• Journal of the Earthquake Engineering Society of Korea
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• v.13 no.2
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• pp.29-36
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• 2009

The present study emphasizes a nonlinear model to predict the shear behaviour of reinforced concrete interior beam-column joints. To model the shear behaviour of a panel zone in the beam-column joint, a modified softened truss model theory for in-plane shear prediction was introduced. This relationship was changed to define the characteristics for the rotational spring to represent the shear deformation in the joint by an equivalent moment-rotation relationship from the joint equilibrium. The analysis model was compared with experiments on reinforced concrete interior beam-column joints that were subjected to axial and shear forces, and the current model was found to accurately predict not only the shear force but also the shear deformation in the joint.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.04a
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• pp.19-26
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• 2002

In this paper, the nonlinear structural analysis for the composite structure of the spent nuclear fuel disposal container and the 50cm thick bentonite buffer is carried out to predict the collapse of the container while the sudden rock movement of 10cm is applied on the composite structure. This sudden rock movement is anticipated by the earthquake etc. at a deep underground. Horizontal symmetric rock movement is assumed in this structural analysis. Elastoplastic material model is adopted. Drucker-Prager yield criterion is used for the material yield prediction of the bentonite buffer and von-Mises yield criterion is used for the material yield prediction of the container(cast iron insert, copper outer shell and lid and bottom). Analysis results show that even though very large deformations occur beyond the yield point in the bentonite buffer, the container structure still endures elastic small strains and stresses below the yield strength. Hence, the 50cm thick bentonite buffer can protect the container safely against the 10cm sudden rock movement by earthquake etc.. Analysis results also show that bending deformations occur in the container structure due to the shear deformation of the bentonite buffer. The elastoplastic nonlinear structural analysis for the composite structure of the container and the bentonite buffer is performed using the finite element analysis code, NISA.

• Transactions of Materials Processing
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• v.12 no.5
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• pp.479-486
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• 2003

This paper presents an elastoplastic analysis for spent nuclear fuel disposal container and its 50 cm thick bentonite buffer to predict the collapse of the container while the horizontal asymmetric sudden rock movement of 10 cm is applied on the composite structure. This sudden rock movement is anticipated by the earthquake etc. at a deep underground. Elastoplastic material model is adopted. Drucker-Prager yield criterion is used for the material yield prediction of the bentonite buffer and von-Mises yield criterion is used for the material yield prediction of the container. Analysis results show that even though very large deformations occur beyond the yield point in the bentonite buffer, the container structure still endures elastic small strains and stresses below the yield strength. Hence, the asymmetric 50 cm thick bentonite buffer can protect the container safely against the 10 cm sudden rock movement by earthquake etc.. Analysis results also show that bending deformations occur in the container structure due to the shear deformation of the bentonite buffer. The finite element analysis code, NISA, is used for the analysis.

• Journal of the Earthquake Engineering Society of Korea
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• v.28 no.2
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• pp.113-119
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• 2024

Existing reinforced concrete buildings with seismically deficient column details affect the overall behavior depending on the failure type of column. This study aims to develop and validate a machine learning-based prediction model for the column failure modes (shear, flexure-shear, and flexure failure modes). For this purpose, artificial neural network (ANN), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) models were used, considering previously collected experimental data. Using four machine learning methodologies, we developed a classification learning model that can predict the column failure modes in terms of the input variables using concrete compressive strength, steel yield strength, axial load ratio, height-to-dept aspect ratio, longitudinal reinforcement ratio, and transverse reinforcement ratio. The performance of each machine learning model was compared and verified by calculating accuracy, precision, recall, F1-Score, and ROC. Based on the performance measurements of the classification model, the RF model represents the highest average value of the classification model performance measurements among the considered learning methods, and it can conservatively predict the shear failure mode. Thus, the RF model can rapidly predict the column failure modes with simple column details.

• Journal of the Korean Society of Hazard Mitigation
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• v.8 no.6
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• pp.73-79
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• 2008

Economic growth, industrialization and urbanization have made society more vulnerable than ever to seismic hazard in Korea. Although Korea has not experienced severe damage due to earthquakes during the last few decades, there is little doubt of the potential for large earthquakes in Korea as documented in the historical literature. As we see no immediate promise of short-term earthquake prediction with current science and technology, earthquake early warning systems attract more and more attention as a practical measure to mitigate damage from earthquakes. Earthquake early warning systems provide a few seconds to tens of seconds of warning time before the onset of strong ground shaking. To achieve rapid earthquake location, we propose to take full advantage of information from existing seismic networks; by using P wave arrival times at two nearest stations from the earthquake hypocenter and also information that P waves have not yet arrived at other stations. Ten earthquakes in the Korean peninsula and its vicinity are selected for the feasibility study. We observed that location results are not reliable when earthquakes occur outside of the seismic network. Earthquakes inside the seismic network, however, can be located very rapidly for the purpose of earthquake early warning. Seoul metropolitan area may secure $10{\sim}50$ seconds of warning time before any strong shaking starts for certain events. Carefully orchestrated actions during the given warning time should be able to reduce hazard and mitigate damages due to potentially disastrous earthquakes.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.5
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• pp.477-484
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• 2012

