• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.1D
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• pp.45-55
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• 2008

The objective of this research was to develop a VEPCD (ViscoElastoPlastic Continuum Damage) Model which is used to predict the behavior of asphalt concrete under various loading and temperature conditions. This paper presents the VEPCD model formulated in a tension mode and its validation using four hot mix asphalt (HMA) mixtures: dense-graded HMA, SBS, CR-TB, and Terpolymer. Modelling approaches consist of two components: the ViscoElastic Continuum Damage (VECD) mechanics and the ViscoPlastic (VP) theory. The VECD model was to describe the time-dependent behavior of HMA with growing damage. The irrecoverable (whether time-dependent or independent) strain has been described by the VP model. Based on the strain decomposition principle, these two models are integrated to form the VEPCD model. For validating the VEPCD model, two types of laboratory tests were performed: 1) a constant crosshead strain rate tension test, 2) a fatigue test with randomly selected load levels and frequencies.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.3A
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• pp.209-216
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• 2009

The structural stability as well as economical efficiency of the wind sensitive structures are strongly dependant on accurate evaluation of the design wind speed. Present study demonstrates a useful wind data obtained at the wind monitoring tower in the Kwangyang Suspension Bridge site. Moreover the Measure-Correlate-Predict (MCP) method has been applied to estimate the long-term wind data at the bridge site based on the wind data at the local weather station. The measured data indicate that the turbulent intensities and roughness exponents are strongly affected by the wind direction and surrounding topography. The new design wind speed based on MCP method is 20m/s lower than that at the original estimation, and the resulting design wind load is only 36% of the old prediction. The field measurement of wind data is recommended to ensure the economical and secure design of the wind sensitive structures because the measured wind data reveal much different from the estimated one due to local topography.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3A
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• pp.429-437
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• 2006

Existing basic mechanical models which are methods characterizing viscoelastic materials were first reviewed to account for viscoelastic behavior of the asphalt pavement structure in this paper. A viscoelastic mechanical model considering a single load of vehicles subsequently was suggested and an equation that indicates the time-dependant behavior of asphalt pavements was derived from the proposed model. Non-destructive tests using falling weight deflectometer(FWD) were performed for a test section to estimate the application of the model. Both deflections and strains procured by the equation were compared to testing results according to loading history. By observing field measurements and theoretical evaluations, if two results are compared by the features of deflection according to time history, it could be concluded that the proposed model is expected to be suitable for prediction of the behavior of asphalt pavements because there is hardly difference between field data and calculated data.

• Journal of the Korean Wood Science and Technology
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• v.51 no.4
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• pp.239-252
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• 2023

In this study, bending and compression strength tests were performed to investigate effect of composition of layer layout of Larix cross-laminated timber (CLT) on mechanical properties. The Larix CLT consists of five laminae, and specimens were classified into four types according to grade and composition of layer. The layer's layout were composited as follows 1) cross-laminating layers in major and minor direction (Type A), and 2) cross-laminating external layer in major direction and internal layer applied grade of layer in minor direction (Type B). E12 and E16 were used as grades of lamina for major direction layer of Type A and external layer of Type B according to KS F 3020. In results of the bending test of CLT using same grade layer according to layer composition, the modulus of elasticity (MOE) of Type B was higher than Type A. In case of prediction of bending MOE of Larix CLT, the experimental MOE was higher than 1.00 to 1.09 times for Shear analogy method and 1.14 to 1.25 times for Gamma method. Therefore, it is recommended to predict the bending MOE for Larix CLT by shear analogy method. Compression strength of CLT in accordance with layer composition was measured to be 2% and 9% higher for Type A using E12 and E16 layers than Type B, respectively. In failure mode of Type A, progress direction of failure generated under compression load was confirmed to transfer from major layer to minor layer by rolling shear or bonding line failure due to the middle lamina in major direction.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.334-334
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• 2022

Improvement of old-fashioned rain gauge systems for automatic, timely, continuous, and accurate precipitation observation is highly essential for weather/climate prediction and natural hazards early warning, since the occurrence frequency and intensity of heavy and extreme precipitation events (especially floods) are recently getting more increase and severe worldwide due to climate change. Although rain gauge accuracy of 0.1 mm is recommended by the World Meteorological Organization (WMO), the traditional rain gauges in both weighting and tipping bucket types are often unable to meet that demand due to several existing technical limitations together with higher production and maintenance costs. Therefore, we aim to introduce a newly developed and cost-effective hybrid rain gauge system at 0.1 mm accuracy that combines advantages of weighting and tipping bucket types for continuous, automatic, and accurate precipitation observation, where the errors from long-term load cells and external environmental sources (e.g., winds) can be removed via an automatic drainage system and artificial intelligence-based data quality control procedure. Our rain gauge system consists of an instrument unit for measuring precipitation, a communication unit for transmitting and receiving measured precipitation signals, and a database unit for storing, processing, and analyzing precipitation data. This newly developed rain gauge was designed according to the weather instrument criteria, where precipitation amounts filled into the tipping bucket are measured considering the receiver's diameter, the maximum measurement of precipitation, drainage time, and the conductivity marking. Moreover, it is also designed to transmit the measured precipitation data stored in the PCB through RS232, RS485, and TCP/IP, together with connecting to the data logger to enable data collection and analysis based on user needs. Preliminary results from a comparison with an existing 1.0-mm tipping bucket rain gauge indicated that our developed rain gauge has an excellent performance in continuous precipitation observation with higher measurement accuracy, more correct precipitation days observed (120 days), and a lower error of roughly 27 mm occurred during the measurement period.

