• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.6
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• pp.34-41
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• 2020

In recent years, magnitude and frequency of earthquakes have increased in Korea. Damage to a bridge, which is one of the main infrastructures, can directly lead to considerable loss of human lives. Therefore, engineers need to evaluate the seismic fragility of the structure and prepare for the possible seismic damage. In particular, the number of aging bridges over 30 years of service increases, and thus the seismic analysis and fragility requires accounting for the aging and retrofit effects on the bridge. In this study, the nonlinear static and dynamic analyses were performed to evaluate the effects of the aging and FRP retrofit on a PSC bridge. The aging and FRP retrofit were applied to piers that dominate the response of the bridge during earthquakes. The maximum displacement of the bridge increased due to the aging of the pier but decreased when FRP retrofit applied to the aged pier. In addition, seismic fragility analysis was performed to evaluate the seismic behavior of the bridge combined with the seismic performance of the pier. Compared with the aged bridge, the FRP retrofit bridge showed a decrease in the seismic fragility in all levels of damage. The reduction of the seismic fragility in the FRP bridge was prominent as the value of PGA and level of damage increased.

• Proceedings of the Korea Concrete Institute Conference
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• 2008.11a
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• pp.101-104
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• 2008

Recently, the serious damage by earthquakes is increased around the world. SOC fo city is established to minimize the loss of lives and assets by earthquakes, which an objective standard is required. Generally, bridges damage by earthquakes occurred the inelastic hinge under the column. Nonlinear element model of inelastic hinge have been used to Bilinear model, but Takeda model for material characterization of concrete is a little. In this study, railway bridge was performed seismic fragility analysis for Takeda model and Bilinear model comparatively. This analysis shows that damage probability of Takeda model is larger than Bilinear model. And analysis of Takeda model in longitudinal direction and transverse direction are different. Therefore developed analysis for concrete column of bridge is expected to apply to material characterization.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.6
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• pp.401-409
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• 2020

Seismic fragility assessments include a procedure to combine the random variables of response and capacity to produce the relationship between failure probability and seismic intensity. The evaluation of the failure probability of simultaneous multiple failures of two or more components assumes that the failure probability of each component is independent of those of the others. However, a correlation is expected to exist because several random factors have the same cause. The multiple-failure probability can differ depending on this correlation and may be unconservative without considering the seismic correlation. Therefore, a practical methodology for fragility assessment should be evaluated using the seismic correlation and correlation coefficient for each random variable. In this study, several random variables were selected for numerical evaluation of the correlation coefficient. The correlation coefficient was then compared with each variable and the combined variables. The correlation coefficient using simplified and complex models were also compared to determine and analyze the differences between each of the approaches.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.2A
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• pp.119-130
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• 2009

Seismic fragility curves of a structure represent the probability of exceeding the prescribed structural damage state for a given various levels of ground motion intensity such as peak ground acceleration (PGA), spectral acceleration ($S_a$) and spectral displacement ($S_d$). So those are very essential to evaluate the structural seismic performance and seismic risk. The purpose of this paper is to develop seismic fragility curves for PSC box girder bridges. In order to construct numerical fragility curve of bridge structure using nonlinear time history analysis, a set of ground motions corresponding to design spectrum are artificially generated. Assuming a lognormal distribution, the fragility curve is estimated by using the methodology proposed by Shinozuka et al. PGA is simple and generally used parameter in fragility curve as ground motion intensity. However, the PGA has not good relationship with the inelastic structural behavior. So, $S_a$ and $S_d$ with more direct relationship for structural damage are used in fragility analysis as more useful intensity measures instead of PGA. The numerical fragility curves based on nonlinear time history analysis are compared with those obtained from simple method suggested in HAZUS program.

