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Giyeol Lee
Department of Landscape Architecture, College of Agriculture and Life Science, Chonnam National University, Gwangju 61186, Korea

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Journal article
Published: 22 April 2021 in Materials
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The purpose of this study is to suggest the optimum mix design with a high volume of GGBS (Ground Granulated Blast-furnace Slag) replacement and the procedure of the cryogenic test to consider mechanical and thermal properties, and durability performance. To decide the optimum mix design, four mix designs with high-volume of GGBS replacement were suggested, in terms of the slump and retention time. Based on the test results, with respect to the workability and compressive strength, the mixtures with 65% of GGBS (C40-2 and C40-4) were better than the mixtures with 50% and 60% of GGBS (C40-1 and C40-3). After selecting two mixtures, two types of cryogenic test methods were conducted under one-cycle cryogenic condition (Test A) and 50-cycles cryogenic condition (Test B). As a result, in Test A, the compressive strength and elastic modulus of the C40-2 and C40-4 mixtures tended to be decreased over time, because of the volume expansion of ice crystals contained in the capillary pores. In Test B, the mechanical properties of the C40-4 mixture were better than those of the C40-2 mixture, in terms of the reduction rate of compressive strength and elastic modulus. In the view of the heat of hydration, the semi-adiabatic test was conducted. In the results, the C40-4 mixture was better to control the thermal cracks. Thus, the C40-4 mixture would be more suitable for cryogenic concrete and this procedure could be helpful to decide the mixture of cryogenic concrete. In the future, the long-term performance of cryogenic concrete needs to be investigated.

ACS Style

Giyeol Lee; Okpin Na. Assessment of Mechanical, Thermal and Durability Properties of High-Volume GGBS Blended Concrete Exposed to Cryogenic Conditions. Materials 2021, 14, 2129 .

AMA Style

Giyeol Lee, Okpin Na. Assessment of Mechanical, Thermal and Durability Properties of High-Volume GGBS Blended Concrete Exposed to Cryogenic Conditions. Materials. 2021; 14 (9):2129.

Chicago/Turabian Style

Giyeol Lee; Okpin Na. 2021. "Assessment of Mechanical, Thermal and Durability Properties of High-Volume GGBS Blended Concrete Exposed to Cryogenic Conditions." Materials 14, no. 9: 2129.

Transportation engineering
Published: 04 March 2021 in KSCE Journal of Civil Engineering
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Various road occupation works pose potential risks of traffic accidents because they cause traffic bottleneck and congestion, which lead to unstable driving patterns such as quick deceleration and lane changes of vehicles. Even though guidance signs are installed and managed in work zones for preventing traffic accidents, the differences of installation criteria set by the Korea Expressway Corporation (KEC) and the Ministry of Land, Infrastructure and Transport (MOLIT) may increase the confusion of drivers. Therefore, this study set scenarios that take into consideration the colors and shapes of the work zone guidance signs used in driving tests through a virtual driving simulator and a survey on the preferences of drivers. According to the survey on the preference of drivers, 73% of the respondents analyzed prefer guidance signs in fluorescent orange color. In terms of driving patterns, the criteria of the KEC were found to induce smooth deceleration in expressway work zones than those of the MOLIT. In terms of driver concentration, the guidance signs conforming to the criteria of the KEC led to approximately 1.15 times higher concentration than those conforming to the MOLIT, which means that the former induce cautious driving in expressway work zones. The result of this study may be utilized as meaningful data for preventing traffic accidents by improving the traffic safety of road work zones on expressways and general roads, as well as by improving the traffic flow efficiency.

ACS Style

Je Jin Park; Im Ki Seo; Gi Yeol Lee. Evaluation of the Expressway Work Zone Guidance Systems Using a Virtual Driving Simulator. KSCE Journal of Civil Engineering 2021, 25, 1446 -1454.

AMA Style

Je Jin Park, Im Ki Seo, Gi Yeol Lee. Evaluation of the Expressway Work Zone Guidance Systems Using a Virtual Driving Simulator. KSCE Journal of Civil Engineering. 2021; 25 (4):1446-1454.

