• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.3
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• pp.2234-2241
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• 2015

This study has been undertaken to develop a relationship between the shear strength coefficients and the cone index. The theoretic mathematical equations for the relationship were rigorously investigated, and then a Artificial Neural Network(ANN) analysis was adapted to enhance the reliability of the investigation. The theoretical investigation involved various assumptions resulting in the significant error involvement of geotechnical behaviors of ground. Therefore, a model using the ANN has been learned to enhance the prediction of the cone index form the shear strength parameters. Site investigation reports from various construction fields were used for ANN model learning. The results of the study show that the model predicts the cone index from the shear strength parameters of soils very well. The further study that is undertaking has a potential promise of the generalized prediction technique for the cone index from the soil parameters.

• Korean Journal of Remote Sensing
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• v.26 no.1
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• pp.47-57
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• 2010

The aim of this study is to detect landslide using digital aerial photography and apply the landslide to landslide susceptibility mapping by artificial neural network (ANN) and geographic information system (GIS) at Jinbu area where many landslides have occurred in 2006 by typhoon Ewiniar, Bilis and Kaemi. Landslide locations were identified by visual interpretation of aerial photography taken before and after landslide occurrence, and checked in field. For landslide susceptibility mapping, maps of the topography, geology, soil, forest, lineament, and landuse were constructed from the spatial data sets. Using the factors and landslide location and artificial neural network, the relative weight for the each factors was determinated by back-propagation algorithm. As the result, the aspect and slope factor showed higher weight in 1.2-1.5 times than other factors. Then, landslide susceptibility map was drawn using the weights and finally, the map was validated by comparing with landslide locations that were not used directly in the analysis. As the validation result, the prediction accuracy showed 81.44%.

• KIEE International Transactions on Electrophysics and Applications
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• v.5C no.2
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• pp.63-66
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• 2005

This paper describes analysis and pattern recognition techniques for Partial Discharge(PD) signals in Gas Insulated Switchgears (GIS). Detection of PD signals is one of the most important factors in the predictive maintenance of GIS. One of the methods of detection is electro magnetic wave detection within the Ultra High Frequency (UHF) band (300MHz $\~$ 3GHz). In this paper, PD activity simulation is generated using three types of artificial defects, which were detected by a UHF PD sensor installed in the GIS. The detected PD signals were performed on three-dimensional phi-q-n analysis. Finally, parameters were calculated and an Artificial Neural Network (ANN) was applied for PD pattern recognition. As a result, it was possible to discriminate and classify the defects.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.2
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• pp.15-20
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• 2023

This study aims to enhance the accuracy of fine dust predictions by analyzing various factors within the local environment, in addition to atmospheric conditions. In the atmospheric environment, meteorological and air pollution data were utilized, and additional factors contributing to fine dust generation within the region, such as traffic volume and electricity transaction data, were sequentially incorporated for analysis. XGBoost, Random Forest, and ANN (Artificial Neural Network) were employed for the analysis. As variables were added, all algorithms demonstrated improved performance. Particularly noteworthy was the Artificial Neural Network, which, when using atmospheric conditions as a variable, resulted in an MAE of 6.25. Upon the addition of traffic volume, the MAE decreased to 5.49, and further inclusion of power transaction data led to a notable improvement, resulting in an MAE of 4.61. This research provides valuable insights for proactive measures against air pollution by predicting future fine dust levels.

• Structural Engineering and Mechanics
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• v.53 no.5
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• pp.939-956
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• 2015

In this study, in order to propose an efficient model to predict the torque capacity of steel fiber reinforced concrete (SFRC) beams, the existing experimental data related to torsional response of beams is reviewed. It is observed that existing data neglects the effects of some parameters on the variation of torque capacity. Thus, an experimental research was also conducted to obtain the effects of neglected parameters. In the experimental study, a total of seventeen SFRC beams are tested against torsion. The parameters considered in the experiments are concrete compressive strength, steel fiber aspect ratio, volumetric ratio of steel fibers and longitudinal reinforcement ratio. The effect of each parameter is discussed in terms of torque versus unit angle of twist graphs. The data obtained from this experimental research is also combined with the data got from previous studies and employed in artificial neural network (ANN) analysis to estimate the ultimate torque capacity of SFRC beams. In addition to parameters considered in the experiments, aspect ratio of beam cross-section, yield strengths of both transverse and longitudinal reinforcements, and transverse reinforcement ratio are also defined as parameters in ANN analysis due to their significant effects observed in previous studies. Assessment of the accuracy of ANN analysis in estimating the ultimate torque capacity of SFRC beams is performed by comparing the analytical and experimental results. Comparisons are conducted in terms of root mean square error (RMSE), mean absolute error (MAE) and coefficient of efficiency ($E_f$). The results of this study revealed that addition of steel fibers increases the ultimate torque capacity of reinforced concrete beams. It is also found that ANN is a powerful method and a feasible tool to estimate ultimate torque capacity of both normal and high strength concrete beams within the range of input parameters considered.

