• Journal of the Earthquake Engineering Society of Korea
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• v.27 no.6
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• pp.275-281
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• 2023

The demand for high-strength steel is rising due to its economic efficiency. Low-cycle fatigue (LCF) tests have been conducted to investigate the nonlinear behaviors of high-strength steel. Accurate material models must be used to obtain reliable results on seismic performance evaluation using numerical analyses. This study uses the combined hardening model to simulate the LCF behavior of high-strength steel. However, it is challenging and complex to determine material model parameters for specific high-strength steel because a highly nonlinear equation is used in the model, and several parameters need to be resolved. This study used the particle swarm algorithm (PSO) to determine the model parameters based on the LCF test data of HSA 650 steel. It is shown that the model with parameter values selected from the PSO accurately simulates the measured LCF curves.

• Journal of Information Technology Services
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• v.10 no.3
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• pp.113-126
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• 2011

The smelting and the continuous casting of steel are important processes that determine the quality of steel products. Especially most of quality defects occur during solidification of the steel continuous casting process. Although quality control techniques such as six sigma, SQC, and TQM can be applied to the continuous casting process for improving quality of steel products, these techniques don't provide real-time analysis to identify the causes of defect occurrence. To solve problems, we have developed a detection model using decision tree which identified abnormal transactions to have a coarse grain structure. And we have compared the proposed model with models using neural network and logistic regression. Experiments on steel data showed that the performance of the proposed model was higher than those of neural network model and logistic regression model. Thus, we expect that the suggested model will be helpful to control the quality of steel products in real-time in the continuous casting process.

• Structural Engineering and Mechanics
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• v.83 no.1
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• pp.19-29
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• 2022

The angle steel frame confined concrete columns (ASFCs) are an emerging form of hybrid columns, which comprise an inner angle steel frame and a concrete column. The inner angle steel frame can provide axial bearing capacity and well confining effect for composite columns. This paper presents the experimental and theoretical studies on the seismic behaviour of ASFCs. The experimental study of the 6 test specimens is presented, based on the previous study of the authors. The theoretical study includes two parts. One part establishes the section analysis model, and it uses to analyze section axial force-moment-curvature. Another part establishes the section moment-curvature hysteresis model. The test and analysis results show that the axial compression ratio and the assembling of steel slabs influence the local buckling of the angle steel. The three factors (axial compression ratio, content of angle steel and confining effect) have important effects on the seismic behaviour of ASFCs. And the theoretical model can provide reasonably accurate predictions and apply in section analysis of ASFCs.

• Steel and Composite Structures
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• v.30 no.4
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• pp.337-350
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• 2019

The objective of this paper is to investigate the confinement coefficient of concrete-filled square stainless steel tubular (CFSSST) stub columns under axial loading. A fine finite 3D solid element model was established, which utilized a constitutive model of stainless steel considering the strain-hardening characteristics and a triaxial plastic-damage constitutive model of concrete with features of the parameter certainty under axial compression. The finite element analysis results revealed that the increased ultimate bearing capacity of CFSSST stub columns compared with their carbon steel counterparts was mainly due to that the composite action of CFSSST stub columns is stronger than that of carbon steel counterparts. A further parametric study was carried out based on the verified model, and it was found that the stress contribution of the stainless steel tube is higher than the carbon steel tube. The stress nephogram was simplified reasonably in accordance with the limit state of core concrete and a theoretical formula was proposed to estimate the ultimate bearing capacity of square CFSSST stub columns using superposition method. The predicted results showed satisfactory agreement with both the experimental and FE results. Finally, the comparisons of the experimental and predicted results using the proposed formula and the existing codes were illustrated.

• Corrosion Science and Technology
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• v.23 no.3
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• pp.228-234
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• 2024

Zinc wiping is a phenomenon used to control zinc-coating thickness on steel substrate during hot dip galvanizing by equipment called air knife. Uniformity of zinc coating weight in length and width profile along with surface quality are most critical quality parameters of galvanized steel. Deviation from tolerance level of coating thickness causes issues like overcoating (excess consumption of costly zinc) or undercoating leading to rejections due to non-compliance of customer requirement. Main contributor of deviation from target coating weight is dynamic change in air knives equipment setup when thickness, width, and type of substrate changes. Additionally, cold coating measurement gauge measure coating weight after solidification but are installed down the line from air knife resulting in delayed feedback. This study presents a coating weight control model (Galvantage) predicting critical air knife parameters air pressure, knife distance from strip and line speed for coating control. A reverse engineering approach is adopted to design a predictive, prescriptive, and descriptive model recommending air knife setups that estimate air knife distance and expected coating weight in real time. Implementation of this model eliminates feedback lag experienced due to location of coating gauge and achieving setup without trial-error by operator.

