• Journal of the Korea institute for structural maintenance and inspection
• /
• v.25 no.1
• /
• pp.75-84
• /
• 2021

In this study, to evaluate deterioration propagation life and deterioration curve of the shop painted and field re-painted steel bridges, accelerated corrosion tests were carried out on 4 types of heavy-duty painting systems with different surface treatments. The surface treatments prior to painting were examined by hand tool(SSPC SP-2), power tool(SP-3,) or blast cleaning(SP-10) considering shop painting and field re-painting. The paint deterioration curves for each painting system and surface treatment were evaluated based on corrosion propagation from the initial paint defects. From the test results, the paint deterioration life of shop painted and field re-painted system was evaluated and compared by using corrosivity categories and durability performance evaluation of structural steel. The deterioration propagation life of shop and field paint was estimated in 18 to 21 years and 5.3 to 8.0 years with atmospheric corrosion category C4.

• Journal of the Society of Disaster Information
• /
• v.18 no.1
• /
• pp.10-18
• /
• 2022

Purpose: For smooth performance of flood analysis due to heavy rain disasters at energy storage facilities in the Incheon area, field surveys, observational surveys, and pre-established reports and drawings were analyzed. Through the field survey, the characteristics of pipelines and rivers that have not been identified so far were investigated, and based on this, the input data of the SWMM model selected for inundation analysis was constructed. Method: In order to determine the critical duration through the probability flood analysis according to the calculation of the probability rainfall intensity by recurrence period and duration, it is necessary to calculate the probability rainfall intensity for an arbitrary duration by frequency, so the research results of the Ministry of Land, Transport and Maritime Affairs were utilized. Result: Based on this, the probability of rainfall by frequency and duration was extracted, the critical duration was determined through flood analysis, and the rainfall amount suggested in the disaster prevention performance target was applied to enable site safety review. Conclusion: The critical duration of the base was found to be a relatively short duration of 30 minutes due to the very gentle slope of the watershed. In general, if the critical duration is less than 30 minutes, even if flooding occurs, the scale of inundation is not large.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.49 no.12
• /
• pp.963-969
• /
• 2021

The wind data measured from local meteorological masts is used to evaluate wind speed distribution and energy production in the specified site for wind farm However, wind data measured from meteorological masts often contain missing information or insufficient desired height or data length, making it difficult to perform wind turbine control and performance simulation. Therefore, long-term continuous wind data is very important to assess the annual energy production and the capacity factor for wind turbines or wind farms. In addition, if seasonal influences are distinct, such as on the Korean Peninsula, wind data with seasonal characteristics should be considered. This study presents methodologies for generating synthetic wind that take into account fluctuations in both wind speed and direction using the hidden Markov model, which is a statistical method. The wind data for statistical processing are measured at Maldo island in the Kokunnsan-gundo, Jeonbuk Province using the Automatic Weather System (AWS) of the Korea Meteorological Administration. The synthetic wind generated using the hidden Markov model will be validated by comparing statistical variables, wind energy density, seasonal mean speed, and prevailing wind direction with measurement data.

• Journal of Korea Water Resources Association
• /
• v.55 no.12
• /
• pp.1091-1104
• /
• 2022

Climate change is predicted to increase the frequency and intensity of rainfall worldwide, and the pattern is changing due to inundation damage in urban areas due to rapid urbanization and industrialization. Accordingly, the impact assessment of climate change is mentioned as a very important factor in urban planning, and the World Meteorological Organization (WMO) is emphasizing the need for an impact forecast that considers the social and economic impacts that may arise from meteorological phenomena. In particular, in terms of traffic, the degradation of transport systems due to urban flooding is the most detrimental factor to society and is estimated to be around £100k per hour per major road affected. However, in the case of Korea, even if accurate forecasts and special warnings on the occurrence of meteorological disasters are currently provided, the effects are not properly conveyed. Therefore, in this study, high-resolution analysis and hydrological factors of each area are reflected in order to suggest the depth of flooding of urban floods and to cope with the damage that may affect vehicles, and the degree of flooding caused by rainfall and its effect on vehicle operation are investigated. decided it was necessary. Therefore, the calculation formula of rainfall-immersion depth-vehicle speed is presented using various machine learning techniques rather than simple linear regression. In addition, by applying the climate change scenario to the rainfall-inundation depth-vehicle speed calculation formula, it predicts the flooding of urban rivers during heavy rain, and evaluates possible traffic network disturbances due to road inundation considering the impact of future climate change. We want to develop technology for use in traffic flow planning.