Chicago/Turabian Style

Je Jin Park; Im Ki Seo; Gi Yeol Lee. 2021. "Evaluation of the Expressway Work Zone Guidance Systems Using a Virtual Driving Simulator." KSCE Journal of Civil Engineering 25, no. 4: 1446-1454.

Journal article
Published: 31 March 2020 in Atmosphere
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Both long- and short-term exposure to high concentrations of airborne particulate matter (PM) severely affect human health. Many countries now regulate PM concentrations. Early-warning systems based on PM concentration levels are urgently required to allow countermeasures to reduce harm and loss. Previous studies sought to establish accurate, efficient predictive models. Many machine-learning methods are used for air pollution forecasting. The long short-term memory and gated recurrent unit methods, typical deep-learning methods, reliably predict PM levels with some limitations. In this paper, the authors proposed novel hybrid models to combine the strength of two types of deep learning methods. Moreover, the authors compare hybrid deep-learning methods (convolutional neural network (CNN)—long short-term memory (LSTM) and CNN—gated recurrent unit (GRU)) with several stand-alone methods (LSTM, GRU) in terms of predicting PM concentrations in 39 stations in Seoul. Hourly air pollution data and meteorological data from January 2015 to December 2018 was used for these training models. The results of the experiment confirmed that the proposed prediction model could predict the PM concentrations for the next 7 days. Hybrid models outperformed single models in five areas selected randomly with the lowest root mean square error (RMSE) and mean absolute error (MAE) values for both PM10 and PM2.5. The error rate for PM10 prediction in Gangnam with RMSE is 1.688, and MAE is 1.161. For hybrid models, the CNN–GRU better-predicted PM10 for all stations selected, while the CNN–LSTM model performed better on predicting PM2.5.

ACS Style

Guang Yang; Hwamin Lee; Giyeol Lee. A Hybrid Deep Learning Model to Forecast Particulate Matter Concentration Levels in Seoul, South Korea. Atmosphere 2020, 11, 348 .

AMA Style

Guang Yang, Hwamin Lee, Giyeol Lee. A Hybrid Deep Learning Model to Forecast Particulate Matter Concentration Levels in Seoul, South Korea. Atmosphere. 2020; 11 (4):348.

Chicago/Turabian Style

Guang Yang; Hwamin Lee; Giyeol Lee. 2020. "A Hybrid Deep Learning Model to Forecast Particulate Matter Concentration Levels in Seoul, South Korea." Atmosphere 11, no. 4: 348.

Journal article
Published: 24 March 2020 in Sustainability
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Many countries worldwide have poor air quality due to the emission of particulate matter (i.e., PM10 and PM2.5), which has led to concerns about human health impacts in urban areas. In this study, we developed models to predict fine PM concentrations using long short-term memory (LSTM) and deep autoencoder (DAE) methods, and compared the model results in terms of root mean square error (RMSE). We applied the models to hourly air quality data from 25 stations in Seoul, South Korea, for the period from 1 January 2015, to 31 December 2018. Fine PM concentrations were predicted for the 10 days following this period, at an optimal learning rate of 0.01 for 100 epochs with batch sizes of 32 for LSTM model, and DAEs model performed best with batch size 64. The proposed models effectively predicted fine PM concentrations, with the LSTM model showing slightly better performance. With our forecasting model, it is possible to give reliable fine dust prediction information for the area where the user is located.

ACS Style

Thanongsak Xayasouk; Hwamin Lee; Giyeol Lee. Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models. Sustainability 2020, 12, 2570 .

AMA Style

Thanongsak Xayasouk, Hwamin Lee, Giyeol Lee. Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models. Sustainability. 2020; 12 (6):2570.

Chicago/Turabian Style

Thanongsak Xayasouk; Hwamin Lee; Giyeol Lee. 2020. "Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models." Sustainability 12, no. 6: 2570.