• Corrosion Science and Technology
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• v.9 no.3
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• pp.109-115
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• 2010

Interstitial-free (IF) steels are widely used for car body material. However, a few types of streaky mark defect are commonly found on hot dip galvannealed (GA) IF steel sheets. In the present study, both the phase structure of a streaky mark defect and the microstructure of the substrate just below it were characterized by optical microscopy (OM) and scanning electron microscopy (SEM). It was found that the bright streaky mark area was composed of ${\delta}$ phase while the dark normal area was full of craters. More than half of the grains at the uppermost surface of the substrate just below the streaky mark defect are unrecrystallized grains which could result from lower finish rolling temperature during hot rolling and be kept stable during the annealing process, while almost all the grains in the normal area are equiaxed grains. In order to confirm the effect of the unrecrystallized grains on the coating morphology, hot dip galvannealing simulation experiments were carried out in IWATANI HDPS. It is proved that the unrecrystallized grains accelerate the Fe-Zn reaction rate during galvannealing and result in a flatter coating surface and an even coating thickness. Finally, a formation mechanism of the streaky mark defect on the hot dip galvannealed IF steel sheet was discussed.

• Structural Engineering and Mechanics
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• v.66 no.2
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• pp.173-183
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• 2018

This paper proposes a developed optimization model for steel frames with semi-rigid beam-to-column connections and fixed bases using teaching-learning-based optimization (TLBO) and genetic algorithm (GA) techniques. This method uses rotational deformations of frame members ends as an optimization variable to simultaneously obtain the optimum cross-sections and the most suitable beam-to-column connection type. The total cost of members plus connections cost of the frame are minimized. Frye and Morris (1975) polynomial model is used for modeling nonlinearity of semi-rigid connections, and the $P-{\Delta}$ effect and geometric nonlinearity are considered through a stepped analysis process. The stress and displacement constraints of AISC-LRFD (2016) specifications, along with size fitting constraints, are considered in the design procedure. The developed model is applied to three benchmark steel frames, and the results are compared with previous literature results. The comparisons show that developed model using both LTBO and GA achieves better results than previous approaches in the literature.

• Journal of Power System Engineering
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• v.17 no.4
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• pp.115-119
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• 2013

The resistance spot welding of high strength steel degrades the weldability because of its high strength with rich chemical composition and coating layer to protect from corrosion. And the weld Expulsion is prone to occur and severely affect the nugget guality when the initial gap between automatic borrowing galvanied steel sheets(SGARC35) and Zn-coateel trip steels(GA580TRIP and GA980 TRIP) exist in resistance spot welding(RSW). RSW is one of the most popular welding processes used to join sheet metals. but weld guality sometimes do creases due to welding condition. in this paper to verity tue weldability using spot welding with the hemispherically concaved electrode, tensile shear strength and cross-tensile strength were measured by a universal test machine. in addition, the nugget size on cross-sectional area of the weld was observed by optical and electron microscopy. As a result, the nugget size of this specimen is increased with increasing welding current and Max load of tensile-shear strength is increased with welding current is increasing.

• Structural Engineering and Mechanics
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• v.83 no.5
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• pp.671-680
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• 2022

The shear capacity of beams is an essential parameter in designing beams carrying shear loads. Precise estimation of the ultimate shear capacity typically requires comprehensive calculation methods. For steel fiber reinforced concrete (SFRC) beams, traditional design methods may not accurately predict the interaction between different parameters affecting ultimate shear capacity. In this study, artificial neural network (ANN) modeling was utilized to predict the ultimate shear capacity of SFRC beams using ten input parameters. The results demonstrated that the ANN with 30 neurons had the best performance based on the values of root mean square error (RMSE) and coefficient of determination (R²) compared to other ANN models with different neurons. Analysis of the ANN model has shown that the clear shear span to depth ratio significantly affects the predicted ultimate shear capacity, followed by the reinforcement steel tensile strength and steel fiber tensile strength. Moreover, a Genetic Algorithm (GA) was used to optimize the ANN model's input parameters, resulting in the least cost for the SFRC beams. Results have shown that SFRC beams' cost increased with the clear span to depth ratio. Increasing the clear span to depth ratio has increased the depth, height, steel, and fiber ratio needed to support the SFRC beams against shear failures. This study approach is considered among the earliest in the field of SFRC.