• The Journal of the Convergence on Culture Technology
• /
• v.7 no.4
• /
• pp.869-878
• /
• 2021

This study is a study on 11-story apartments that increase the event of fires in old apartments where building-related laws and regulations are not retroactively applied. As a result of analyzing the risk of installing fire doors in Improvement Scenario 2-4, assuming that fire doors are installed as basic scenario 1 in the existing situation where fire doors are not installed at the entrance of direct stairs. In basic scenario 1, the visible distance to the entrance of the direct staircase due to the spread of smoke was 260 seconds. Improvement scenarios 3 to 4 with fire doors installed open 300 seconds after the fire was recognized, and when the fire doors were installed at the entrance of the direct stairs, the visibility to the entrance of the statistics team was less than 600 seconds. In this case, the visibility was 600 seconds at the time of installation of the fire door, and scenarios 3 to 4 increased 56.6% compared to scenario 1, lowering the risk of evacuation by more than 50%. In order to eliminate the risk of non-installation of direct statistical groups that increase the risk of smoke spread, building-related laws such as the Fire Fighting Act shall be retroactively applied when installing a direct stairway entrance or balcony folding evacuation system. The improvement caused by the installation of fire doors has numerically proven the necessity of fire doors during evacuation, and the importance of maintaining fire doors can be grasped.

• Journal of the Society of Disaster Information
• /
• v.19 no.3
• /
• pp.698-709
• /
• 2023

Purpose: In this paper, we would like to analyze the growth rate of existing urban immersion in the downstream during large-scale urban development and the degree of reduction in existing urban immersion in the downstream when small excellent storage facilities are planned in apartment complexes. Method: A large-scale sewage model was built using the SWMM model of the U.S. Environmental Protection Agency, and the impact of flooding in existing downtown areas downstream was analyzed through simulation. The built model included the development zone, the existing downtown area downstream, and the entire river basin that discharges rainwater. Result: As a result of calculating and simulating the minimum excellent reservoir capacity for each apartment block in the study target area, it was found that the immersion of 4,893㎥ based on one hour, 25,815㎥ based on two hours, and 55,528㎥ based on three hours in the downstream urban area. Conclusion: As in this study, large-scale flooding simulation considering the existing downtown area in the downstream shows a significant increase in flooding in the downstream, and if excellent reservoir capacity is planned for each apartment block before development and the construction of excellent reservoirs is recommended.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.05a
• /
• pp.43-45
• /
• 2021

The short-term quantitative precipitation prediction (QPF) system is important socially and economically to prevent damage from severe weather. Recently, many studies for short-term QPF model applying the Deep Neural Network (DNN) has been conducted. These studies require the sophisticated pre-processing because the mistreatment of various and vast meteorological data sets leads to lower performance of QPF. Especially, for more accurate prediction of the non-linear trends in precipitation, the dataset needs to be carefully handled based on the physical and dynamical understands the data. Thereby, this paper proposes the following approaches: i) refining and combining major factors (weather radar, terrain, air temperature, and so on) related to precipitation development in order to construct training data for pattern analysis of precipitation; ii) producing predicted precipitation fields based on Convolutional with ConvLSTM. The proposed algorithm was evaluated by rainfall events in 2020. It is outperformed in the magnitude and strength of precipitation, and clearly predicted non-linear pattern of precipitation. The algorithm can be useful as a forecasting tool for preventing severe weather.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.43 no.6
• /
• pp.883-896
• /
• 2023