• Steel and Composite Structures
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• v.15 no.3
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• pp.281-298
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• 2013

Composite special moment frame is one of the systems that are utilized in areas with low to high seismicity to deal with earthquake forces. Composite moment frames are composed of reinforced concrete columns (RC) and steel beams (S); therefore, the connection region is a combination of steel and concrete materials. In current study, a three dimensional finite element model of composite connections is developed. These connections are used in special composite moment frame, between reinforced concrete columns and steel beams (RCS). Finite element model is discussed as a most reliable and low cost method versus experimental procedures. Based on a tested connection model by Cheng and Chen (2005), the finite element model has been developed under cyclic loading and is verified with experimental results. A good agreement between finite element model and experimental results was observed. The connection configuration contains Face Bearing Plates (FBPs), Steel Band Plates (SBPs) enveloping around the RC column just above and below the steel beam. Longitudinal column bars pass through the connection with square ties around them. The finite element model represented a stable response up to the first cycles equal to 4.0% drift, with moderately pinched hysteresis loops and then showed a significant buckling in upper flange of beam, as the in test model.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2003.04a
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• pp.313-320
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• 2003

The efficient and well organized database is essential for the management of information in every industrial field. In this study, a practical and effective database which can handle 3-D information of steel bridges is built on the basis of a STEP-based data model. The data model of steel bridge information is classified into geometric and non-geometric part and the design information is represented by linking geometric information and life cycle supported non-geometric information. Especially, the shape information is represented by boundary representation method, which is one of the representative methods of solid model information. In this study, ISO/STEP(STandard for the Exchange of product model data) AP203(configuration controlled design) EXPRESS schema is used to represent the shape information of steel bridge. The syntax of EXPRESS schema of developed data model is verified by NIST Expresso - is a tool for parsing and compiling EXPRESS schema. Also, this study verifies the conformance of the data model by applying to the real data of Hannam bridge. Therefore, the constructed database using STEP-based data model of steel bridges can be used effectively in the concurrent engineering point of view with transferring and sharing steel bridge information.

• International journal of steel structures
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• v.18 no.5
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• pp.1560-1576
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• 2018

With the aim to provide an efficient platform for the elastic-plastic analysis of steel structures, reinforced concrete (RC) structures and steel-concrete composite structures, a program iFiberLUT based on the fiber model was developed within the framework of ABAQUS. This program contains an ABAQUS Fiber Generator which can automatically divide the beam and column cross sections into fiber sections, and a material library which includes several concrete and steel uniaxial material models. The range of applications of iFiberLUT is introduced and its feasibility is verified through previously reported test data of individual structural members as well as planar steel frames, RC frames and composite frames subjected to various loadings. The simulation results indicate that the developed program is able to achieve high calculation accuracy and favorable convergence within a wide range of applications.

• Steel and Composite Structures
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• v.44 no.3
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• pp.309-324
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• 2022

The post-fire elastic stiffness and performance of concrete-filled steel tube (CFST) columns containing recycled aggregate concrete (RAC) has rarely been addressed, particularly in terms of material properties. This study was conducted with the aim of assessing the modulus of elasticity of recycled aggregate concrete-filled steel tube (RACFST) stub columns following thermal loading. The test data were employed to model and assess the elastic modulus of circular RACFST stub columns subjected to axial loading after exposure to elevated temperatures. The length/diameter ratio of the specimens was less than three to prevent the sensitivity of overall buckling for the stub columns. The gene expression programming (GEP) method was employed for the model development. The GEP model was derived based on a comprehensive experimental database of heated and non-heated RACFST stub columns that have been properly gathered from the open literature. In this study, by using specifications of 149 specimens, the variables were the steel section ratio, applied temperature, yielding strength of steel, compressive strength of plain concrete, and elastic modulus of steel tube and concrete core (RAC). Moreover, parametric and sensitivity analyses were also performed to determine the contribution of different effective parameters to the post-fire elastic modulus. Additionally, comparisons and verification of the effectiveness of the proposed model were made between the values obtained from the GEP model and the formulas proposed by different researchers. Through the analyses and comparisons of the developed model against formulas available in the literature, the acceptable accuracy of the model for predicting the post-fire modulus of elasticity of circular RACFST stub columns was seen.

• Steel and Composite Structures
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• v.39 no.3
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• pp.319-335
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• 2021

This study proposes a new and highly-accurate artificial intelligence model, namely ANN-IP, which combines an interior-point (IP) algorithm and artificial neural network (ANN), to improve the axial compression capacity prediction of elliptical concrete-filled steel tubular (CFST) columns. For this purpose, 145 tests of elliptical CFST columns extracted from the literature are used to develop the ANN-IP model. In this regard, axial compression capacity is considered as a function of the column length, the major axis diameter, the minor axis diameter, the thickness of the steel tube, the yield strength of the steel tube, and the compressive strength of concrete. The performance of the ANN-IP model is compared with the ANN-LM model, which uses the robust Levenberg-Marquardt (LM) algorithm to train the ANN model. The comparative results show that the ANN-IP model obtains more magnificent precision (R² = 0.983, RMSE = 59.963 kN, a20 - index = 0.979) than the ANN-LM model (R² = 0.938, RMSE = 116.634 kN, a20 - index = 0.890). Finally, a new Graphical User Interface (GUI) tool is developed to use the ANN-IP model for the practical design. In conclusion, this study reveals that the proposed ANN-IP model can properly predict the axial compression capacity of elliptical CFST columns and eliminate the need for conducting costly experiments to some extent.