• Proceedings of the Korean Institute of Surface Engineering Conference
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• 2003.10a
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• pp.139-139
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• 2003

• Proceedings of the Korean Institute of Surface Engineering Conference
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• 1998.11a
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• pp.1-1
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• 1998

• Journal of the Korea institute for structural maintenance and inspection
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• v.9 no.1
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• pp.259-269
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• 2005

This paper describes the application of genetic algorithm for the discrete optimum design of reinforced concrete continuous beams. The objective is to minimize the total cost of reinforced concrete beams including the costs of concrete, form work, main reinforcement and stirrup. The flexural and shear strength, deflection, crack, spacing of reinforcement, concrete cover, upper-lower bounds on main reinforcement, beam width-depth ratio and anchorage for main reinforcement are considered as the constraints. The width and effective depth of beam and steel area are taken as design variables, and those are selected among the discrete design space which is composed with dimensions and steel area being used from in practice. Optimum result obtained from GA is compared with other literature to verify the validity of GA. To show the applicability and efficiency of GA, it is applied to three and five span reinforced concrete beams satisfying with the Korean standard specifications.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.16 no.4
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• pp.407-417
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• 2003

Enen though several GA-based optimization algorithms have been successfully applied to complex optimization problems in various engineering fields, GA-based optimization methods are computationally too expensive for practical use in the field of structural optimization, particularly for large- scale problems. Furthermore, a successful implementation of GA-based optimization algorithm requires a cumbersome and trial-and-error routine related to setting of parameters dependent on a optimization problem. Therefore, to overcome these disadvantages, a high-performance GA is developed in the form of distributed hybrid genetic algorithm for structural optimization on a cluster of personal computers. The distributed hybrid genetic algorithm proposed in this paper consist of a simple GA running on a master computer and multiple μ-GAs running on slave computers. The algorithm is implemented on a PC cluster and applied to the minimum weight design of steel structures. The results show that the computational time required for structural optimization process can be drastically reduced and the dependency on the parameters can be avoided.

• Structural Engineering and Mechanics
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• v.11 no.2
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• pp.151-170
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• 2001

With increasing competition, the engineering industry is in need of optimization of designs that would lead to minimum cost or weight. Recent developments in Genetic Algorithms (GAs) makes it possible to model and obtain optimal solutions in structural design that can be put to use in industry. The main objective of this paper is to illustrate typical applications of GAs to practical design of structural systems such as steel trusses, towers, bridges, reinforced concrete frames, bridge decks, shells and layout planning of buildings. Hence, instead of details of GA process, which can be found in the reported literature, attention is focussed on the description of the various applications and the practical aspects that are considered in Genetic Modeling. The paper highlights scope and future directions for wider applications of GA based methodologies for optimal design in practice.

• Journal of Surface Science and Engineering
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• v.34 no.4
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• pp.297-302
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• 2001

As GA (Hot dip galvannealed steel sheet) has good corrosion resistance, weldability and paintability as well as excellent formability, it's demand is rapidly increasing for automotive panels. The GA coated layers are composed of several kinds of brittle Fe-Zn Metallic compounds which are susceptible to powdering during the press forming, however, very careful controls of manufacturing conditions such as galvannealing heat-treatment or bath composition are essential to meet with the required quality of automotive use. In this study the required characteristics of automotive panel are practically surveyed in detail and the appropriate manufacturing conditions of galvannealing or bath composition have experimentally investigated by using the various analyzing and simulating equipments. The result in this study indicated that the corrosion resistance at the cut edges was improved by increasing of coating weight and decreasing of thickness of sheet steels.