• 한국지반환경공학회 논문집
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• 제23권2호
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• pp.13-23
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• 2022

교량 기초의 지반-구조물 상호작용은 지진 시 교량의 거동에 영향을 미치는 주요한 요인으로 지적되어 왔다. 본 연구에서는 지반의 특성 및 기초의 특성이 교량 기초의 지진취약도에 미치는 영향을 분석하였다. 지반의 특성 변화 및 기초의 크기 변화를 고려한 등가정적해석 결과, 상재하중이 작용하는 경우 같은 수준의 횡방향 변위를 발생시키기 위해 요구되는 하중이 증가되는 것을 확인할 수 있었으며, 비선형성은 상재하중이 없는 경우가 더 큰 것으로 나타났다. 느슨한 지반에서 조밀한 지반으로 갈수록 기초의 크기가 증가할수록 동일한 변위를 발생시키기 위해 더 큰 하중을 필요로 하는 것으로 나타났다. 또한, 교량의 지진취약도를 합리적으로 획득하기 위한 접근법을 도출하기 위하여 교량 기초의 지진취약도를 4가지의 조건(고정단 조건, 도로교 설계기준-등가선형강성, 상재하중 고려 시 및 미고려 시 비선형 강성)을 고려하여 비교하였다. 단주교각에 대한 지진해석은 Opensees를 이용하여 수행하였다. 지진취약도 분석 결과, 보수적인 접근법으로 확대기초는 고정단으로 고려할 수 있으며, 말뚝기초의 크기가 작은 경우는 고정단으로 고려하여 안전측 설계를 검토할 수 있으나, 말뚝의 크기가 대형화 하는 경우는 비경제적인 설계가 될 수 있으므로, 지반조건에 따라 기초의 강성을 평가할 수 있는 도로교 등가 선형 스프링 강성을 고려하는 것이 합리적인 접근법으로 판단된다.

• Earthquakes and Structures
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• 제10권1호
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• pp.141-161
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• 2016

In seismic fragility and risk analysis, the definition of structural limit state (LS) capacities is of crucial importance. Traditionally, LS capacities are defined according to design code provisions or using deterministic pushover analysis without considering the inherent randomness of structural parameters. To assess the effects of structural randomness on LS capacities, ten structural parameters that include material strengths and gravity loads are considered as random variables, and a probabilistic pushover method based on a correlation-controlled Latin hypercube sampling technique is used to estimate the uncertainties in LS capacities for four typical reinforced concrete frame buildings. A series of ten LSs are identified from the pushover curves based on the design-code-given thresholds and the available damage-controlled criteria. The obtained LS capacities are further represented by a lognormal model with the median $m_C$ and the dispersion ${\beta}_C$. The results show that structural uncertainties have limited influence on $m_C$ for the LSs other than that near collapse. The commonly used assumption of ${\beta}_C$ between 0.25 and 0.30 overestimates the uncertainties in LS capacities for each individual building, but they are suitable for a building group with moderate damages. A low uncertainty as ${\beta}_C=0.1{\sim}0.15$ is adequate for the LSs associated with slight damages of structures, while a large uncertainty as ${\beta}_C=0.40{\sim}0.45$ is suggested for the LSs near collapse.

• Structural Engineering and Mechanics
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• 제83권3호
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• pp.327-340
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• 2022

The Collapse Margin Ratio (CMR) is a notable index used for seismic assessment of the structures. As proposed by FEMA P695, a set of analyses including the Nonlinear Static Analysis (NSA), Incremental Dynamic Analysis (IDA), together with Fragility Analysis, which are typically time-taking and computationally unaffordable, need to be conducted, so that the CMR could be obtained. To address this issue and to achieve a quick and efficient method to estimate the CMR, the Artificial Neural Network (ANN), Response Surface Method (RSM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) will be introduced in the current research. Accordingly, using the NSA results, an attempt was made to find a fast and efficient approach to derive the CMR. To this end, 5016 IDA analyses based on FEMA P695 methodology on 114 various Reinforced Concrete (RC) frames with 1 to 12 stories have been carried out. In this respect, five parameters have been used as the independent and desired inputs of the systems. On the other hand, the CMR is regarded as the output of the systems. Accordingly, a double hidden layer neural network with Levenberg-Marquardt training and learning algorithm was taken into account. Moreover, in the RSM approach, the quadratic system incorporating 20 parameters was implemented. Correspondingly, the Analysis of Variance (ANOVA) has been employed to discuss the results taken from the developed model. Additionally, the essential parameters and interactions are extracted, and input parameters are sorted according to their importance. Moreover, the ANFIS using Takagi-Sugeno fuzzy system was employed. Finally, all methods were compared, and the effective parameters and associated relationships were extracted. In contrast to the other approaches, the ANFIS provided the best efficiency and high accuracy with the minimum desired errors. Comparatively, it was obtained that the ANN method is more effective than the RSM and has a higher regression coefficient and lower statistical errors.