• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.5
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• pp.10-19
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• 2022

Irregularity in structural shape is a ubiquitous phenomenon. Structural hazards evoked from irregularity need to be checked against extreme lateral loadings. Structures containing four distinct types of irregularities in terms of continuity and discontinuity in upper half-length and all story levels along with O-shape are investigated. The structures were analyzed numerically and different seismic responses such as displacements, bending moment, axial forces, torsions, story drift, etc. were scrutinized. The seismic and wind load analysis was conducted for ACI 318-11 conditions. Results show that buildings having discontinuous beams on the upper half exhibit better resilience. It is also concluded that O-shaped building structures provide better resistance to overturning, making this shape relatively safe.

• Earthquakes and Structures
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• v.21 no.5
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• pp.531-549
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• 2021

Analytical models were developed and seismic behaviors were analyzed for a three-story stone pagoda at the Cheollyongsa temple site, which was damaged by the Gyeongju earthquake of 2016. Both finite and discrete element modeling were used and the analysis results were compared to the actual earthquake damage. Vulnerable parts of stone pagoda structure were identified and their seismic behaviors via sliding, rocking, and risk analyses were verified. In finite and discrete element analyses, the 3F main body stone was displaced uniaxially by 60 and 80 mm, respectively, similar to the actual displacement of 90 mm resulting from the earthquake. Considering various input conditions such as uniaxial excitation and soil-structure interaction, as well as seismic components and the distance from the epicenter, both models yielded reasonable and applicable results. The Gyeongju earthquake exhibited extreme short-period characteristics; thus, short-period structures such as stone pagodas were seriously damaged. In addition, we found that sliding occurred in the upper parts because the vertical load was low, but rocking predominated in the lower parts because most structural members were slender. The third-floor main body and roof stones were particularly vulnerable because some damage occurred when the sliding and rocking limits were exceeded. Risk analysis revealed that the probability of collapse was minimal at 0.1 g, but exceeded 80% at above 0.3 g. The collapse risks at an earthquake peak ground acceleration of 0.154 g at the immediate occupancy, life safety, and collapse prevention levels were 90%, 52%, and 6% respectively. When the actual damage was compared with the risk analysis, the stone pagoda retained earthquake-resistant performance at the life safety level.

• 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.

• 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.

• Geomechanics and Engineering
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• v.31 no.2
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• pp.129-147
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• 2022

The properties of soil are naturally highly variable and thus, to ensure proper safety and reliability, we need to test a large number of samples across the length and depth. In pile foundations, conducting field tests are highly expensive and the traditional empirical relations too have been proven to be poor in performance. The study proposes a state-of-art Particle Swarm Optimization (PSO) hybridized Artificial Neural Network (ANN), Extreme Learning Machine (ELM) and Adaptive Neuro Fuzzy Inference System (ANFIS); and comparative analysis of metaheuristic models (ANN-PSO, ELM-PSO, ANFIS-PSO) for prediction of bearing capacity of pile foundation trained and tested on dataset of nearly 300 dynamic pile tests from the literature. A novel ensemble model of three hybrid models is constructed to combine and enhance the predictions of the individual models effectively. The authenticity of the dataset is confirmed using descriptive statistics, correlation matrix and sensitivity analysis. Ram weight and diameter of pile are found to be most influential input parameter. The comparative analysis reveals that ANFIS-PSO is the best performing model in testing phase (R² = 0.85, RMSE = 0.01) while ELM-PSO performs best in training phase (R² = 0.88, RMSE = 0.08); while the ensemble provided overall best performance based on the rank score. The performance of ANN-PSO is least satisfactory compared to the other two models. The findings were confirmed using Taylor diagram, error matrix and uncertainty analysis. Based on the results ELM-PSO and ANFIS-PSO is proposed to be used for the prediction of bearing capacity of piles and ensemble learning method of joining the outputs of individual models should be encouraged. The study possesses the potential to assist geotechnical engineers in the design phase of civil engineering projects.

• Steel and Composite Structures
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• v.34 no.5
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• pp.743-767
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• 2020

Due to the impressive flexural performance, enhanced compressive strength and more constrained crack propagation, Fibre-reinforced concrete (FRC) have been widely employed in the construction application. Majority of experimental studies have focused on the seismic behavior of FRC columns. Based on the valid experimental data obtained from the previous studies, the current study has evaluated the seismic response and compressive strength of FRC rectangular columns while following hybrid metaheuristic techniques. Due to the non-linearity of seismic data, Adaptive neuro-fuzzy inference system (ANFIS) has been incorporated with metaheuristic algorithms. 317 different datasets from FRC column tests has been applied as one database in order to determine the most influential factor on the ultimate strengths of FRC rectangular columns subjected to the simulated seismic loading. ANFIS has been used with the incorporation of Particle Swarm Optimization (PSO) and Genetic algorithm (GA). For the analysis of the attained results, Extreme learning machine (ELM) as an authentic prediction method has been concurrently used. The variable selection procedure is to choose the most dominant parameters affecting the ultimate strengths of FRC rectangular columns subjected to simulated seismic loading. Accordingly, the results have shown that ANFIS-PSO has successfully predicted the seismic lateral load with R² = 0.857 and 0.902 for the test and train phase, respectively, nominated as the lateral load prediction estimator. On the other hand, in case of compressive strength prediction, ELM is to predict the compressive strength with R² = 0.657 and 0.862 for test and train phase, respectively. The results have shown that the seismic lateral force trend is more predictable than the compressive strength of FRC rectangular columns, in which the best results belong to the lateral force prediction. Compressive strength prediction has illustrated a significant deviation above 40 Mpa which could be related to the considerable non-linearity and possible empirical shortcomings. Finally, employing ANFIS-GA and ANFIS-PSO techniques to evaluate the seismic response of FRC are a promising reliable approach to be replaced for high cost and time-consuming experimental tests.