• Steel and Composite Structures
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• v.49 no.5
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• pp.533-546
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• 2023

Cost-effective solutions provided by composite construction are gaining popularity which, in turn, promotes the appearance on the market of new types of composite sections that allow not only to take advantage of the synergy of steel and concrete working together at room temperature, but also to improve their behaviour at high temperatures. When combined with high performance materials, significant load-bearing capacities can be achieved even with reduced cross-sectional dimensions. Steel-reinforced concrete-filled steel tubular (SR-CFST) columns are one of these innovative composite sections, where an open steel profile is embedded into a CFST section. Besides the renowned benefits of these typologies at room temperature, the fire protection offered by the surrounding concrete to the inner steel profile, gives them an enhanced fire performance which delays its loss of mechanical capacity in a fire scenario. The experimental evidence on the fire behaviour of SR-CFST columns is still scarce, particularly when combined with high performance materials. However, it is being much needed for the development of specific design provisions that consider the use of the inner steel profile in CFST columns. In this work, a new experimental program on the thermo-mechanical behaviour of SR-CFST columns is presented to extend the available experimental database. Ten SR-CFST stub columns, with circular and square geometries, combining high strength steel and concrete were tested. It was seen that the circular specimens reached higher failure times than the square columns, with the failure time increasing both when high strength steel was used at the embedded steel profile and high strength concrete was used as infill. Finally, different proposals for the reduction coefficients of high performance materials were assessed in the prediction of the cross-sectional fire resistance of the SR-CFST columns.

• Steel and Composite Structures
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• v.51 no.4
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• pp.441-456
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• 2024

This paper aims to develop Machine Learning (ML) algorithms to predict the buckling resistance of cold-formed steel (CFS) channels with restrained flanges, widely used in typical CFS sheathed wall panels, and provide practical design tools for engineers. The effects of cross-sectional restraints were first evaluated on the elastic buckling behaviour of CFS channels subjected to pure axial compressive load or bending moment. Feedforward multi-layer Artificial Neural Networks (ANNs) were then trained on different datasets comprising CFS channels with various dimensions and properties, plate thicknesses, and restraining conditions on one or two flanges, while the elastic distortional buckling resistance of the elements were determined according to the Finite Strip Method (FSM). To develop less biased networks and ensure that every observation from the original dataset has the chance of appearing in the training and test set, a K-fold cross-validation technique was implemented. In addition, the hyperparameters of the ANNs were tuned using a grid search technique to provide ANNs with optimum performances. The results demonstrated that the trained ANNs were able to predict the elastic distortional buckling resistance of CFS flange-restrained elements with an average accuracy of 99% in terms of coefficient of determination. The developed models were then used to propose a simple ANN-based design formula for the prediction of the elastic distortional buckling stress of CFS flange-restrained elements. Finally, the proposed formula was further evaluated on a separate set of unseen data to ensure its accuracy for practical applications.

• Journal of the Earthquake Engineering Society of Korea
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• v.28 no.4
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• pp.193-203
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• 2024

Existing reinforced concrete (RC) building frames constructed before the seismic design was applied have seismically deficient structural details, and buildings with such structural details show brittle behavior that is destroyed early due to low shear performance. Various reinforcement systems, such as fiber-reinforced polymer (FRP) jacketing systems, are being studied to reinforce the seismically deficient RC frames. Due to the step-by-step modeling and interpretation process, existing seismic performance assessment and reinforcement design of buildings consume an enormous amount of workforce and time. Various machine learning (ML) models were developed using input and output datasets for seismic loads and reinforcement details built through the finite element (FE) model developed in previous studies to overcome these shortcomings. To assess the performance of the seismic performance prediction models developed in this study, the mean squared error (MSE), R-square (R²), and residual of each model were compared. Overall, the applied ML was found to rapidly and effectively predict the seismic performance of buildings according to changes in load and reinforcement details without overfitting. In addition, the best-fit model for each seismic performance class was selected by analyzing the performance by class of the ML models.

• Computers and Concrete
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• v.34 no.1
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• pp.93-122
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• 2024

One well-known reason for using Fiber Reinforced Polymer (FRP) composites is to improve concrete strength and strain capacity via external confinement. Hence, various studies have been undertaken to offer a good illustration of the response of FRP-wrapped concrete for practical design intents. However, in such studies, the strength and strain of the confined concrete were predicted using regression analysis based on a limited number of test data. This study presents an approach based on artificial neural networks (ANNs) to develop models to predict the strength and strain at maximum stress enhancement of circular concrete cross-sections confined with different FRP types (Carbone, Glass, Aramid). To achieve this goal, a large test database comprising 493 axial compression experiments on FRP-confined concrete samples was compiled based on an extensive review of the published literature and used to validate the predicted artificial intelligence techniques. The ANN approach is currently thought to be the preferred learning technique because of its strong prediction effectiveness, interpretability, adaptability, and generalization. The accuracy of the developed ANN model for predicting the behavior of FRP-confined concrete is commensurate with the experimental database compiled from published literature. Statistical measures values, which indicate a better fit, were observed in all of the ANN models. Therefore, compared to existing models, it should be highlighted that the newly developed models based on FRP type are remarkably accurate.

• Journal of the Korea institute for structural maintenance and inspection
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• v.12 no.3
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• pp.119-126
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• 2008

In 1980 and 1990's most of residential buildings were constructed with relatively low strength concrete of 18 MPa. And, columns were designed considering only vertical loads. In this study, compressive strength tests for low strength RC columns retrofitted by carbon fiber sheets were carried out. Carbon fiber sheet provides constructability and high tensile strength as well as good corrosion resistance characteristics. A pair of carbon sheets were wrapped with ${\pm}60^{\circ}$ angle with respect to longitudinal direction of RC column to increase structural capacity against axial and lateral load simultaneously. Strength and strain patterns and failure modes of specimens were analyzed and prediction equation of increased compressive strength of RC column confined by carbon fiber sheet was proposed based on regression analysis.