• Journal of the Korea Concrete Institute
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• v.20 no.1
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• pp.51-58
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• 2008

Analytical probabilistic vulnerability analysis requires extensive computing effort as a result of the randomness in both input motion and response characteristics. In this study, a new methodology whereby a set of vulnerability curves are derived based on the fundamental response quantities of stiffness, strength and ductility is presented. A response database of coefficients describing lognormal vulnerability relationships is constructed by employing aclosed-form solution for a generalized single-degree-of-freedom system. Once the three fundamental quantities of a wide range of structural systems are defined, the vulnerability curves for various limit states can be derived without recourse to further simulation. Examples of application are given and demonstrate the extreme efficiency of the proposed approach in deriving vulnerability relationships.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.28 no.3
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• pp.241-248
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• 2015

There are a lot of equipment related to safety and electric power production in nuclear power plants. The structure and equipment in NPPs were generally designed considering a high safety factor to remain in the elastic zone under earthquake load. However it is needed to revaluate the seismic capacity of the structure and equipment as the magnitude of earthquake was recently increased. In this study the floor response due to the nonlinear behaviors of structure was analyzed and the inelastic structural response factor was calculated by the nonlinear time history analysis. The inelastic structural response factor was calculated by the EPRI method and the nonlinear analysis method to realistically evaluate the seismic fragility for the equipment. According to the analysis result, it was represented that the inelastic structural response factor was affected by the natural frequency of equipment, the location of equipment and the dynamic property of structure.

• Journal of Korean Association for Spatial Structures
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• v.19 no.2
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• pp.4-7
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• 2019

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.6
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• pp.177-184
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• 2021

In recent years, large-scale earthquake activity has occurred in Korea, and thus public interest in earthquakes is increasing. Accordingly, the importance of seismic performance management of structures is emerging. In particular, the collapse of a bridge, one of main road facilities, directly leads to many casualties. Therefore, engineers need to evaluate the seismic fragility of the bridge and prepare for the earthquake event. The service life of these bridges has been over 30 years, which requires a study on the aging effect on bridges. In this study, seismic analysis of the target RC slab bridge was performed considering the aging effects of each component of the bridge. Components of the bridge included pier and bearing, which dominate the seismic response of the bridge. The seismic performance of the bridge was evaluated using nonlinear static and dynamic analyses. In addition, the limit state and dynamic response of each component were used to evaluate the seismic fragility according to the aging of each component.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.5
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• pp.533-541
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• 2021

Retaining walls have been used to prevent slope failure through resistance of earth pressure in railway, road, nuclear power plant, dam, and river infrastructure. To calculate dynamic earth pressure and determine the characteristics for seismic behavior, many researchers have analyzed the nonlinear response of ground and structure based on various numerical analyses (FLAC, PLAXIS, ABAQUS etc). In addition, seismic fragility evaluation is performed to ensure safety against earthquakes for structures. In this study, we used the FLAC2D program to understand the seismic response of the inverted T-type wall with a backfill slope, and evaluated seismic fragility based on relative horizontal displacements of the wall. Nonlinear site response analysis was performed for each site (S2 and S4) using the seven ground motions to calculate various seismic loadings reflecting site characteristics. The numerical model was validated based on other numerical models, experiment results, and generalized formula for dynamic active earth pressure. We also determined the damage state and damage index based on the height of retaining wall, and developed the seismic fragility curves. The damage probabilities of the retaining wall for the S4 site were computed to be larger than those for the S2 site.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.159-165
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• 2023

While the frequency of seismic occurrence has been increasing recently, the domestic seismic response system is weak, the objective of this research is to compare and analyze the seismic vulnerability of buildings using statistical analysis and machine learning techniques. As the result of using statistical technique, the prediction accuracy of the developed model through the optimal scaling method showed about 87%. As the result of using machine learning technique, because the accuracy of Random Forest method is 94% in case of Train Set, 76.7% in case of Test Set, which is the highest accuracy among the 4 analyzed methods, Random Forest method was finally chosen. Therefore, Random Forest method was derived as the final machine learning technique. Accordingly, the statistical analysis technique showed higher accuracy of about 87%, whereas the machine learning technique showed the accuracy of about 76.7%. As the final result, among the 22,296 analyzed building data, the seismic vulnerabilities of 1,627(0.1%) buildings are expected as more dangerous when the statistical analysis technique is used, 10,146(49%) buildings showed the same rate, and the remaining 10,523(50%) buildings are expected as more dangerous when the machine learning technique is used. As the comparison of the results of using advanced machine learning techniques in addition to the existing statistical analysis techniques, in spatial analysis decisions, it is hoped that this research results help to prepare more reliable seismic countermeasures.