• Korean Journal of Ecology and Environment
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• v.35 no.3 s.99
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• pp.237-246
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• 2002

Tamjin Dam is built in the upper reaches of the Tamjin River which flows through the Janghung-gun and Gangjin-gun of the Jeollanamdo, Korea. In order to map out a preservation strategy of the fish community from dam construction, We studied the distribution of fish distribution and changes of the habitat environment. we found 49 fish species inhabiting in the downstream and upstream of the Tamjin Dam. Among them, migratory fish were two species sweet smelt, Plecoglossus altivelis and freshwater eel, Anguilla japonica. The Coreoperca kawamebari which designated as a species to be protected by The Ministry of Environment of Korea was also observed. After the dam construction, reservoir would be filled with water and running water system will change to standing water system. Then the habitat and spawning space for mountain torrent fish will be reduced and the migration of migratory fish to upstream will be blocked. Through our study, we proposed several ways to protect fish community. In order to preserve the reduced habitat and spawning area of mountain torrent fish, a fishway has been diagnosed to be built in the shallow reservoir in the entrance of the upriver. The establishment of artificial spawning ground on the riverside has been recommended. In addition, We propose a creation of a shelter for fresh water eel, Anguilla japonica in areas where the depth of the water is about l0m by laying rocks. Since it is difficult for a spawning ground to be formed naturally in the reservoir due to the year-round changes in water level, We suggested a floating spawning facility using an artificial fixture. In the downstream of the dam, a waterway-style habitat and spawning ground in the river and increasing the diversity and abundance of fish fauna in the Tamjin River. A low-cost and highly efficient operational fishway has been recommended so that migratory fish such as Plecoglossus altivelis (sweetfish) can migrate from the lower reaches to the upper reaches of the river.

• Journal of the Korean Geosynthetics Society
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• v.16 no.4
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• pp.231-240
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• 2017

Recently, there have been frequent occurrences of ground sink in the urban area, which have resulted in human and material damage and are accompanied by economic losses. This is caused by artificial factors such as soil loss, poor compaction, horizontal excavation due to the breakage of the aged sewage pipe, and lack of water proof at vertical excavation. The ground sink can be prevented by preliminary restoration and reinforcement through exploration, but it can be considered that it is not suitable for urgent restoration by the existing method. In this study, a model experiment was carried out to simulate the in-ground cavities caused by groundwater flow for developing non-excavation urgent restoration in underground cavity and the range of the relaxation zone was estimated by detecting the around the cavity using a relaxation zone detector. In addition, disturbance region and relaxation region were separated by injecting gypsum into cavity formed in simulated ground. The shape of the underground cavity due to the groundwater flow was similar to that of the failure mode III formed in the dense relative density ground due to water pipe breakage in the previous study. It was confirmed that the relaxed region detected using the relaxation zone detector is formed in an arch shape in the cavity top. The length ratio of the relaxation region to the disturbance region in the upper part of the cavity center is 2: 1, and it can be distinguished by the difference in the decrease of the shear resistance against the external force. In other words, it was confirmed that the secondary damage should not occur in consideration of the expandability of the material used as the injecting material in the pre-repair and reinforcement, and various ground deformation states will be additionally performed through additional experiments.

• The Journal of Engineering Geology
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• v.7 no.3
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• pp.191-205
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• 1997

