• International conference on construction engineering and project management
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• 2017.10a
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• pp.252-259
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• 2017

Nowadays, the existing manual method for recording actual progress of the construction site has some drawbacks, such as great reliance on the experience of professional engineers, work-intensive, time consuming and error prone. A method integrating computer vision and BIM(Building Information Modeling) is presented for indoor automatic progress monitoring. The developed method can accurately calculate the engineering quantity of target component in the time-lapse images. Firstly, sample images of on-site target are collected for training the classifier. After the construction images are identified by edge detection and classifier, a voting algorithm based on mathematical geometry and vector operation will divide the target contour. Then, according to the camera calibration principle, the image pixel coordinates are conversed into the real world Coordinate and the real coordinates would be corrected with the help of the geometric information in BIM model. Finally, the actual engineering quantity is calculated.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.6
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• pp.101-112
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• 2022

Extreme precipitation events have recently become a leading cause of disasters. Thus, investigating the variability and trends of extreme precipitation is crucial to mitigate the increasing impact of such events. Spatial distribution and temporal trends in annual precipitation and four extreme precipitation indices of duration (CWD), frequency (R10 mm), intensity (Rx1day), and percentile-based threshold (R95pTOT) were analyzed using the daily precipitation data of 10 observation stations in Chungcheong province during 1974-2020. The precipitation at all observation stations, except the Boryeong station, showed nonsignificant increasing trends at 95% confidence level (CL) and increasing magnitudes from the west to east regions. The high variability in mean annual precipitation was more pronounced around the northeast and northwest regions. Similarly, there were moderate to high patterns in extreme precipitation indices around the northeast region. However, the precipitation indices of duration and frequency consistently increased from the west to east regions, while those of intensity and percentile-based threshold increased from the south to east regions. Nonsignificant increasing trends dominated in CWD, R10 mm, and Rx1day at all stations, except for R10 mm at Boeun station and Rx1day at Cheongju and Jecheon stations, which showed a significantly increasing trend. The spatial distribution of trend magnitude shows that R10 mm increased from the west to east regions. Furthermore, variations in precipitation were very strongly correlated (99% CL) with R10 mm, Rx1day, and R95pTOT at all stations, except with wR10 mm at Cheongju station, which was strongly correlated with a 95% CL.

• Proceedings of the IEEK Conference
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• 2001.10a
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• pp.117-121
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• 2001

The present study has been conflicted to verify the applicability of tire powder and food waste compost as biofilter materials to degrade volatile organic compounds. Batch and column tests were performed to determine the optimum ratio of tire powder to compost and the appropriate mixing type of two materials for removal of the selected VOCs, i.e., benzene, ethylbenzene, PCE, and TCE. According to batch tests, tire powder and compost mixture had faster removal rate than the compost. The biofilter column filled with tire powder and compost showed better VOC removal efficiency than that filled with only tire powder. In this study, the best removal rate was observed in the sandwich type column test of which the tire : compost weight ratio was 1:2

• Steel and Composite Structures
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• v.34 no.4
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• pp.467-485
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• 2020

Genetic Algorithm (GA) is a meta-heuristic algorithm which is capable of providing robust solutions for optimal design of structural components, particularly those one needs considering many design requirements. Hence, it has been successfully used by engineers in the typology optimization of structural members. As a novel approach, this study employs GA in order for conducting a case study with high constraints on the optimum mechanical properties of reinforced concrete (RC) beams under different load combinations. Accordingly, unified optimum sections through a computer program are adopted to solve the continuous beams problem. Genetic Algorithms proved in finding the optimum resolution smoothly and flawlessly particularly in case of handling many complicated constraints like a continuous beam subjected to different loads as moments shear - torsion regarding the curbs of design codes.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.1
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• pp.79-89
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• 2022

Soil moisture is significantly related to crop growth and plays an important role in irrigation management. To predict soil moisture, various process-based model has been developed and used in the world. Various models (Land surface model) may have different performance depending on the model parameters and structures that causes the different model output for the same modeling condition. In this study, the three land surface models (Noah Land Surface Model, Soil Water Atmosphere Plant, Community Land Model) were used to compare the model performance (soil moisture prediction) and develop the multi-model simulation. At first, the genetic algorithm was used to estimate the optimal soil parameters for each model, and the parameters were used to predict soil moisture in the study area. Then, we used the multi-model approach based on Bayesian model averaging (BMA). The results derived from this approach showed a better match to the measurements than the results from the original single land surface model. In addition, identifying the strengths and weaknesses of the single model and utilizing multi-model methods can help to increase the accuracy of soil moisture prediction.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2001.10a
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• pp.211-218
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• 2001

