• Structural Engineering and Mechanics
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• v.88 no.4
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• pp.311-322
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• 2023

Riverine flood is one of the critical natural threats to river-crossing bridges. As floods are the most-occurred natural hazard worldwide, survival probability of bridges due to floods must be assessed in a speedy but precise manner. In this regard, the paper presents a reliability-based approach for a rapid assessment of failure probability of vulnerable bridge components under floods. This robust method is generic in nature and can be applied to both concrete and steel girder bridges. The developed methodology essentially utilizes limit state performance functions, expressed in terms of capacity and flood demand, for probable failure modes of various vulnerable components of bridges. Advanced First Order Reliability Method (AFORM), Monte Carlo Simulation (MCS), and Latin Hypercube Simulation (LHS) techniques are applied for the purpose of reliability assessment and developing flood fragility curves of bridges in which flow velocity and water height are taken as flood intensity measures. Upon validating the proposed method, it is applied to a case study bridge that experiences the flood scenario of a river in Gujarat, India. Research outcome portrays how effectively and efficiently the proposed reliability-based method can be applied for a quick assessment of flood vulnerability of bridges in any flood-prone region of interest.

• Nuclear Engineering and Technology
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• v.56 no.5
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• pp.1654-1666
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• 2024

The goal of this research is to propose a way to maximize small modular reactor (SMR) utilization to gain better market feasibility in support of carbon neutrality. For that purpose, a comprehensive tool was developed, combining off-design thermohydraulic models, economic objective models (levelized cost of electricity, annual profit), non-economic models (saved CO2), a parameter input sampling method (Latin hypercube sampling, LHS), and a multi-objective evolutionary algorithm (Non-dominated Sorting Algorithm-2, NSGA2 method) for optimizing a SMR-combined heat and power cycle (CHP) system design. Considering multiple objectives, it was shown that NSGA2+LHS method can find better optimal solution sets with similar computational costs compared to a conventional weighted sum (WS) method. Out of multiple multi-objective optimal design configurations for a 105 MWe design generation rating, a chosen reference SMR-CHP system resulted in its levelized cost of electricity (LCOE) below $60/MWh for various heat prices, showing economic competitiveness for energy market conditions similar to South Korea. Examined economic feasibility may vary significantly based on CHP heat prices, and extensive consideration of the regional heat market may be required for SMR-CHP regional optimization. Nonetheless, with reasonable heat market prices (e.g. district heating prices comparable to those in Europe and Korea), SMR can still become highly competitive in the energy market if coupled with a CHP system.

• Geomechanics and Engineering
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• v.23 no.3
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• pp.261-273
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• 2020

This paper presents an efficient method utilizing user-defined computer functional codes to determine the reliability of an embankment slope with spatially varying soil properties in real time. The soils' mechanical properties varied with the soil layers that had different degrees of compaction and moisture content levels. The Latin Hypercube Sampling (LHS) for the degree of compaction and Kriging simulation of moisture content variation were adopted and programmed to predict their spatial distributions, respectively, that were subsequently used to characterize the spatial distribution of the soil shear strengths. The shear strength parameters were then integrated into the Geostudio command file to determine the safety factor of the embankment slope. An explicit metamodal for the performance function, using the Kriging method, was established and coded to efficiently compute the failure probability of slope with varying moisture contents. Sensitivity analysis showed that the proposed method significantly reduced the computational time compared to Monte Carlo simulation. About 300 times LHS Geostudio computations were needed to optimize precision and efficiency in determining the failure probability. The results also revealed that an embankment slope is prone to high failure risk if the degree of compaction is low and the moisture content is high.

• Nuclear Engineering and Technology
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• v.54 no.6
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• pp.2048-2054
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• 2022

The coolant temperature feedback coefficient is an important parameter of reactor core power control system. To study the coolant temperature feedback coefficient influence on the core power control system of small PWR, the core power control system is built with the nonlinear model and fuzzy control theory. Then, the uncertainty quantification method of reactor core parameters is established based on the Latin hypercube sampling method and the Bootstrap method. Finally, under the conditions of reactivity step perturbation and coolant inlet temperature step perturbation, uncertainty analysis for two cases is carried out. The result shows that with fuzzy controller and fuzzy PID controller, the uncertainty of the coolant temperature feedback coefficient affects the core power control system, and the maximum uncertainties of core relative power, coolant temperature deviation, fuel temperature deviation and total reactivity are acceptable.

