• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1682-1685
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• 2005

System algorithms estimated by deterministic input may occur the error between predicted and actual output. Especially, actual system can't predict the exact outputs due to uncertainty and tolerance of input parameters. A single output to a set of inputs has a limited value without the variation. Hence, we should consider various scatters caused by the load assessment, material characteristics, stress analysis and manufacturing methods in order to perform the robust design or estimate the reliability of structure. The system design with uncertainty should perform the probabilistic structural optimization with the statistical response and the reliability. This method calculated the probability distributions of the characteristics such as stress by combining stress analysis, response surface methodology and Monte-Carlo Method and got the probabilistic sensitivity. The sensitivity of structural response with respect to inconstant design variables was estimated by fracture probability. Therefore, this paper proposed the probabilistic reliability design method for fracture of uncorved freight end beam and the design criteria by fracture probability.

• Nuclear Engineering and Technology
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• v.37 no.3
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• pp.245-258
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• 2005

This paper addresses two types of uncertainty: stochastic uncertainty and subjective uncertainty in probabilistic accident consequence assessments. The off-site consequence assessment code OSCAAR has been applied to uncertainty and sensitivity analyses on the individual risks of early fatality and latent cancer fatality in the population outside the plant boundary due to a severe accident. A new stratified meteorological sampling scheme was successfully implemented into the trajectory model for atmospheric dispersion and the statistical variability of the probability distributions of the consequence was examined. A total of 65 uncertain input parameters was considered and 128 runs of OSCAAR with 144 meteorological sequences were performed in the parameter uncertainty analysis. The study provided the range of uncertainty for the expected values of individual risks of early and latent cancer fatality close to the site. In the sensitivity analyses, the correlation/regression measures were useful for identifying those input parameters whose uncertainty makes an important contribution to the overall uncertainty for the consequence. This could provide valuable insights into areas for further research aiming at reducing the uncertainties.

• Earthquakes and Structures
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• v.11 no.1
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• pp.109-127
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• 2016

This paper examines the contribution of three sources of uncertainties to probabilistic seismic behaviour of wood frame buildings, including ground motions, intensity and seismic mass. This sensitivity analysis is performed using three methods, including the traditional method based on the conditional distributions of ground motions at given intensity measures, a method using the summation of conditional distributions at given ground motion records, and the Monte Carlo simulation. FEMA P-695 ground motions and its scaling methods are used in the analysis. Two archetype buildings are used in the sensitivity analysis, including a two-storey building and a four-storey building. The results of these analyses indicate that using data-fitting techniques to obtain probability distributions may cause some errors. Linear interpolation combined with data-fitting technique may be employed to improve the accuracy of the calculated exceeding probability. The procedures can be used to quantify the risk of wood frame buildings in seismic events and to calibrate seismic design provisions towards design code improvement.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.6 s.177
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• pp.1529-1539
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• 2000

A newly developed optimization method which uses the genetic algorithm combined with the sensitivity analysis is presented in this paper. The genetic algorithm is a probabilistic method, searching the optimum at several points simultaneously, requiring only the values of the object and constraint functions. It has therefore more chances to find global solution and can be applied various problems. Nevertheless, it has such shortcomings that even it approaches the optimum rapidly in the early stage, it slows down afterward and it can't consider the constraints explicitly. It is only because it can't search the local area near the current points. The traditional method, on the other hand, using sensitivity analysis is of great advantage in searching the near optimum. Thus the combination of the two techniques makes use of the individual advantages, that is, the superiority both in global searching by the genetic algorithm and in local searching by the sensitivity analysis. Application of the method to the several test functions verifies that the method suggested is very efficient and powerful to find the global solutions, and that the constraints can be considered properly.

• Smart Structures and Systems
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• v.20 no.2
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• pp.207-217
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• 2017

Because of the inevitable uncertainties such as structural parameters, external excitations and measurement noises, the effects of uncertainties should be taken into consideration in structural damage detection. In this paper, two probabilistic structural damage detection approaches are proposed to account for the underlying uncertainties in structural parameters and external excitation. The first approach adopts the statistical moment-based structural damage detection (SMBDD) algorithm together with the sensitivity analysis of the damage vector to the uncertain parameters. The approach takes the advantage of the strength SMBDD, so it is robust to measurement noise. However, it requests the number of measured responses is not less than that of unknown structural parameters. To reduce the number of measurements requested by the SMBDD algorithm, another probabilistic structural damage detection approach is proposed. It is based on the integration of structural damage detection using temporal moments in each time segment of measured response time history with the sensitivity analysis of the damage vector to the uncertain parameters. In both approaches, probability distribution of damage vector is estimated from those of uncertain parameters based on stochastic finite element model updating and probabilistic propagation. By comparing the two probability distribution characteristics for the undamaged and damaged models, probability of damage existence and damage extent at structural element level can be detected. Some numerical examples are used to demonstrate the performances of the two proposed approaches, respectively.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.11
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• pp.453-462
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• 2006

