• Geomechanics and Engineering
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• v.34 no.6
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• pp.597-609
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• 2023

One of the applicable methods for the stabilization of soil walls is the nailing system which consists of tensile struts. The stability and safety of soil nail wall systems are influenced by the geometrical parameters of the nailing system. Generally, the determination of nailing parameters in order to achieve optimal performance of the nailing system for the safety of soil walls is defined in the framework of optimization problems. Also, according to the various uncertainty in the mechanical parameters of soil structures, it is necessary to evaluate the reliability of the system as a probabilistic problem. In this paper, the optimal design of the nailing system is carried out in deterministic and probabilistic cases using meta-heuristic and reliability-based design optimization methods. The colliding body optimization algorithm and first-order reliability method are used for optimization and reliability analysis problems, respectively. The objective function is defined based on the total cost of nails and safety factors and reliability index are selected as constraints. The mechanical properties of the nailing system are selected as design variables and the mechanical properties of the soil are selected as random variables. The results show that the reliability of the optimally designed soil nail system is very sensitive to uncertainty in soil mechanical parameters. Also, the design results are affected by uncertainties in soil mechanical parameters due to the values of safety factors. Reliability-based design optimization results show that a nailing system can be designed for the expected level of reliability and failure probability.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.5
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• pp.799-807
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• 2020

This paper deals with identification of probabilistic design using reliability based robust optimization in structure design of automatic salt collector. The thickness sizing variables of main structure member in the automatic salt collector were considered the random design variables including the uncertainty of corrosion that would be an inevitable hazardousness in the saltern work environment. The probabilistic constraint functions were selected from the strength performances of the automatic salt collector. The reliability based robust optimum design problem was formulated such that the random design variables were determined by minimizing the weight of the automatic salt collector subject to the probabilistic strength performance constraints evaluating from reliability analysis. Mean value reliability method and adaptive importance sampling method were applied to the reliability evaluation in the reliability based robust optimization. The three sigma level quality was considered robustness in side constraints. The probabilistic optimum design results according to the reliability analysis methods were compared to deterministic optimum design results. The reliability based robust optimization using the mean value reliability method showed the most rational results for the probabilistic optimum structure design of the automatic salt collector.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.8
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• pp.24-32
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• 2006

Using a deterministic design optimization, the effect of uncertainty can result in violation of constraints and deterioration of performances. For this reason, design optimization is required to guarantee reliability for constraints and ensure robustness for an objective function under uncertainty. Therefore, this study drew Monte Carlo Simulation(MCS) for the evaluation of reliability and robustness, and selected an artificial neural network as an approximate model that is suitable for MCS. Applying to the aero-structural optimization problem of aircraft wing, we can explore robuster optima satisfying the sigma level of reliability than the baseline.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.8
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• pp.1719-1726
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• 2002

Design variables and design parameters are rarely deterministic in practice. Robust optimal design takes into consideration of the uncertainties in the design variables and parameters. Robust optimization methodology with probability constraints requires a lot of system analyses fer calculating failure probability of each constraint. By introducing an envelope function to reduce the number of constraints, efficiency of robust optimization techniques can be considerably improved. Through four illustrative examples, it is shown that the number of system analyses is greatly decreased while little differences in the optimum results are observed.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.31 no.2
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• pp.55-65
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• 2023

Due to extreme urbanization, ground transportation in the city center is saturated, and problems such as the lack of expansion infrastructure and traffic congestion increase social costs. To solve this problem, a 3D mobility platform, Urban Air Mobility (UAM), has emerged as a new alternative. A vertiport is a physical space that conducts a similar role to an airport terminal. Vertiport consists of take-off and landing facilities (TLOF, Touchdown and Lift-Off area), space for boarding and disembarking from UAM aircraft (gates), taxiways, and passenger terminals. The type of vertiport (structure, number of facilities) and concept of operations are key variables that determine the number of UAM aircraft that can be accommodated per hour. In this study, a capacity calculation method was presented using an optimization technique (Deterministic Integer Linear Programming). The absolute capacity of the vertiport was calculated using an optimization technique, and a sensitivity analysis was also performed.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.1 s.232
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• pp.59-66
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• 2005

In this paper, a robust stationary Kalman filter is designed by minimizing selected performance indices so that it is less sensitive to uncertainties. The uncertainties include not only stochastic factors such as process noise and measurement noise, but also deterministic factors such as unknown initial estimation error, modeling error and sensing bias. To reduce the effect on the uncertainties, three performance indices that should be minimized are selected based on the quantitative error analysis to both the deterministic and the stochastic uncertainties. The selected indices are the size of the observer gain, the condition number of the observer matrix, and the estimation error variance. The observer gain is obtained by optimally solving the multi-objectives optimization problem that minimizes the indices. The robustness of the proposed filter is demonstrated through the comparison with the standard Kalman filter.

• Journal of the Korean Magnetics Society
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• v.24 no.5
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• pp.160-164
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• 2014

In this paper, an efficient robust design utilizing an enhanced Taguchi method is proposed to reduce cogging torque of a BLDC motor in the presence of design uncertainty. To overcome defects of the conventional Taguchi method in dealing with a generalized robust design problem, a penalty function and an optimal level searching technique are newly introduced. In order to verify the proposed method, a 5 kW, rated speed of 2,300 rpm, rated torque of 20 Nm BLDC motor for driving electric vehicles is optimized. Then, the robust design is compared with conceptual and deterministic ones in terms of the cogging torque, rated torque and torque ripple.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.1 s.173
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• pp.215-224
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• 2000

In order to reduce the variation effects of uncertainties in the engineering environments, new robust optimization method, which considers the uncertainties in design process, is proposed. Both design variables and system parameters are considered as random variables about their nominal values. To ensure the robustness of performance function, a new objective is set to minimize the variance of that function. Constraint variations are handled by introducing probability constraints. Probability constraints are solved by the advanced first order second moment (AFOSM) method based on the reliability theory. The proposed robust optimization method has an advantage that the second derivatives of the constraints are not required. The suggested method is examined by solving three examples and the results are compared with those for deterministic case and those available in literature.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.9
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• pp.1483-1492
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• 2001

A robust optimization procedure is applied to determine the design of the laminated composite plates with buckling constraints. In order to investigate the variation effect to the whole performance of a structure, both design variables and system parameters are assumed as random variables about their nominal values. The robust optimization method has advantages that the mean value and the variation of the performance function are controlled simultaneously and the second order sensitivity information is not required. Considering the information of uncertainty, robust optima for the buckling load of the laminated composite plates with cut-out is obtained. The robustness of the structures is compared to that of the deterministic optimization using scaling factors.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2000.10a
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• pp.32-39
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• 2000

Probabilistic structural design optimization, which is characterized by the so-called probabilistic. constraints which introduce permissible probability of violation, is preferred to deterministic design optimization since unpredictable inherent uncertainties and randomness in structural and environmental properties are to be taken quantitatively into account by probabilistic design optimization. In this paper, the well-known reliability index based MPFP(Most Probable Failure Point) search approach and the newly introduced target performance measure based MPTP(Minimum Performance Target Point) search approach are summarized and compared. The present comparison focuses on the number of iterations required for the estimation of probabilistic constraints and a technique for improvement which removes exhaustive iterations is presented as well. A 10 bar truss problem is examined for this.