• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.11a
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• pp.133-134
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• 2022

Recently, the criteria for assessing industrial accidents have been replaced by the mortality rate. It was found that the number of deaths from excavation work was the highest among construction machinery. The risk assessment is being conducted, however the industrial accident mortality rate has not decreased. Accordingly, this study aims to provide the basic for the create of a risk assessment model specialized in construction work at excavator. It provides absolute value from the risk model which is capable of delivery the probability of a disaster. In addition, we provide a relative risk model that compares the risk through scores between detailed works. The relative risk model is combined by likelihood and severity; the likelihood indicates the frequency of accidents and the severity indicates seriousness of fatal accidents. A variable that reflects the conditions of the construction site was added to the risk assessment model based on past disaster cases. And using the concepts of probability and average, the risk assessment process was quantified and used as an objective indicator. Therefore, the model is expected to reduce disasters by raising the awareness of disasters.

• Journal of Construction Engineering and Project Management
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• v.7 no.3
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• pp.39-55
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• 2017

Preeminent performance of construction industry are unattainable with poor productivity resulting in time and cost over runs. Enhancement in productivity cannot be achieved without identifying and analyzing factors that adversely affect productivity. The objective therefore is to propose a productivity analysis model to quantify the probability of effect of factors influencing productivity by using fuzzy logic incorporated with relative importance index method, for various types of construction projects. To achieve this objective, a questionnaire survey was carried out targeting respondents of Indian construction industry, from four distinct projects, namely, residential, commercial, infrastructure and industrial projects. Based on questionnaire administered, the relative importance and ranks of factors demonstrated using relative importance index method. Probability assessment model to analyze productivity was then developed by using Fuzzy Logic Toolbox of MATLAB. The applicability of the proposed model was tested in seven construction projects and the probability of impact of factors on productivity evaluated. The results of application of model in the construction firms infers that the most contributing factor groups for most of the projects were discerned to be manpower, motivation and time group.

• Journal of Computational Design and Engineering
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• v.1 no.4
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• pp.266-271
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• 2014

The survivability of the naval ship is the capability of a warship to avoid or withstand a hostile environment. The survivability of the naval ship assessed by three categories (susceptibility, vulnerability and recoverability). The magnitude of susceptibility of a warship encountering with threat is dependent upon the attributes of detection equipment and weapon system. In this paper, as a part of a naval ship's survivability analysis, an assessment process model for the ship's susceptibility analysis technique is developed. Naval ship's survivability emphasizing the susceptibility is assessed by the probability of detection, and the probability of hit. Considering the radar cross section (RCS), the assessment procedure for the susceptibility is described. It's emphasizing the simplified calculation model based on the probability density function for probability of hit. Assuming the probability of hit given a both single-hit and multiple-hit, the susceptibility is accessed for a RCS and the hit probability for a rectangular target is applied for a given threat.

• Journal of Mechanical Science and Technology
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• v.16 no.7
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• pp.889-895
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• 2002

This paper presents the effect of external corrosion, material properties, operation condition and design thickness in pipeline on failure prediction using a failure probability model. The predicted failure assessment for the simulated corrosion defects discovered in corroded pipeline is compared with that determined by ANSI/ASME B31G code and a modified B31G method. The effects of environmental, operational, and random design variables such as defect depth, pipe diameter, defect length, fluid pressure, corrosion rate, material yield stress and pipe thickness on the failure probability are systematically studied using a failure probability model for the corrosion pipeline.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.310-315
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• 2003

This paper presents the effect of boundary condition of failure pressure model for buried pipelines on failure prediction by using a failure probability model. The first order Taylor series expansion of the limit state function is used in order to estimate the probability of failure associated with various corrosion defects for long exposure periods in years. A failure pressure model based on a failure function composed of failure pressure and operation pressure is adopted for the assessment of pipeline failure. The effects of random variables such as defect depth, pipe diameter, defect length, fluid pressure, corrosion rate, material yield stress, material ultimate tensile strength and pipe thickness on the failure probability of the buried pipelines are systematically studied by using a failure probability model for the corrosion pipeline.

• Journal of the Korean Society of Systems Engineering
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• v.17 no.1
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• pp.53-64
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• 2021

Wargame is a simulated military operation with certain rules, specifications, and procedures, in which soldiers can virtually and indirectly experience the war. The ROK Navy operates the Cheonghae model, a training wargame model for helping commanders and staff master the procedures for conducting the war. It is important for commanders, staff and analysts to know whether a warship can perform its missions and how long it can last during a war. In existing model, the Cheonghae, the probability of kill of a warship is calculated simply considering the number of tonnage without any stochastic elements, and the warship's mission availability is also determined based on predetermined values. With this model, it is difficult to get a value of the probability of kill that makes sense. In this dissertation, the author has developed a probabilistic model in which the warship vulnerability data of ROK-JMEM can be used. A conceptual model and methodology that can evaluate the mission performance of personnel, equipment, and supplies has been proposed. This can be expanded to a comprehensive assessment of wartime warship loss rates by integrating damage rates for personnel, equipment, and supplies in wartime.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2003.04a
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• pp.357-365
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• 2003

The major problems with the conventional neural network, especially Back Propagation Neural Network, arise from the necessity of many training data for neural network learning and ambiguity in the relation of neural network structure to the convergence of solution. In this paper, the PNN is used as a pattern classifier to detect the damage of structure to avoid those drawbacks of the conventional neural network. In the PNN-based pattern classification problems, the probability density function for patterns is usually assumed by Gaussian distribution. But, in this paper, several probability density functions are investigated in order to select the most approriate one for structural damage assessment.

