-based system structure using JESS. This system is not confined to a particular computing platform and is easily interlocked with OSGi-based middleware.