As human activities in Korea are spread throughout the mountains, forest fires often affect residential areas, infrastructure, and other facilities. Hence, it is necessary to detect fire-damaged areas quickly to enable support and recovery. Remote sensing is the most efficient tool for this purpose. Fire damage detection experiments were conducted on the east coast of Korea. Because this area comprises a mixture of forest and artificial land cover, data with low resolution are not suitable. We used Sentinel-2 multispectral instrument (MSI) data, which provide adequate temporal and spatial resolution, and the k-nearest neighbor (kNN) algorithm in this study. Six bands of Sentinel-2 MSI and two indices of normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as features for kNN classification. The kNN classifier was trained using 2,000 randomly selected samples in the fire-damaged and undamaged areas. Outliers were removed and a forest type map was used to improve classification performance. Numerous experiments for various neighbors for kNN and feature combinations have been conducted using bi-temporal and uni-temporal approaches. The bi-temporal classification performed better than the uni-temporal classification. However, the uni-temporal classification was able to detect severely damaged areas.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.43 no.6
• /
• pp.775-784
• /
• 2023

In terms of flood management, it is necessary to analyze quantitative rainfall and runoff from a spatial and temporal perspective and to analyze runoff for heavy rainfall events that are concentrated within a short period of time. The simulation and analysis results of rainfall-runoff models vary depending on the type and input data. In particular, rainfall data is an important factor, so calculating areal mean rainfall is very important. In this study, the areal mean rainfall of the Samcheok Osipcheon(Riv.) watersheds located in the mountainous terrain was calculated using the Arithmetic Mean Method, Thiessen's Weighting Method, and the Isohyetal Method, and the rainfall-runoff results were compared by applying the distributional model S-RAT and the lumped model HEC-HMS. The results of the temporal transferability study showed that the combination of the distributional model and the Isohyetal Method had the best statistical performance with MAE of 64.62 m³/s, RMSE of 82.47 m³/s, and R² and NSE of 0.9383 and 0.8547, respectively. It is considered that this study was properly analyzed because the peak flood volume occurrence time of the observed and simulated flows is within 1 hour. Therefore, the results of this study can be used for frequency analysis in the future, which can be used to improve the accuracy of simulating peak flood volume and peak flood occurrence time in mountainous watersheds with steep slopes.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.23 no.3
• /
• pp.149-155
• /
• 2021

Early warning systems for weather risk management in the agricultural sector have been developed to predict potential wind damage to crops. These systems take into account the daily maximum wind speed to determine the critical wind speed that causes fruit drops and provide the weather risk information to farmers. In an effort to increase the accuracy of wind risk predictions, an artificial neural network for binary classification was implemented. In the present study, the daily wind speed and other weather data, which were measured at weather stations at sites of interest in Jeollabuk-do and Jeollanam-do as well as Gyeongsangbuk- do and part of Gyeongsangnam- do provinces in 2019, were used for training the neural network. These weather stations include 210 synoptic and automated weather stations operated by the Korean Meteorological Administration (KMA). The wind speed data collected at the same locations between January 1 and December 12, 2020 were used to validate the neural network model. The data collected from December 13, 2020 to February 18, 2021 were used to evaluate the wind risk prediction performance before and after the use of the artificial neural network. The critical wind speed of damage risk was determined to be 11 m/s, which is the wind speed reported to cause fruit drops and damages. Furthermore, the maximum wind speeds were expressed using Weibull distribution probability density function for warning of wind damage. It was found that the accuracy of wind damage risk prediction was improved from 65.36% to 93.62% after re-classification using the artificial neural network. Nevertheless, the error rate also increased from 13.46% to 37.64%, as well. It is likely that the machine learning approach used in the present study would benefit case studies where no prediction by risk warning systems becomes a relatively serious issue.