• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.1B
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• pp.41-53
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• 2008

Unprecedented weather phenomena are occurring because of climate change: extreme heavy rains, heat waves, and severe rain storms after the rainy season. Recently, the frequency of these abnormal phenomena has increased. However, regular pattern or cycles cannot be found. Analysis of annual data or annual average data, which has been established a research method of climate change, should be applied to find frequency and tendencies of extreme climate events. In this paper, extreme indicators of precipitation and temperature marked by objectivity and consistency were established to analyze data collected by 66 observatories throughout Korea operated by the Meteorological Administration. To assess the statistical significance of the data, linear regression and Kendall-Tau method were applied for statistical diagnosis. The indicators were analyzed to find tendencies. The analysis revealed that an increase of precipitation along with a decrease of the number of rainy days. A seasonal trend was also found: precipitation rate and the heavy rainfall threshold increased to a greater extent in the summer(June-August) than in the winter (September-November). In the meanwhile, a tendency of temperature increase was more prominent in the winter (December-February) than in the summer (June-August). In general, this phenomenon was more widespread in inland areas than in coastal areas. Furthermore, the number of winter frost days diminished throughout Korea. As was mentioned in the literature, the progression of climate change has influenced the increase of temperature in the winter.

• International Journal of Computer Science & Network Security
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• v.21 no.5
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• pp.23-30
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• 2021

In recent years, hazardous flash flooding has caused deaths and damage to infrastructure in Saudi Arabia. In this paper, our aim is to assess patterns and trends in climate means and extremes affecting flash flood hazards and water resources in Saudi Arabia for the purpose to improve risk assessment for forecast capacity. We would like to examine temperature, precipitation climatology and trend magnitudes at surface stations in Saudi Arabia. Based on the assessment climate patterns maps and trends are accurately used to identify synoptic situations and tele-connections associated with flash flood risk. We also study local and regional changes in hydro-meteorological extremes over recent decades through new applications of statistical methods to weather station data and remote sensing based precipitation products; and develop remote sensing based high-resolution precipitation products that can aid to develop flash flood guidance system for the flood-prone areas. A dataset of extreme events has been developed using the multi-decadal station data, the statistical analysis has been performed to identify tele-connection indices, pressure and sea surface temperature patterns most predictive to heavy rainfall. It has been combined with time trends in extreme value occurrence to improve the potential for predicting and rapidly detecting storms. A methodology and algorithms has been developed for providing a well-calibrated precipitation product that can be used in the early warning systems for elevated risk of floods.

• Structural Engineering and Mechanics
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• v.83 no.4
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• pp.537-549
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• 2022

In this paper, a novel multi-feature model predictive control (MPC) framework with real-time and adaptive performances is proposed for intelligent structural control in which some drawbacks of the algorithm including, complex control rule and non-optimality, are alleviated. Hence, Linear Programming (LP) is utilized to simplify the resulted control rule. Afterward, the Whale Optimization Algorithm (WOA) is applied to the optimal and adaptive tuning of the LP weights independently at each time step. The stochastic control rule is also achieved using Kalman Filter (KF) to handle noisy measurements. The Extreme Learning Machine (ELM) is then adopted to develop a data-driven and real-time control algorithm. The efficiency of the developed algorithm is then demonstrated by numerical simulation of a twenty-story high-rise benchmark building subjected to earthquake excitations. The competency of the proposed method is proven from the aspects of optimality, stochasticity, and adaptivity compared to the KF-based MPC (KMPC) and constrained MPC (CMPC) algorithms in vibration suppression of building structures. The average value for performance indices in the near-field and far-field (El earthquakes demonstrates a reduction up to 38.3% and 32.5% compared with KMPC and CMPC, respectively.

