• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.7
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• pp.33-38
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• 2017

The number of damaged lifeline structures have been increasing with urban acceleration under earthquakes. To predict the damage, damage mitigation technology of lifeline structures should be analyzed using damage prediction technology. Therefore, in this paper, the degree of the fragility of structures under an earthquake was evaluated stochastically through an evaluation of the seismic fragility. The aim was to develop damage prediction technology of sewage pipes among the lifeline facilities. The site response was performed using the data from 158 boreholes in Seoul and 7 real earthquake waves to determine the responses in real urban areas. The seismic fragility was deduced through a total of 29822 time history analysis. In addition, sewer pipes were evaluated and the persisting period was passed by applying the research results of strength reduction which is due to sulphate erosion. As a result, the difference in failure probability between 300 and 800 with the smaller diameter of the representative pipes was approximately double and the size of the pipes has a significant effect on the seismic fragility function. Moreover, the failure probability of a seismic load increases by up to 10 fold as the strength reduction rate increases. The results of this study can be used as a means of predicting the damage and countermeasures of sewer pipes and might be reflected in the seismic design of underground facilities.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.25 no.6
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• pp.412-421
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• 2013

Nowadays, small and medium-sized earthquakes occur frequently in the west coast of Korea. The earthquake induced damages on the harbor structure such as quay wall possibly make a severe impact on national economy. Therefore, not only a seismic design for the structures but warning system for seismic damage right after the occurrence of earthquake should be developed. In this study, seismic fragility analysis was performed to be given to earthquake damage prediction system for quay wall structures in Busan and Incheon harbor. Four types of structures such as pier-type, caisson type, counterfort type, block-type were analyzed and fragility curves of functional performance level and collapse prevention level based on displacement criteria were found. Regression analyses by using the results of the two ports were done for possible use in other port structures.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.2A
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• pp.351-361
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• 2006

In this study, nonlinear finite element analysis procedures are presented for the seismic performance assessment of circular reinforced concrete bridge piers with confinement steel. This paper defines a damage index based on the predicted hysteretic behavior of a circular reinforced concrete bridge pier. Damage indices aim to provide a means of quantifying numerically the damage in circular reinforced concrete bridge piers sustained under earthquake loading. The proposed numerical method is applied to circular reinforced concrete bridge piers with confinement steel tested by the authors. The proposed numerical method gives a realistic prediction of seismic performance throughout the loading cycles for several test specimens investigated.

• Journal of the Earthquake Engineering Society of Korea
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• v.14 no.2
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• pp.49-56
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• 2010

In general, the seismic intensity deduced from instrumental data has been evaluated from the empirical relation between the intensity and the PGA. From the point of view that the degree of earthquake damage is more closely associated with the seismic intensity than with the observed PGA, JMA developed the instrumental seismic intensity (JMA instrumental intensity) meter that estimate the real-time seismic intensity from the observed strong motion data to obtain a more correct estimate of earthquake damage. The purpose of the present study is to propose a practical application of the JMA instrumental intensity in Korea. Since the occurrence of strong earthquakes is scarce in the Korean Peninsula, there is an insufficiency of strong motion data. As a result, strong motion data were synthesized by a stochastic procedure to satisfy the characteristics of a seismic source and crustal attenuation of the Peninsula. Six engineering ground motion parameters, including the JMA instrumental intensity, were determined from the synthesized strong motion data. The empirical relations between the ground motion parameters were then analyzed. Cluster analysis to classify the parameters into groups was also performed. The result showed that the JMA acceleration ($a_0$) could be classified into similar group with the spectrum intensity and the relatively distant group with the CAV (Cumulative Absolute Velocity). It is thought that the $a_0$ or JMA intensity can be used as an alternative criterion in the evaluation of seismic damage. On the other hand, attenuation relation equations for PGA and $a_0$ to be used in the prediction of seismic hazard were derived as functions of the moment magnitude and hypocentral distance.

• Journal of Digital Contents Society
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• v.19 no.4
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• pp.771-777
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• 2018

This study proposes a big data based risk analysis framework to analyze more comprehensive disaster risk and vulnerability. We introduce a distributed and parallel framework that allows large volumes of data to be processed in a short time by using open-source disaster risk assessment tool. A performance analysis of the proposed system presents that it achieves a more faster processing time than that of the existing system and it will be possible to respond promptly to precise prediction and contribute to providing guideline to disaster countermeasures. Proposed system is able to support accurate risk prediction and mitigate severe damage, therefore will be crucial to giving decision makers or experts to prepare for emergency or disaster situation, and minimizing large scale damage to a region.