• Korean Management Science Review
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• v.31 no.2
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• pp.33-48
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• 2014

It is important to maintain on operational availability of aircraft during wartime. The KF-16 fighter, the backbone of the ROKAF (Republic Of Korea Air Force), has a single engine. Therefore, the engine has a critical influence on operational availability. The purpose of this study is to estimate optimal levels of spare part inventories concerning both engines and modules. That is provided by linear programming methods utilizing a developed meta-model. For drawing out the meta-model, we develop a simulation model which can consider wartime demands. In the previous study, $2^k$ factorial design method is used to check the influence of each independent variable. That method requires relatively many scenarios because every extreme value combination of independent variables should be checked. However, this study adopts NOLH (Nearly Orthogonal Latin Hypercube) as an experimental design. By adopting NOLH, this study increases not only efficiency but also accuracy. That is proven by comparing the validity of the developed meta-model on both experimental designs. This study also utilizes the OptQuest simulation tool in ARENA to derive the optimal level of spare stocks. By comparing the result of OptQuest to that of the developed meta-model, the validity of this study is secured.

• Proceedings of the Korea Concrete Institute Conference
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• 2009.05a
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• pp.275-276
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• 2009

This paper provides a new approach for predicting the corrosion resistivity of reinforced concrete structures exposed to carbonation. In this method, the prediction can be updated successively by a Bayesian theory when additional data are available. The stochastic properties of model parameters are explicitly taken into account into the model. To simplify the procedure of the model, the probability of the durability limit is determined from the samples obtained from the Latin hypercube sampling technique. The new method may be very useful in designing important concrete structures and help to predict the remaining service life of existing concrete structures which have been monitored.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.5
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• pp.897-903
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• 2010

This paper presents a response surface method(RSM) with Latin Hypercube Sampling strategy, which is employed to optimize a magnet pole shape of large scale BLDC motor to minimize the cogging torque. The proposed LHS algorithm consists of the multi-objective Pareto optimization and (1+1) evolution strategy. The algorithm is compared with the uniform sampling point method in view points of computing time and convergence. In order to verify the developed algorithm, a 6 MW BLDC motor is simulated with 4 design parameters (arc length and 3 variables for magnet) and 4 constraints for minimizing of the cogging torque. The optimization procedure has two stages; the fist is to optimize the arc length of the PM and the second is to optimize the magnet pole shape by using the proposed hybrid algorithm. At the 3rd iteration, an optimal point is obtained, and the cogging torque of the optimized shape is converged to about 14% of the initial one. It means that 3 iterations aregood enough to obtain the optimal design parameters in the program.

• Earthquakes and Structures
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• v.18 no.3
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• pp.285-296
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• 2020

Dams are important structures for management of water supply for irrigation or drinking, flood control, and electricity generation. In seismic regions, the structural safety of concrete gravity dams is important due to the high potential of life and economic loss if they fail. Therefore, the seismic analysis of existing dams in seismically active regions is crucial for predicting responses of dams to ground motions. In this paper, earthquake response of concrete gravity dams is investigated using the finite element (FE) method. The FE model accounts for dam-water-foundation rock interaction by considering compressible water, flexible foundation effects, and absorptive reservoir bottom materials. Several uncertainties regarding structural attributes of the dam and external actions are considered to obtain the fragility curves of the dam-water-foundation rock system. The structural uncertainties are sampled using the Latin Hypercube Sampling method. The Pine Flat Dam in the Central Valley of Fresno County, California, is selected to demonstrate the methodology for several limit states. The fragility curves for base sliding, and excessive deformation limit states are obtained by performing non-linear time history analyses. Tensile cracking including the complex state of stress that occurs in dams was also considered. Normal, Log-Normal and Weibull distribution types are considered as possible fits for fragility curves. It was found that the effect of the minimum principal stress on tensile strength is insignificant. It is also found that the probability of failure of tensile cracking is higher than that for base sliding of the dam. Furthermore, the loss of reservoir control is unlikely for a moderate earthquake.

• 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.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.49-54
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• 2007

Structural integrity assessment technique for pavement system is studied considering the uncertainties among the material properties. The artificial neural networks technique is applied for the inverse analysis to estimate the elastic modulus based on the measured deflections from the FWD test. A computer code based on the spectral element method was developed for the accurate and fast analysis of the multi-layered soil structures, and the developed program was used for generating the training and testing patterns for the neural network. Neural networks was applied to estimate the elastic modulus of pavement system using the maximum deflections with and without the uncertainties in the material properties. It was found that the estimation results by the conventiona1 neural networks were very poor when there exist the uncertainties and the estimation results could be significantly improved by adopting the proposed method for generating training patterns considering the uncertainties among material properties.