• Journal of Korean Association for Spatial Structures
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• v.23 no.3
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• pp.87-94
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• 2023

Chloride is one of the most common threats to reinforced concrete (RC) durability. Alkaline environment of concrete makes a passive layer on the surface of reinforcement bars that prevents the bar from corrosion. However, when the chloride concentration amount at the reinforcement bar reaches a certain level, deterioration of the passive protection layer occurs, causing corrosion and ultimately reducing the structure's safety and durability. Therefore, understanding the chloride diffusion and its prediction are important to evaluate the safety and durability of RC structure. In this study, the chloride diffusion coefficient is predicted by machine learning techniques. Various machine learning techniques such as multiple linear regression, decision tree, random forest, support vector machine, artificial neural networks, extreme gradient boosting annd k-nearest neighbor were used and accuracy of there models were compared. In order to evaluate the accuracy, root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2) were used as prediction performance indices. The k-fold cross-validation procedure was used to estimate the performance of machine learning models when making predictions on data not used during training. Grid search was applied to hyperparameter optimization. It has been shown from numerical simulation that ensemble learning methods such as random forest and extreme gradient boosting successfully predicted the chloride diffusion coefficient and artificial neural networks also provided accurate result.

• Journal of Korea Water Resources Association
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• v.45 no.4
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• pp.393-407
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• 2012

This study suggests the results of temporal and spatial variations for rainfall data in the Korean Peninsula. We got the index of the rainfall amount, frequency and extreme indices from 65 weather stations. The results could be easily understood by drawing the graph, and the Mann-Kendall trend analysis was also used to determine the tendency (up & downward/no trend) of rainfall and temperature where the trend could not be clear. Moreover, by using the FARD, frequency probability rainfalls could be calculated for 100 and 200 years and then compared each other value through the moment method, maximum likelihood method and probability weighted moments. The Average Rainfall Index (ARI) which is meant comprehensive rainfalls risk for the flood could be obtained from calculating an arithmetic mean of the RI for Amount (RIA), RI for Extreme (RIE), and RI for Frequency (RIF) and as well as the characteristics of rainfalls have been mainly classified into Amount, Extremes, and Frequency. As a result, these each Average Rainfall Indices could be increased respectively into 22.3%, 26.2%, and 5.1% for a recent decade. Since this study showed the recent climate change trend in detail, it will be useful data for the research of climate change adaptation.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.1
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• pp.81-96
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• 2017

Along with climate change, it is reported that the scale and the frequency of extreme climate events (e.g. heavy rain, typhoon, etc.) show unstable tendency of increase. In case of Korea, also, the frequency of heavy rainfall shows increasing tendency, thus causing natural disaster damage in downtown and agricultural areas by rainfall that exceeds the design criteria of hydraulic structures. In order to minimize natural disaster damage, it is necessary to analyze how extreme precipitation event changes under climate change. Therefore a new design criteria based on non-stationarity frequency analysis is needed to consider a tendency of future extreme precipitation event and to prepare countermeasures to climate change. And a quantitative and objective characteristic analysis could be a key to preparing countermeasures to climate change impact. In this study, non-stationarity frequency analysis was performed and inundation risk indices developed by 4 inundation characteristics (e.g. inundation area, inundation depth, inundation duration, and inundation radius) were assessed. The study results showed that future probable rainfall could exceed the existing design criteria of hydraulic structures (rivers of state: 100yr-200yr, river banks: 50yr-100yr) reaching over 500yr frequency probable rainfall of the past. Inundation characteristics showed higher value in the future compared to the past, especially in sections with tributary stream inflow. Also, the inundation risk indices were estimated as 0.14 for the past period of 1973-2015, and 0.25, 0.29, 1.27 for the future period of 2016-2040, 2041-2070, 2071-2100, respectively. The study findings are expected to be used as a basis to analyze future inundation damage and to establish management solutions for rivers with inundation risks.

