• Geomechanics and Engineering
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• v.20 no.3
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• pp.191-205
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• 2020

Soil shear strength parameters play a remarkable role in designing geotechnical structures such as retaining wall and dam. This study puts an effort to propose two accurate and practical predictive models of soil shear strength parameters via hybrid artificial neural network (ANN)-based models namely genetic algorithm (GA)-ANN and particle swarm optimization (PSO)-ANN. To reach the aim of this study, a series of consolidated undrained Triaxial tests were conducted to survey inherent strength increase due to addition of polypropylene fibers to sandy soil. Fiber material with different lengths and percentages were considered to be mixed with sandy soil to evaluate cohesion (as one of shear strength parameter) values. The obtained results from laboratory tests showed that fiber percentage, fiber length, deviator stress and pore water pressure have a significant impact on cohesion values and due to that, these parameters were selected as model inputs. Many GA-ANN and PSO-ANN models were constructed based on the most effective parameters of these models. Based on the simulation results and the computed indices' values, it is observed that the developed GA-ANN model with training and testing coefficient of determination values of 0.957 and 0.950, respectively, performs better than the proposed PSO-ANN model giving coefficient of determination values of 0.938 and 0.943 for training and testing sets, respectively. Therefore, GA-ANN can provide a new applicable model to effectively predict cohesion of fiber-reinforced sandy soil.

• Journal of Wetlands Research
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• v.7 no.3
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• pp.57-66
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• 2005

This paper is to present the determination of the optimal loss rate parameters and unit hydrographs from the observed single rainfall-runoff event using optimization model. The linear program models has been formulated to derive the optimal unit hydrographs and loss rate parameters for the site of the Su-Jik Bridge in the HwangGuJichen River; one minimizes the summation of the absolute residual between predicted and observed runoff ordinates. In the perturbation stage of parameters the trial and error method has been adopted to determine the loss rate parameters for Kostiakov's, Philip's, Horton's, and Green-Ampt's equation. The unique unit hydrograph ordinates for a given rainfall-runoff event is exclusively obtained with ${\Phi}$ index, but unit hydrograph ordinates depend upon the parameters for each loss rate equations. In this paper the single rainfall-runoff event observed from the sample watershed is considered to test the proposed method. The optimal unit hydrograph obtained by the optimization model has smaller deviations than the ones by the conventional method.

• Nuclear Engineering and Technology
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• v.53 no.7
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• pp.2377-2386
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• 2021

To model the atmospheric dispersion of radionuclides released from nuclear accident is very important for nuclear emergency. But the uncertainty of model parameters, such as source term and meteorological data, may significantly affect the prediction accuracy. Data assimilation (DA) is usually used to improve the model prediction with the measurements. The paper proposed a parameter bias transformation method combined with Lagrangian puff model to perform DA. The method uses the transformation of coordinates to approximate the effect of parameters bias. The uncertainty of four model parameters is considered in the paper: release rate, wind speed, wind direction and plume height. And particle swarm optimization is used for searching the optimal parameters. Twin experiment and Kincaid experiment are used to evaluate the performance of the proposed method. The results show that the proposed method can effectively increase the reliability of model prediction and estimate the parameters. It has the advantage of clear concept and simple calculation. It will be useful for improving the result of atmospheric dispersion model at the early stage of nuclear emergency.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2005.04a
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• pp.55-62
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• 2005

In this paper, a CAB/CAE integrated optimal design system is developed, in which design and analysis process is automated using CAD/CAE softwares, for a complicated model for which parametric modeling provided by CAD software is not possible. CAD modeling process is automated by using UG/OPEN API function and UG/Knowledge Fusion provided by Unigraphics. The generated model is transferred to the analysis code ANSYS in parasolid format. Visual DOC software is used for optimization. The system is developed for PLS(Plasma Lighting System), which is a next generation illumination system that is used to illuminate stadium or outdoor advertizing panel. The PLS system consists of more then 20 components, which requires a lot of human efforts in modeling and analysis. The analysis for PLS includes static load, wind load and impact load analysis. As a result of analysis, it is found that the most critical component is a tilt assembly, which links lower & upper body assembly. For more reliable analysis, experiment is conducted using MTS and compared with the Finite element analysis result. The objective in the optimization is to minimize the material volume under allowable stresses. The design variables are three parameters in the tilt assembly that are chosen to be the most sensitive in stress values of twelve parameters. Gradient based method and RSM(Response Surface Method) are used for the algorithm and the results are compared. As a result of optimization, the maximum stress is reduced by 57%.

