Cement and other similar compounds have been used to prevent a levee breach during a flood. However, the demand is increasing for eco-friendly and sustainable alternatives to replace the conventional method for levee stabilization and strengthening. To improve the durability and environmental friendliness of a levee, the Andong River Experiment Center applied a biopolymer, which is a new eco-friendly substance, to fabricate a levee model, and conducted a hydraulic model experiment to evaluate the reliability and stability of the new type of levee. An image analysis was applied to calculate the scale of the breaches of the levee slopes. Based on the experimental results obtained, the characteristics of the breach between an earthen levee and the proposed levee were compared. The stability of the levee body was also evaluated according to the thickness of the new substance. The ultimate aim of this study was to derive the optimal conditions by verifying the performance and effectiveness of the new substance in terms of levee breach factors such as overflow, seepage, or piping in a series of hydraulic experiments. In the future, the field application of these optimal conditions will be verified through a real-scale experiment.

No abstract

This study proposes an earthen levee reinforcement method with a new biopolymer-based material to prevent levee scour and breach. It is an eco-friendly method that can efficiently protect the levee slope as it enhances soil strength, even at a very low concentration of biopolymer, and has high resistance to surface runoff in addition to promoting vegetation growth. The function and effectiveness of this method were demonstrated through an overflow-based semi-scale experiment in a previous study. In this study, we examined the effect of biopolymer-mixed soil layer on levee stability against an overflow-induced breach. In these experiments, biopolymer-mixed soils were sprayed on the crest and land-side levee surface. Two full-scale tests were conducted (2.5–2.7 m high and 14 m wide on bottom). Case 1 (control case) consisted of bare sand without any treatment, while Case 2 consisted of a 1.0% biopolymer-mixed soil sprayed on the crest and landside slope of the levee and turf put on it. By applying an image analysis technique, we analyzed the breach phenomenon and breach retardation effect of the levee treated with a biopolymer and covered with vegetation. In this experiment, the slope loss rate of Case 2 was retarded 1.5 to 2.3 times over time as compared to Case 1. During the experiment, we observed that soil erosion followed through the narrow water channel formed by the stripped turfs. This means that the grasses did not root firmly enough to protect the surface. In this regard, although the experimental results may seem unsatisfactory, the biopolymer was found to help improve erosion retardation. In 2020, we will conduct more experiments with different compositions and concentrations of the biopolymer regardless of levee vegetation. With this research, we expect to confirm that the new technology of using biopolymer-treated soils is promising for solving the levee overflow breach problem.

The research regarding side weirs of stormwater storage systems which can be considered as effective hydraulic structure to mitigate the urban flooding and estimation of overflowing discharge over the side weir into those storage systems are significant. The De Marchi's equation seems appropriate for the open channel flow condition to understand hydraulic behavior of the side weir but there are no studies that identify its suitability under the pressurized flow condition.Hence in this study, the overflow discharge coefficient in the De Marchi's equation is evaluated for pressurized flow condition with different side weir length and height to verify the variation of discharge coefficient. The process of proposing discharge coefficient for each different side weir condition in circular channel is discussed through comparisons between experiment and simulation.

Schizophrenia, a mental disorder experienced by more than 20 million people worldwide, is emerging as a serious issue in society. Currently, the diagnosis of schizophrenia is based only on mental disorder diagnosis and/or diagnosis by a psychiatrist or mental health professional using DSM-5, a diagnostic and statistical manual of mental disorders. Furthermore, patients in countries with insufficient access to healthcare are difficult to diagnose for schizophrenia and early diagnosis is even more problematic. While various studies are being conducted to solve the challenges of schizophrenia diagnosis, methodology is considered to be limited, and diagnostic accuracy needs to be improved. In this study, a new approach using EEG data and deep learning is proposed to increase objectivity and efficiency of schizophrenia diagnosis. Existing deep learning studies use EEG data to classify schizophrenic patients and healthy subjects by learning EEG in the form of graphs or tables. However, in this study, EEG, a time series data, was converted into an image to improve classification accuracy, and is then studied in deep learning models. This study used EEG data of 81 people, in which the difference in N100 EEG between schizophrenic patients and healthy patients had been analyzed in prior research. EEGs were converted into images using time series image conversion algorithms, Recurrence Plot (RP) and Gramian Angular Field (GAF), and converted EEG images were learned with Convolutional Neural Network (CNN) models built based on VGGNet. When the trained deep learning model was applied to the same data from prior research, it was demonstrated that classification accuracy improved when compared to previous studies. Among the two algorithms used for image conversion, the deep learning model that learned through GAF showed significantly higher classification accuracy. The results of this study suggest that the use of GAF and CNN models based on EEG results can be an effective way to increase objectivity and efficiency in diagnosing various mental disorders, including schizophrenia.