This paper evaluates the reliability of KEPCO system using TRELSS, which is a probabilistic reliability evaluation program for large-scaled power system. In order to reflect the characteristic of KEPCO system, the sensitivity of reliability indices such as LOLP, EDLC, EENS and Energy Curtailment for variations of TRELSS parameter and input data was analyzed. Additionally, probabilistic reliability of KEPCO system reflecting sensitivity analysis results was systematically evaluated and simulated. Finally, maximum acceptable FOR of KEPCO system to satisfy reliability criterion, which meet in process of establishing the basic plan for long-term electricity supply and demand is suggested.

• Smart Structures and Systems
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• v.10 no.2
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• pp.89-110
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• 2012

This study was intended to efficiently perform the probabilistic optimal safety assessment of steel cable-stayed bridges (SCS bridges) using stochastic finite element analysis (SFEA) and expected life-cycle cost (LCC) concept. To that end, advanced probabilistic finite element algorithm (APFEA) which enables to execute the static and dynamic SFEA considering aleatory uncertainties contained in random variable was developed. APFEA is the useful analytical means enabling to conduct the reliability assessment (RA) in a systematic way by considering the result of SFEA based on linearity and nonlinearity of before or after introducing initial tensile force. The appropriateness of APFEA was verified in such a way of comparing the result of SFEA and that of Monte Carlo Simulation (MCS). The probabilistic method was set taking into account of analytical parameters. The dynamic response characteristic by probabilistic method was evaluated using ASFEA, and RA was carried out using analysis results, thereby quantitatively calculating the probabilistic safety. The optimal design was determined based on the expected LCC according to the results of SFEA and RA of alternative designs. Moreover, given the potential epistemic uncertainty contained in safety index, failure probability and minimum LCC, the sensitivity analysis was conducted and as a result, a critical distribution phase was illustrated using a cumulative-percentile.

• Computational Structural Engineering
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• v.4 no.4
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• pp.125-133
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• 1991

For the purpose of efficiently calculating the design sensitivity and the reliability for the complicated structures in which the structural responses or limit state functions are given by implicit form, 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 method needed in the reliability-based design is proposed. As numerical examples, two thin plates are analyzed for the cases of plane stress and plate bending. The initial yielding is defined as failure criterion, and applied loads, yield stress, plate thickness, Young's modulus and Poisson's ratio are treated as random variables. It is found that the response variances and the failure probabilities calculated by the proposed PFEM-based reliability method show good agreement with those by Monte Carlo simulation. The probabilistic design sensitivity evaluates explicitly the contribution of each random variable to probability of failure. Further, the design change can be evaluated without any difficulty, and their effect on reliability can be estimated quickly with high accuracy.

• Journal of the Korea institute for structural maintenance and inspection
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• v.11 no.1
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• pp.141-152
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• 2007

Considering uncertainties of random input data, it is more reasonable to use probabilistic method than the conventional deterministic method for the design of structures or for the assessment of the responses of structures, which are designed as safe even under extreme loads. Therefore, to assess the quantitative effects of the constructed cable stayed bridge by the input random variables, a sensitivity analysis is studied. Using perturbation method, an analysis program is developed for the iterative probabilistic finite element analyses and sensitivity analyses of the cable stayed bridge, except the initial shape analysis. Monte-Carlo Simulations were used for the verification of the developed program. The results of sensitivity analysis shows the governing effects of external loads. Because the results also provide the sensitive effects of the stiffness of members and the magnitudes of prestressing force of cables, the developed

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.275-282
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• 2006

System algorithms estimated by deterministic input may occur the error between predicted and actual output. Especially, actual system can't predict the exact outputs due to uncertainty and tolernce of input parameters. A single output to a set of inputs has a limited value without the variation. Hence, we should consider various scatters caused by the load assessment, material characteristics, stress analysis and manufacturing methods in order to perform the robust design or etimate the reliability of structure. The system design with uncertainty should perform the probabilistic structural optimization with the statistical response and the reliability. This method calculated the probability distributions of the characteristics such as stress by combining stress analysis, response surface methodology and Monte Carlo simulation and got the probabilistic sensitivity. The sensitivity of structural response with respect to in constant design variables was estimated by fracture probability. Therefore, this paper proposed the probabilistic reliability design method for fracture of uncorved freight end beam and the design criteria by fracture probability.