Measurements on subsurface movement of the Songsanri tomb site including the Muryong royal tomb was conducted using a tiltmeter system for the period of 15 months form July 7, 1996 to September 30, 1997. Two coordinate tilt monitoring data shows the biggest movement rate of 2.3mm/m/yr toward south in the frontal wall(N-S tilt) of the Muryong royal tomb. Southward tilting of bricks above the southern fire place in the western wall of the Muryong royal tomb is a proof of southward tilting of the royal tomb since its excavation in 1971. The eastern wall of the Muryong royal tomb is also tilting toward inside the tomb with the rate of 1.523mm/m/yr. Furthermore, tilting rate of wall increases twice in rainy season. It is interpreted tbat infiltration of water into the tomb and nearby ground in rainy season results in dangerous status for the safety of tomb structure. On the whole, normal component tilting of the walls of the 5th tomb is large than its shear component. It shows a small displacement toward one direction without no abrupt change in its direction and amount of tilting. The tilting rate of walls of the 6th tomb is about 8.8mm/m/yr in the dry season which is much bigger than those of other tombs in rainy season. Deformation events of walls of the tombs are closely related to amount of precipitation and variation of temperature. In comparison with different weather conditions, tilting is much bigger during the period of rainy weather than sunny weather. It is interpreted that rainwater flew into the turm through faults and nearby ground. High water content in nearby ground resulted strength of ground. The tilting event of walls shows a hysterisis phenomenon in analysis of temperature effect on tilting event. The walls tilt rapidly with steep rising of temperature, but the tilted walls do not come back to original position with temperature falling. Therefore, a factor of steep increase of the temperature must be removed. It means the tomb have to be kept with constant temperature. The observation of groundwater level using three boreholes located in construction site and original ground represented that groundwater level in construction site is higher than that of original ground during the rainy season from the end of June to August. It means that the drainage system of the Muryong royal tomb is worse than original ground, and it is interpreted that the poor drainage system is related to safety of tomb structure. As above mentioned, it is interpreted that artificial changes of the tomb environment since the excavation, infiltration of rainwater and groundwater into the tomb site and poor drainage system had resulted in dangerous situation for the tomb structure. According to the result of the long period observation for the tomb site, it is interpreted that protection of the tomb site from high water content should be carried out at first, and the rise of temperature by means of the dehumidifier inside the tomb must be removed.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.45 no.9
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• pp.794-806
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• 2017

It is essential to protect the national space assets and space environment safely as a space development country from the continuously increasing space debris. And Active Debris Removal(ADR) is the most active way to solve this problem. In this paper, we studied the Artificial Neural Network(ANN) for a stable recognition model of vision-based space debris tracking system. We obtained the simulated image of the space environment by the KARICAT which is the ground-based space debris clearing satellite testbed developed by the Korea Aerospace Research Institute, and created the vector which encodes structure and color-based features of each object after image segmentation by depth discontinuity. The Feature Vector consists of 3D surface area, principle vector of point cloud, 2D shape and color information. We designed artificial neural network model based on the separated Feature Vector. In order to improve the performance of the artificial neural network, the model is divided according to the categories of the input feature vectors, and the ensemble technique is applied to each model. As a result, we confirmed the performance improvement of recognition model by ensemble technique.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.2B
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• pp.155-163
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• 2011

In this study, a soil moisture estimation model was developed using a decision tree model, an artificial neural networks (ANN) model, remotely sensed data, and ground network data of daily precipitation, soil moisture and surface temperature. Soil moisture data of the Yongdam dam basin (5 sites) were used for model validation. Satellite remote sensing data and geographical data and meteorological data were used in the classification and regression tree (CART) model for data classification and the ANNs model was applied for clustered data to estimate soil moisture. Soil moisture data of Jucheon, Bugui, Sangjeon, Ahncheon sites were used for training and the correlation coefficient between soil moisture estimates and observations was between 0.92 to 0.96, root mean square error was between 1.00 to 1.88%, and mean absolute error was between 0.75 to 1.45%. Cheoncheon2 site was used for validation. Test statistics showed that the correlation coefficient, the root mean square error, the mean absolute error were 0.91, 3.19%, and 2.72% respectively. Results demonstrated that the developed soil moisture model using CART and ANN was able to apply for the estimation of soil moisture distribution.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.2
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• pp.227-235
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• 2018

In this study, we developed a relational formula for observing high - resolution rainfall using vehicle rain sensor. The vehicle rain sensor consists of eight channels. Each channel generates a sensor signal by detecting the amount of rainfall on the windshield of the vehicle when rainfall occurs. The higher the rainfall, the lower the sensor signal is. Using these characteristics of the sensor signal generated by the rain sensor, we developed a relational expression. In order to generate specific rainfall, an artificial rainfall generator was constructed and the change of the sensor signal according to the variation of the rainfall amount in the artificial rainfall generator was analyzed. Among them, the optimal sensor channel which reflects various rainfall amounts through the sensitivity analysis was selected. The sensor signal was generated in 5 minutes using the selected channel and the representative values of the generated 5 - minute sensor signals were set as the average, 25th, 50th, and 75th quartiles. The calculated rainfall values were applied to the actual rainfall data using the constructed relational equation and the calculated rainfall amount was compared with the rainfall values observed at the rainfall station. Although the reliability of the relational expression was somewhat lower than that of the data of the verification result data, it was judged that the experimental data of the residual range was insufficient. The rainfall value was calculated by applying the developed relation to the actual rainfall, and compared with the rainfall value generated by the ground rainfall observation instrument observed at the same time to verify the reliability. As a result, the rain sensor showed a fine rainfall of less than 0.5 mm And the average observation error was 0.36mm.