• Smart Structures and Systems
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• v.21 no.4
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• pp.521-535
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• 2018

The main purpose of this paper is to predict missing absolute out-of-plane displacements and failure limits of infill walls by artificial neural network (ANN) models. For this purpose, two shake table experiments are performed. These experiments are conducted on a 1:1 scale one-bay one-story reinforced concrete frame (RCF) with an infill wall. One of the experimental models is composed of unreinforced brick model (URB) enclosures with an RCF and other is composed of an infill wall with bed joint reinforcement (BJR) enclosures with an RCF. An artificial earthquake load is applied with four acceleration levels to the URB model and with five acceleration levels to the BJR model. After a certain acceleration level, the accelerometers are detached from the wall to prevent damage to them. The removal of these instruments results in missing data. The missing absolute maximum out-of-plane displacements are predicted with ANN models. Failure of the infill wall in the out-of-plane direction is also predicted at the 0.79 g acceleration level. An accuracy of 99% is obtained for the available data. In addition, a benchmark analysis with multiple regression is performed. This study validates that the ANN-based procedure estimates missing experimental data more accurately than multiple regression models.

• Earthquakes and Structures
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• v.13 no.1
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• pp.79-87
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• 2017

Seismic analysis of local site conditions is fundamental for a reliable site seismic hazard assessment. It plays a major role in mitigation of seismic damage potential through the prediction of surface ground motion in terms of amplitude, frequency content and duration. Such analysis requires the determination of the transfer function, which is a simple tool for characterizing a soil profile by estimating its vibration frequencies and its amplification potential. In this study, numerical simulations are carried out and are then combined with a statistical study to allow the characterization of design sites classified by the Algerian Building Seismic Code (RPA99, ver 2003), by average transfer functions. The mean transfer functions are thereafter used to compute RPA99 average site factors. In this regard, coming up seismic fields are simulated based on Power Spectral Density Functions (PSDF) defined at the rock basement. Results are also used to compute average site factor where, actual and synthetic time histories are introduced. In absence of measurement data, it is found that the proposed approach can be used for a better soil characterization.

• Journal of the Earthquake Engineering Society of Korea
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• v.1 no.2
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• pp.1-15
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• 1997

This paper presents substructural identification methods for the assessment of local damages in complex and large structural systems. For this purpose, an auto-regressive and moving average with stochastic input (ARMAX) model is derived for a substructure to process the measurement data impaired by noises. Using the substructural methods, the number of unknown parameters for each identification can be significantly reduced, hence the convergence and accuracy of estimation can be improved. Secondly, the damage index is defined as the ratio of the current stiffness to the baseline value at each element for the damage assessment. The indirect estimation method was performed using the estimated results from the identification of the system matrices from the substructural identification. To demonstrate the proposed techniques, several simulation and experimental example analyses are carried out for structural models of a 2-span truss structure, a 3-span continuous beam model and 3-story building model. The results indicate that the present substructural identification method and damage estimation methods are effective and efficient for local damage estimation of complex structures.

• Korean Journal of Artificial Intelligence
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• v.10 no.1
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• pp.1-7
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• 2022

In this paper, a study was conducted to predict natural disasters in Afghanistan based on machine learning. Natural disasters need to be prepared not only in Korea but also in other vulnerable countries. Every year in Afghanistan, natural disasters(snow, earthquake, drought, flood) cause property and casualties. We decided to conduct research on this phenomenon because we thought that the damage would be small if we were to prepare for it. The Azure Machine Learning Studio used in the study has the advantage of being more visible and easier to use than other Machine Learning tools. Decision Forest is a model for classifying into decision tree types. Decision forest enables intuitive analysis as a model that is easy to analyze results and presents key variables and separation criteria. Also, since it is a nonparametric model, it is free to assume (normality, independence, equal dispersion) required by the statistical model. Finally, linear/non-linear relationships can be searched considering interactions between variables. Therefore, the study used decision forest. The study found that overall accuracy was 89 percent and average accuracy was 97 percent. Although the results of the experiment showed a little high accuracy, items with low natural disaster frequency were less accurate due to lack of learning. By learning and complementing more data, overall accuracy can be improved, and damage can be reduced by predicting natural disasters.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.547-548
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• 2023

내진 설계가 되어있지 않은 건물의 경우, 지진으로 인해 건물 붕괴 가능성이 높아지며 이로 인해 많은 인명 피해가 발생할 수 있다. 지진으로 인한 건물의 피해를 예측하고 이를 기반으로 취약점을 보완한다면 인명 피해를 줄일 수 있으므로 건물 피해 예측 모델에 대한 연구가 필요하다. 본 논문에서는 2015 년 네팔 대지진으로 인해 손상된 건물 데이터를 활용하여 Random Forest 와 Extreme Gradient Boosting 기계학습 분류 알고리즘을 사용하여 지진 피해 예측 모델의 정확도를 비교하였다.