• Wind and Structures
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• v.27 no.1
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• pp.11-27
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• 2018

The straight-cone steel cooling tower is a novel type of structure, which has a distinct aerodynamic distribution on the internal surface of the tower cylinder compared with conventional hyperbolic concrete cooling towers. Especially in the extreme weather conditions of strong wind and heavy rain, heavy rain also has a direct impact on aerodynamic force on the internal surface and changes the turbulence effect of pulsating wind, but existing studies mainly focus on the impact effect brought by wind-driven rain to structure surface. In addition, for the indirect air cooled cooling tower, different additional ventilation rate of shutters produces a considerable interference to air movement inside the tower and also to the action mechanism of loads. To solve the problem, a straight-cone steel cooling towerstanding 189 m high and currently being constructed is taken as the research object in this study. The algorithm for two-way coupling between wind and rain is adopted. Simulation of wind field and raindrops is performed with continuous phase and discrete phase models, respectively, under the general principles of computational fluid dynamics (CFD). Firstly, the rule of influence of 9 combinations of wind sped and rainfall intensity on flow field mechanism, the volume of wind-driven rain, additional action force of raindrops and equivalent internal pressure coefficient of the tower cylinder is analyzed. On this basis, the internal pressures of the cooling tower under the most unfavorable working condition are compared between four ventilation rates of shutters (0%, 15%, 30% and 100%). The results show that the 3D effect of equivalent internal pressure coefficient is the most significant when considering two-way coupling between wind and rain. Additional load imposed by raindrops on the internal surface of the tower accounts for an extremely small proportion of total wind load, the maximum being only 0.245%. This occurs under the combination of 20 m/s wind velocity and 200 mm/h rainfall intensity. Ventilation rate of shutters not only changes the air movement inside the tower, but also affects the accumulated amount and distribution of raindrops on the internal surface.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.7
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• pp.8-15
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• 2019

In this study, we synthesized a calcium sulfonate complex grease and a lithium complex grease to investigate their physical, rheological and tribological properties. The thermal stability of the calcium sulfonate was higher than $300^{\circ}C$ and the lithium complex grease was $245^{\circ}C$ in the dropping point test. In the grease viscosity measurement, the calcium sulfonate complex grease was measured as $7.0Pa{\cdot}s$ and the lithium complex grease was as $4.5Pa{\cdot}s$. Therefore, it was confirmed that the calcium sulfonate complex grease is superior to the lithium complex grease in terms of thermal stability and cohesiveness. In the 4-ball wear test, the calcium sulfonate complex grease was measured to be 0.43 mm and the lithium complex grease to 0.85 mm. In the 4-ball extreme pressure test, calcium sulfonate complex grease was measured as 620 kgf and the lithium complex grease was as 125 kgf. Therefore, it was confirmed that the calcium sulfonate complex grease is superior to the lithium complex grease in abrasion resistance and load-bearing property. It was found that the calcium sulfonate complex grease is more effective than the lithium complex grease in the lubrication at high temperature and high load.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.1
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• pp.1-6
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• 2021

Concrete structures gradually age due to deterioration of materials or excess loads and environmental factors, and their performance decreases, affecting the usability and safety of structures. Although external tension construction methods are widely used among the reinforcement methods of old bridges, it is insufficient to identify the effects and effects of reinforcement depending on the level of aging. Therefore, in this study, a four-point loading experiment was conducted on the subject with the non-reinforced and external tensioning method to confirm the reinforcement effect of the external tensioning method, assuming the aging of the structure as a reduction in the compressive strength and tensile reinforcement of concrete, to analyze the behavior of the reinforcement and confirm the reinforcement effect. As a result of the experiment, it was difficult to identify the amount of reinforcement in the extreme condition due to early elimination of the anchorage. Therefore, compliance with the regulations on anchor bolts is required when applying the external tension reinforcement method. Crack load and yield load increased depending on whether external tension was reinforced, but before the crack, the stiffness before and after reinforcement was similar, making it difficult to confirm the reinforcement effect.

• Journal of Wetlands Research
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• v.24 no.1
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• pp.25-37
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• 2022

Climate change is becoming increasingly unpredictable. This has led to changes in various systems such as ecosystems, human life and hydrological cycles. In particular, the recent unpredictable climate change frequently causes extreme droughts and torrential rains, resulting in complex water resources disasters that cause water pollution due to inundation and retirement rather than primary disasters. SWAT was used as a watershed model to analyze future runoff and pollutant loads. The climate scenario analyzed the RCP4.5 climate scenario of the Meteorological Agency standard scenario (HadGEM3-RA) using the normal quantitative mapping method. Runoff and pollutant load analysis were performed by linkage simulation of climate scenario and watershed model. Finally, the results of application and verification of linkage model and analysis of future water quality change due to climate change were presented. In this study, we simulated climate change scenarios using artificial neural networks, analyzed changes in water temperature and turbidity, and compared the results of dams with artificial neural network results through W2 model, a reservoir water quality model. The results of this study suggest the possibility of applying the nonlinearity and simplicity of neural network model to Hapcheon dam water quality prediction using climate change.