Recently, as earthquake is more frequently taking place around the world due to diastrophism, the importance of diastrophism and disaster detection is becoming more important. In this study, to analyze the interpretation of seismic displacement by the Japanese earthquake in March, 2011, and monitor the diastrophism of plates in Japan and surrounding Eurasia, Pacific, and Philippines before and after the earthquake, the observational data from IGS observatories in Japan and Asian regions were processed by precise point positioning. The displacement was biggest in MIZU, which was the closest to the epicenter, and the earthquake-affected region was in inverse proportion to the distance from the epicenter. The result of calculating the diastrophism speed before and after the earthquake, based on precise point positioning of IGS observatories located in the 4 plates around Japan, showed that the displacement speed changed and different plates showed different results. The comparison with the plate fate model allowed to analyze the change in diastrophism by earthquake, and to understand the characteristics of the displacement of the plates around Japan. Later, a continuous diastrophism monitoring based on GPS is needed for earthquake prediction and diastrophism research, and the data gained by continuous GPS-based monitoring of diastrophism will be fully used as basic data for relevant research and earthquake disaster management.

• International Journal of Contents
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• v.12 no.1
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• pp.49-53
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• 2016

"Hahoe Village" in Andong region is an UNESCO World Heritage Site. It should be protected against various disasters such as fire, flooding, earthquake, etc. Among these disasters, flooding has drastic impact on the lives and properties in a wide area. Since "Hahoe Village" is adjacent to Nakdong River, it is important to monitor the water level near the village. In this paper, we developed a hydrological modelling using multi-layer perceptron (MLP) to predict the water level of Nakdong River near "Hahoe Village". To develop the prediction model, error back-propagation (EBP) algorithm was used to train the MLP with water level data near the village and rainfall data at the upper reaches of the village. After training with data in 2012 and 2013, we verified the prediction performance of MLP with untrained data in 2014.

• Magazine of the Korean Society of Agricultural Engineers
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• v.42 no.5
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• pp.144-150
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• 2000

This study dealt with the comparison of lateral baring capacity for vertical PHC pile between predicted and measured values driven in weathered granite soils to build world cup gymnasium in Kwangju area. Recently, the calculation of horizontal bearing capacity of piles foundation has been considered very important for earthquake or wind resistant design in Korea. During this study , Matlock & Reese, Broms and Chang's methods were selected in prediction of lateral resistant of PHC piles. As for case study, the prediction values were compared with 5 measured ones based on ASTM. The result showed that prediction values proposed by Matlock & Reese , Chang and Broms were smaller that real values. Three proposed methods by Matlock & Reese and Chang based on lateral deflection and Broms by ultimated lateral resistance turned out valid in view of engineering practice.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.03a
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• pp.146-153
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• 2009

One dimensional site response analysis is widely used in prediction of the ground motion that is induced by earthquake. Equivalent linear analysis is the most widely used method due to its simplicity and ease of use. However, the equivalent linear method has been known to be unreliable since it approximates the nonlinear soil behavior within the linear framework. To consider the nonlinearity of the ground at frequency domain, frequency dependent algorithms that can simulate shear strain - frequency dependency have been proposed. In this study, the results of the modified equivalent linear analysis are compared to evaluate the degree of improvement and the applicability of the modified algorithms. Results show the novel smoothed curve that is proposed by this study indicates the most stable prediction and can enhance the accuracy of the prediction.

• International conference on construction engineering and project management
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• 2005.10a
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• pp.913-916
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• 2005

This paper presents the development of Artificial Neural Network models for forecasting the cost and contract duration of school reconstruction projects to assist the planners' decision-making in the early stage of the projects. 132 schools reconstruction projects in central Taiwan, which received the most serious damage from the Chi-Chi Earthquake, were collected. The developed Artificial Neural Network prediction models demonstrate good prediction abilities with average error rates under 10% for school reconstruction projects. The analytical results indicate that the Artificial Neural Network model with back-propagation learning is a feasible method to produce accurate prediction results to assist planners' decision-making process.