There are numerous factors in designing the civil-structure even for simple ones. So the designer has to be decide for such conditions and this makes him difficult. Recently, some design programs are used for the design of civil-structure. But even for using the existing design programs, another program is necessary, such as a finite element analysis program. Moreover a few errors may be made in the drafts which must be coincided with the structural calculations. In this study, the design programs for rahmen bridge with single span and box culvert were developed to reduce the design efforts and the manmade errors. These two design programs perform structural analysis, calculations, and making report and draft at a time. In addition, These programs suggest manuals according to standard specifications and references for design. When these programs are used for design, it will be able to reduce the efforts and time of civil engineers.

• Journal of Ocean Engineering and Technology
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• v.30 no.3
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• pp.214-220
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• 2016

Imaging sonars such as side-scanning sonar or forward-looking sonar are becoming fundamental sensors in the underwater robotics field. However, using sonar images for underwater perception presents many challenges. Sonar images are usually low resolution with inherent speckled noise. To overcome the limited sensor information for underwater perception, we investigated preprocessing methods for sonar images and feature detection methods for a nonlinear scale space. In this paper, we focus on a comparative analysis of (1) preprocessing for sonar images and (2) the feature detection performance in relation to the scale space composition.

• Smart Structures and Systems
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• v.25 no.3
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• pp.285-299
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• 2020

Long term structural health monitoring has gained wide attention among civil engineers in recent years due to the scale and severity of infrastructure deterioration. Establishing effective damage indicators and proposing enhanced monitoring methods are of great interests to the engineering practices. In the case of bridge health monitoring, long term structural vibration measurement has been acknowledged to be quite useful and utilized in the planning of maintenance works. Previous researches are majorly concentrated on linear time series models for the measurement, whereas nonlinear dependences among the measurement are not carefully considered. In this paper, a new bridge health monitoring method is proposed based on the use of long term vibration measurement. A combination of the fundamental ARMA model and copula theory is investigated for the first time in detecting bridge structural damages. The concept is applied to a real engineering practice in Japan. The efficiency and accuracy of the copula based damage indicator is analyzed and compared in different window sizes. The performance of the copula based indicator is discussed based on the damage detection rate between the intact structural condition and the damaged structural condition.

• Journal of Korean Society of Environmental Engineers
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• v.38 no.9
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• pp.482-496
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• 2016

Algal blooms of cyanobacteria (blue-green Algae) due to the eutrophication of rivers and lakes can cause not only the damage by its biological toxins but also the economic loss in drinking water treatment. The natural algae coagulant, a commercial product known as W.H. containing the algicidal and allelopathic material derived from oak, can control algal problems proactively through the coagulation flotation process. However, because there have been no applications of the process for pre-treatment in drinking water plants, we could find no report on the optimum injection dose of W.H.. In this study, we have conducted several sets of jar-tests while changing W.H. dose and concentration of chl-a for (1) Han-river samples and (2) subcultured cyanobacteria samples, and monitored the removal mechanisms of algae intensively. Based on these jar-test results, two linear equations with variables of chl-a and turbidity have been deduced to predict the optimal W.H. dose after the multiple regression analysis using IBM-SPSS. Also, prototypes of automatic control logic have been suggested to inject the optimal W.H. dose promptly in response to the variation of water quality.

• Journal of Korean Society of Environmental Engineers
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• v.38 no.4
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• pp.184-192
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• 2016

A profiling monitoring system has been developed to monitor the water quality variations according to each water depth and applied for a test-bed. The key parameters were derived to disclose the aquatic ecology and environment of river systems, and the real-time monitoring techniques to profile the variations of each parameter were verified. Monitoring parameters were configured to include water quality, hydrodynamic, and weather conditions. Considering the water depth of the 4 major rivers in Korea, a profiling monitoring system with 1.0 m water depth interval for each monitoring has been established. To understand the real-time variation properties in the Han river, the monitoring system has been installed and operated at the YangHwa-Dock as a test-bed. Based on the results of the detailed analyses on the spring season, as the characteristic diurnal and water-depth-related variations for water temperature, pH, dissolved oxygen (DO), and chlorophyll-a were observed, it could be concluded that the real-time water-depth profiling monitoring system is a very effective tool for the proper management of river environment.