• Journal of the Korean Geotechnical Society
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• v.18 no.5
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• pp.37-42
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• 2002

Artificial neural networks are efficient computing techniques that are widely used to solve complex problems in many fields. In this paper a liquefaction potential was estimated by using a back propagation neural network model applicated to cyclic triaxial test data, soil parameters and site investigation data. Training and testing of the network were based on a database of 43 cyclic triaxial test data from 00 sites. The neural networks are trained by modifying the weights of the neurons in response to the errors between the actual output values and the target output value. Training was done iteratively until the average sum squared errors over all the training patterns were minimized. This generally occurred after about 15,000 cycles of training. The accuracy from 72% to 98% was shown for the model equipped with two hidden layers and ten input variables. Important effective input variables have been identified as the NOC,$D_10$ and (N$_1$)$_60$. The study showed that the neural network model predicted a CSR(Cyclic shear stress Ratio) of silty-sand reasonably well. Analyzed results indicate that the neural-network model is more reliable than simplified method using N value of SPT.

• Journal of the Korean Geotechnical Society
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• v.22 no.6
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• pp.15-26
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• 2006

Although numerous investigations have been performed over the years to predict the behavior and bearing capacity of piles, the mechanisms are not yet entirely understood. The prediction of bearing capacity is a difficult task, because large numbers of factors affect the capacity and also have complex relationship one another. Therefore, it is extremely difficult to search the essential factors among many factors, which are related with ground condition, pile type, driving condition and others, and then appropriately consider complicated relationship among the searched factors. The present paper describes the application of Artificial Neural Network (ANN) in predicting the capacity including its components at the tip and along the shaft from dynamic load test of the driven piles. Firstly, the effect of each factor on the value of bearing capacity is investigated on the basis of sensitivity analysis using ANN modeling. Secondly, the authors use the design methodology composed of ANN and genetic algorithm (GA) to find optimal neural network model to predict the bearing capacity. The authors allow this methodology to find the appropriate combination of input parameters, the number of hidden units and the transfer structure among the input, the hidden and the out layers. The results of this study indicate that the neural network model serves as a reliable and simple predictive tool for the bearing capacity of driven piles.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.3
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• pp.71-79
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• 2023

It is not efficient to install a maintenance system that measures seismic acceleration and displacement on all bridges and buildings to evaluate the safety of structures after an earthquake occurs. In order to maintain this, an on-site investigation is conducted. Therefore, it takes a lot of time when the scope of the investigation is wide. As a result, secondary damage may occur, so it is necessary to predict the safety of individual structures quickly. The method of estimating earthquake damage of a structure includes a finite element analysis method using approved seismic information and a structural analysis model. Therefore, it is necessary to predict the seismic information generated at arbitrary location in order to quickly determine structure damage. In this study, methods to predict the ground response spectrum and acceleration time history at arbitrary location using linear estimation methods, and artificial neural network learning methods based on seismic observation data were proposed and their applicability was evaluated. In the case of the linear estimation method, the error was small when the locations of nearby observatories were gathered, but the error increased significantly when it was spread. In the case of the artificial neural network learning method, it could be estimated with a lower level of error under the same conditions.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1437-1449
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• 2023

The purpose of this study is to analyze whether pervious and impervious areas in urban areas affect tree growth. In order to determine the differences in the growth of six species of trees planted simultaneously, the effects of pervious and impervious surfaces on tree growth were analyzed using the Normalized Difference Vegetation Index (NDVI) produced using Sentinel-2 and sub-divided land cover map from the Ministry of Environment. For this purpose, the Geospatial eXplainable Artificial Intelligence(GeoXAI) concept was applied. As a result of the analysis, the explanatory power of the model was found to be the best when considering the area of land cover included in the 10m range for Pinus densiflora, the 20 m range for Zelkova Serrata, Metasequoia glyptostroboides, and Ginkgo biloba, the 30 m range for Platanus occidentalis, and the 40 m range for Yoshino cherry trees. In addition, the wider the pervious area, the more active the growth of trees,showing a positive correlation, and the wider the impervious area, such as nearby artificial ground, showed a negative correlation with tree growth. This shows that surrounding pervious and impervious areas affect the growth of trees and that the scope of influence varies depending on the tree species.