• Journal of Korean Society for Quality Management
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• v.45 no.2
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• pp.191-207
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• 2017

Purpose: To develop an interactive version of the conventional process capability-based approach, called 'Interactive Process Capability-Based Approach (IPCA)' in multi-response surface optimization to obtain a satisfactory compromise which incorporates a decision maker(DM)'s preference information precisely. Methods: The proposed IPCA consists of 4 steps. Step 1 is to obtain the estimated process capability indices and initialize the parameters. Step 2 is to maximize the overall process capability index. Step 3 is to evaluate the optimization results. If all the responses are satisfactory, the procedure stops with the most preferred compromise solution. Otherwise, it moves to Step 4. Step 4 is to adjust the preference parameters. The adjustment can be made in two modes: relaxation and tightening. The relaxation is to make the importance of one of the satisfactory responses lower, which is implemented by decreasing its weight. The tightening is to make the importance of one of the unsatisfactory responses higher, which is implemented by increasing its weight. Then, the procedure goes back to Step 2. If there is no response to be adjusted, it stops with the unsatisfactory compromise solution. Results: The proposed IPCA was illustrated through a multi-response surface problem, colloidal gas aphrons problem. The illustration shows that it can generate a satisfactory compromise through an interactive procedure which enables the DM to provide his or her preference information conveniently. Conclusion: The proposed IPCA has two major advantages. One is to obtain a satisfactory compromise which is faithful to the DM preference structure. The other is to make the DM's participation in the interactive procedure easier by using the process capability index in judging satisfaction/unsatisfaction. The process capability index is very familiar with quality practitioners as well as indicates the process performance levels numerically.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.395-402
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• 2016

This paper presents a design methodology for improving the energy efficiency by considering the inertial properties of serial manipulators. This method employed is to put the inertia matrix, which has a critical effect on the equation of motion, into the constraints of the optimization problem. Through the optimization process, we propose a design algorithm that can double-check whether the optimized parameters satisfy the required performance or not by using an auxiliary index associated with the inertia and energy. Using this design algorithm, we were able to improve the energy efficiency by minimizing the torque. We applied this method to a 3 degrees of freedom serial manipulator and simulated it.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.30 no.1
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• pp.39-46
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• 2017

In this paper, a model updating procedure based on the response surface method combined with the multi-objective optimization was proposed and applied for updating of the FE models representing a low-rise reinforced concrete building before and after the seismic retrofit. The dynamic properties to be matched were obtained from vibration tests using a small shaker system. By varying the structural parameters according to the central composite design, analysis results from the initial FE model using a commercial software were collected and used to produce two regression functions each of which representing the errors in the natural frequencies and mode shapes. The two functions were used as the objective functions for multi-objective optimization. Final solution was determined by examining the Pareto solutions with one iteration. The parameters representing the stiffnesses of existing concrete, masonry, connection stiffness in expansion joint, new concrete, retrofitted members with steel section jacketing were selected and identified.

• Journal of Korean Association for Spatial Structures
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• v.5 no.3 s.17
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• pp.83-89
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• 2005

This paper provides the results of the investigation on the optimum topology of simply supported deep beam structures with multi-point load cases. In this study, the strain energy to be minimized is considered as the objective function and the initial volume of structures is used as the constraint function. The resizing algorithm based on the optimality criteria is adopted to update the hole size existing inside the material. In this study, the sensitivities of topology optimization parameters to the optimum topology of the deep bean structures is investigated and also the effect of filtering process on the optimum topology is thoroughly tested. From numerical tests, the optimum topology of the deep beam is closely related with the optimization parameters used in the iteration and the filtering process play important role in order to find the optimum topology of the deep beam.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.16 no.5
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• pp.91-98
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• 2017

This study was conducted to optimize the spring constant and the damping coefficient, which are design parameters of the tractor cabin suspension system, to minimize the ride vibration. A 3D tractor MBD (multi-body dynamics) model with a cabin suspension system was developed using a dynamic analysis program (Recurdyn). Using the developed model and optimization algorithm, the spring constant and the damping coefficient, which are the design parameters of the cabin suspension for the tractor, was were optimized so thatto minimize the maximum overshoot for the vertical displacement of the cabin was minimized. The percent maximum overshoot of the tractor cabin was simulated for the 13 initial models, which were obtained using the ISCD-II method, and for the 3 additional SAO models presented in the optimization algorithm software. The model that represents with the smallest percent maximum overshoot among the 16 models was selected as the optimized model. The percent maximum overshoot of the optimized model was about approximately 5% lower than that of the existing model.

• Journal of the Korean Institute of Gas
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• v.24 no.1
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• pp.66-75
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• 2020

Drilling operation is the most important and costly essential work in oil and gas exploration and development. Therefore, the studies about rate of penetration have been carried out continuously to improve drilling efficiency. In recent years, data-driven models have been developed by various researchers to overcome disadvantages of traditional mathematical models. For the data-driven models, selecting proper algorithms and parameters is very important. In addition, data-driven models should be retrained in real-time during continuous drilling operations in order to improve the model performance. In this paper, the latest studies are investigated to provide information about algorithms, drilling parameters and model retraining intervals that used in drilling optimization.