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.855-863
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• 2022

In this study, the Artificial Neural Network (ANN) was used to mapping air temperature in Seoul. MODerate resolution Imaging Spectroradiomter (MODIS) data was used as auxiliary data for mapping. For the ANN network topology optimizing, scatterplots and statistical analysis were conducted, and input-data was classified and combined that highly correlated data which surface temperature, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), time (satellite observation time, Day of year), location (latitude, hardness), and data quality (cloudness). When machine learning was conducted only with data with a high correlation with air temperature, the average values of correlation coefficient (r) and Root Mean Squared Error (RMSE) were 0.967 and 2.708℃. In addition, the performance improved as other data were added, and when all data were utilized the average values of r and RMSE were 0.9840 and 1.883℃, which showed the best performance. In the Seoul air temperature map by the ANN model, the air temperature was appropriately calculated for each pixels topographic characteristics, and it will be possible to analyze the air temperature distribution in city-level and national-level by expanding research areas and diversifying satellite data.

• Journal of Korea Water Resources Association
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• v.49 no.10
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• pp.877-885
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• 2016

In this study, the Standard Precipitation Index(SPI), meteorological drought index, was used to evaluate the temporal and spatial assessment of drought forecasting results for all cross Korea. For the drought forecasting, the Multi Layer Perceptron-Artificial Neural Network (MLP-ANN) was selected and the drought forecasting was performed according to different forecasting lead time for SPI (3) and SPI (6). The precipitation data observed in 59 gaging stations of Korea Meteorological Adminstration (KMA) from 1976~2015. For the performance evaluation of the drought forecasting, the binary classification confusion matrix, such as evaluating the status of drought occurrence based on threshold, was constituted. Then Receiver Operating Characteristics (ROC) score and F score according to conditional probability are computed. As a result of ROC analysis on forecasting performance, drought forecasting performance, of applying the MLP-ANN model, shows satisfactory forecasting results. Consequently, two-month and five-month leading forecasts were possible for SPI (3) and SPI (6), respectively.

• Structural Engineering and Mechanics
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• v.83 no.5
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• pp.671-680
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• 2022

The shear capacity of beams is an essential parameter in designing beams carrying shear loads. Precise estimation of the ultimate shear capacity typically requires comprehensive calculation methods. For steel fiber reinforced concrete (SFRC) beams, traditional design methods may not accurately predict the interaction between different parameters affecting ultimate shear capacity. In this study, artificial neural network (ANN) modeling was utilized to predict the ultimate shear capacity of SFRC beams using ten input parameters. The results demonstrated that the ANN with 30 neurons had the best performance based on the values of root mean square error (RMSE) and coefficient of determination (R²) compared to other ANN models with different neurons. Analysis of the ANN model has shown that the clear shear span to depth ratio significantly affects the predicted ultimate shear capacity, followed by the reinforcement steel tensile strength and steel fiber tensile strength. Moreover, a Genetic Algorithm (GA) was used to optimize the ANN model's input parameters, resulting in the least cost for the SFRC beams. Results have shown that SFRC beams' cost increased with the clear span to depth ratio. Increasing the clear span to depth ratio has increased the depth, height, steel, and fiber ratio needed to support the SFRC beams against shear failures. This study approach is considered among the earliest in the field of SFRC.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.96-103
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• 2018

An analysis using a statistical method is generally used to determine the effective temperature based on the temperature design load of a bridge. In this study, the effective temperature was calculated by building an artificial neural network (ANN) capable of improving the statistical method. A Steel box girder bridge specimen was made with a width of 2.0 m, height of 2.0 m, and length of 3.0 m and 0.2 m the upper slab. Twenty one temperature gauges were attached to measure the temperature between 2014 and 2016 for three years. An ANN was learned using the data measured from 2014~2015 and the results were compared with the Euro codes. The error rate between the Euro code and statistical analysis values was analyzed to be 4.1 % for the total measurement point. The ANN was verified and the effective bridge temperatures were calculated using the temperature data measured in 2016. The results revealed an approximate 3.97 % difference from the statistical analysis values. This degree of error is considered to be acceptable in terms of engineering for the analysis of an ANN. An ANN can easily predict the effective temperature of a bridge by knowing the input values of the region's highest temperature, bridge type, and upper asphalt thickness when designing the bridge's temperature loads.

• Journal of Advanced Marine Engineering and Technology
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• v.34 no.6
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• pp.914-921
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• 2010

When industrial structures are constructed on soft ground, ground subsidence is occurred by problems of bearing capacity. To protect ground subsidence have to improve soft ground, and have to predict settlement estimation for reasonable construction. Artificial Neural Networks(ANN) is adopted for prediction of settlement of construction during the initial design. In the study, Artificial Neural Networks are applied to predict the settlement estimation of initial condition ground and ground improved by D.C.M method. Also, this study compares results of Artificial Neural Networks and results of continuum analysis using Mohr-Coulomb models. In result, settlements of initial condition ground decreased over 0.7 times. Also, by comparing ANN and continuum analysis, coefficient of determination was comparatively high value 0.79. Thought this study, it was confirmed that settlements of improvement ground is predicted using laboratory experiment data.