• Safety and Health at Work
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• v.9 no.1
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• pp.42-52
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• 2018

Background: Maintenance operations on-board ships are highly demanding. Maintenance operations are intensive activities requiring high man-machine interactions in challenging and evolving conditions. The evolving conditions are weather conditions, workplace temperature, ship motion, noise and vibration, and workload and stress. For example, extreme weather condition affects seafarers' performance, increasing the chances of error, and, consequently, can cause injuries or fatalities to personnel. An effective human error probability model is required to better manage maintenance on-board ships. The developed model would assist in developing and maintaining effective risk management protocols. Thus, the objective of this study is to develop a human error probability model considering various internal and external factors affecting seafarers' performance. Methods: The human error probability model is developed using probability theory applied to Bayesian network. The model is tested using the data received through the developed questionnaire survey of >200 experienced seafarers with >5 years of experience. The model developed in this study is used to find out the reliability of human performance on particular maintenance activities. Results: The developed methodology is tested on the maintenance of marine engine's cooling water pump for engine department and anchor windlass for deck department. In the considered case studies, human error probabilities are estimated in various scenarios and the results are compared between the scenarios and the different seafarer categories. The results of the case studies for both departments are also compared. Conclusion: The developed model is effective in assessing human error probabilities. These probabilities would get dynamically updated as and when new information is available on changes in either internal (i.e., training, experience, and fatigue) or external (i.e., environmental and operational conditions such as weather conditions, workplace temperature, ship motion, noise and vibration, and workload and stress) factors.

• Journal of Environmental Health Sciences
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• v.45 no.5
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• pp.497-510
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• 2019

Objectives: The aim of this study was to secure the reliability of using the CalTOX model when evaluating LADD (or ADD) and Risk (or HQ) among local residents for the emission of BTEX (Benzene, Toluene, Ethylbenzene, Xylene) and by closely examining the difference in the confidence interval of the assessment outcomes according to the difference in the probability density function of input variables. Methods: The assessment was made by dividing it according to the method ($I^{\dagger}$) of inputting the probability density function in meteorological variables of the model with log-normal distribution and the method of inputting ($II^{\ddagger}$) after grasping the optimal probability density function using @Risk. A T-test was carried out in order to analyze the difference in confidence interval of the two assessment results. Results: It was evaluated to be 1.46E-03 mg/kg-d in LADD of Benzene, 1.96E-04 mg/kg-d in ADD of Toluene, 8.15E-05 mg/kg-d in ADD of Ethylbenzene, and 2.30E-04 mg/kg-d in ADD of Xylene. As for the predicted confidence interval in LADD and ADD, there was a significant difference between the $I^{\dagger}$ and $II^{\ddagger}$ methods in $LADD_{Inhalation}$ for Benzene, and in $ADD_{Inhalation}$ and ADD for Toluene and Xylene. It appeared to be 3.58E-05 for risk in Benzene, 3.78E-03 for HQ in Toluene, 1.48E-03 for HQ in Ethylbenzene, and 3.77E-03 for HQ in Xylene. As a result of the HQ in Toluene and Xylene, the difference in confidence interval between the $I^{\dagger}$ and $II^{\ddagger}$ methods was shown to be significant. Conclusions: The human risk assessment for BTEX was made by dividing it into the method ($I^{\dagger}$) of inputting the probability density function of meteorological variables for the CalTOX model with log-normal distribution, and the method of inputting ($II^{\ddagger}$) after grasping the optimal probability density function using @Risk. As a result, it was identified that Risk (or HQ) is the same, but that there is a significant difference in the confidence interval of Risk (or HQ) between the $I^{\dagger}$ and $II^{\ddagger}$ methods.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.11
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• pp.1571-1577
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• 2012

This paper presents a method for assessing the voltage sag performance of power system involving wind power generation. Wind power generation is considered as one of the most desirable renewable energy sources. However, wind power generation have uncertain energy output and it is difficult to control the output. The existing methods of voltage sag assessment are not reflected the characteristics of wind power generation. Therefore, in order to more accurately assess the voltage sag performance, the probability of wind power operation is evaluated. In this paper, the probability is determined by combining the wind speed model with the output curve of wind turbine. The probability of wind power operation is reflected as a parameter in voltage sag assessment. The proposed method can provide more accurate results of voltage sag assessment for the case involving the wind power generation.