• Smart Structures and Systems
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• v.22 no.2
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• pp.239-247
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• 2018

A magnetic flux leakage (MFL) method was applied to detect and quantify defects in a steel bar. A multi-channel MFL sensor head was fabricated using Hall sensors and magnetization yokes with permanent magnets. The MFL sensor head scanned a damaged specimen with five levels of defects to measure the magnetic flux density. A series of signal processing procedures, including an enveloping process based on the Hilbert transform, was performed to clarify the flux leakage signal. The objective damage detection of the enveloped signals was then analyzed by comparing them to a threshold value. To quantitatively analyze the MFL signal according to the damage level, five kinds of damage indices based on the relationship between the enveloped MFL signal and the threshold value were applied. Using the proposed damage indices and the general damage index for the MFL method, the detected MFL signals were quantified and analyzed relative to the magnitude of the damage increase.

• Journal of The Korean Society of Agricultural Engineers
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• v.55 no.4
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• pp.73-82
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• 2013

The main objective of this study was to evaluate Representative Concentration Pathways (RCP) scenarios-based flood risk at a Si-Gun level. A bias correction using a quantile mapping method with the Generalized Extreme Value (GEV) distribution was performed to correct future precipitation data provided by the Korea Meteorological Administration (KMA). A series of proxy variables including CN80 (Number of days over 80 mm) and CX3h (Maximum precipitation during 3-hr) etc. were used to carry out flood risk assessment. Indicators were normalized by a Z-score method and weighted by factors estimated by principal component analysis (PCA). Flood risk evaluation was conducted for the four different time periods, i.e. 1990s, 2025s, 2055s, and 2085s, which correspond to 1976~2005, 2011~2040, 2041~2070, and 2071~2100. The average flood risk indices based on RCP4.5 scenario were 0.08, 0.16, 0.22, and 0.13 for the corresponding periods in the order of time, which increased steadily up to 2055s period and decreased. The average indices based on RCP8.5 scenario were 0.08, 0.23, 0.11, and 0.21, which decreased in the 2055s period and then increased again. Considering the average index during entire period of the future, RCP8.5 scenario resulted in greater risk than RCP4.5 scenario.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.6
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• pp.735-742
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• 2019

Drought is a social phenomenon in which the degree of perception varies depending on the affected factors, and is defined as various relative concepts such as meteorological drought, hydrological drought, agricultural drought, and climatological drought. In this study, a comparative analysis of meteorological drought among variously defined droughts was conducted and the applicability of the drought index was examined by comparing the actual drought cases and the results of meteorological drought index analysis. In order to compare the drought index, we used standardized Precipitation Index (SPI), China-Z Index (CZI), Modified CZI (MCZI) and Z-Score Index Respectively. Four drought indices were used for the Taebaek and Sokcho areas. The drought index was analyzed using the meteorological data from 1986 to 2015 for a duration of 3 months. As a result of the analysis, the SPI drought index was analyzed to be highly reproducible for the case of drought with past limited water series. In the case of CZI and MCZI drought indices, the number of extreme dry occurrences is similar to that of the past cases, but the reproducibility is low for the actual drought years. In the case of ZSI drought index, it is analyzed that the number of occurrences and the comparison with the past cases are inferior in reproducibility. For the meteorological drought index using precipitation, it would be effective to use the SPI drought index with the highest reproducibility and the past drought case.

• Steel and Composite Structures
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• v.33 no.3
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• pp.319-332
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• 2019

This study is aimed to predict the behaviour of channel shear connectors in composite floor systems at different temperatures. For this purpose, a soft computing approach is adopted. Two novel intelligence methods, including an Extreme Learning Machine (ELM) and a Genetic Programming (GP), are developed. In order to generate the required data for the intelligence methods, several push-out tests were conducted on various channel connectors at different temperatures. The dimension of the channel connectors, temperature, and slip are considered as the inputs of the models, and the strength of the connector is predicted as the output. Next, the performance of the ELM and GP is evaluated by developing an Artificial Neural Network (ANN). Finally, the performance of the ELM, GP, and ANN is compared with each other. Results show that ELM is capable of achieving superior performance indices in comparison with GP and ANN in the case of load prediction. Also, it is found that ELM is not only a very fast algorithm but also a more reliable model.