Non-seismic-designed reinforced concrete (RC) pier walls often include lap splices in potential plastic hinge regions. This study develops an analytical model to capture the post-yield load–deformation response of lap-spliced RC pier walls subjected to earthquake loading. The parameters of the model were calibrated using experimental results, and linear regression was conducted to propose predictive equations for these parameters. The accuracy of the model was validated by comparing it to the load–deformation responses of specimens not included in the calibration database. Subsequently, the developed model was applied to probabilistic bridge models supported by RC pier walls. A multi-parameter seismic demand model was constructed, taking into account geometric, material, and structural uncertainties, using Lasso regression. This model achieved R² values of 0.73 for in-plane loading and 0.75 for out-of-plane loading. The improvements in performance metrics and the results of the sensitivity analysis emphasize the need to develop a multi-parameter seismic demand model to ensure more reliable seismic demand predictions.

본 연구에서는 TMD 설계 방법에 따른 배관의 지진응답 감소효과를 분석하였다. 구체적으로, 실제 원전 배관에 대한 진동대 시험 결과를 바탕으로 수치 배관 모델을 수립하고 검증하였다. 검증된 배관 모델을 바탕으로 TMD 설치 위치를 결정하고, 여러 가지 방법을 사용하여 TMD 설계값을 도출하였다. 더불어, 본 연구에서는 기존 설계식들을 기하평균한 값을 TMD 설계값으로 활용하였다. 최종적으로, 기존 배관을 기반으로 설계된 TMD가 지진의 무작위성과 지진 및 대상물질의 불확실성 아래에서도 효용성을 검증하였다. 또한, 연구에서 제안한 기하평균 모델을 기반으로 설계된 TMD의 작동성을 확인하였다. 결과적으로, TMD 설계 공식 및 방법에 따른 성능 차이를 비교한 결과, 기하평균 모델의 경우, 기존 설계식들의 특징을 포괄하는 양상이 보였다. 이러한 기하평균 모델은 추후 반복 적인 수치해석을 수행할 때 초기값으로 사용될 수 있을 것으로 보인다. 더불어, 이러한 분석 결과는 향후 원전 배관 계통의 TMD 설계 를 통해 내진 성능을 개선하는 데 유용한 자료로 활용될 것으로 기대된다.

Seismic design and risk assessment require input ground motions that accurately reflect both the seismic intensity associated with the target hazard level and the regional seismic characteristics of Korea. In this study, a scenario earthquake was defined through seismic hazard deaggregation. Due to the lack of recorded ground motions in Korea for this particular scenario, a finite fault was modeled. Seed ground motions related to the scenario earthquake were generated using the empirical Green’s function method, based on the 912 Gyeongju earthquake. During the spectral matching process, the convergence of the spectrum used for ground motion selection and the target Uniform Hazard Spectrum (UHS) was analyzed. This analysis led to the proposal of specific spectral conditions for selecting ground motions. The final set of input ground motions was then applied in time-history analyses of a nuclear power plant containment structure to assess its seismic response characteristics. The analysis results demonstrate that the proposed ground motion generation procedure applies to the development of ground motions in regions with moderate seismicity.

Machine learning (ML) techniques have been increasingly applied to the field of structural engineering for the prediction of complex dynamic responses of safety-critical infrastructures such as nuclear power plant (NPP) structures. However, the development of ML-based prediction models requires a large amount of training data, which is computationally expensive to generate using traditional finite element method (FEM) time history analysis, especially for aging NPP structures. To address this issue, this study investigates the effectiveness of synthetic data generated using Conditional Tabular GAN (CTGAN) in training ML models for seismic response prediction of an NPP auxiliary building. To overcome the high computational cost of data generation, synthetic tabular data was generated using CTGAN and its quality was evaluated in terms of distribution similarity (Shape) and feature relationship consistency (Pair Trends) with the original FEM data. Four training datasets with varying proportions of synthetic data were constructed and used to train neural network models. The predictive accuracy of the models was assessed using a separate test set composed only of original FEM data. The results showed that models trained with up to 50% synthetic data maintained high prediction accuracy, comparable to those trained with only original data. These findings indicate that CTGAN-generated data can effectively supplement training datasets and reduce the computational burden in ML model development for seismic response prediction of NPP structures.

In the United States, seismic design standards are crucial in classifying buildings into Risk Categories I to IV. These categories are based on the buildings' occupancy type and the potential risk they pose to public safety, the protection of human life, and the socioeconomic consequences of structural collapse in the event of an earthquake. As the risk category increases, a higher seismic importance factor and more stringent drift limits are imposed on the respective building. This results in enhanced lateral strength and stiffness of the seismic force-resisting system. This study, which compares the seismic demands of special moment frame buildings assigned to high-risk categories, focusing on static system overstrength, ductility, and collapse risk, provides practical insights for structural engineers and architects. For this purpose, nonlinear static and dynamic analyses are performed to quantify the seismic demands of 18 steel frame buildings assigned to Risk Categories II, III, and IV. The findings indicate that buildings in Risk Category II do not meet the target collapse risk of 1% in 50 years, as specified in ASCE/SEI 7. For buildings in higher risk categories, the equivalent lateral force method for estimating seismic base shear is deemed more effective in ensuring adequate seismic performance.

The 3T irregular shape structure is used for designing wind loads in high-rise buildings. Among them, the Tapered shape is a shape with a cross-section that changes throughout the entire floor. Recently, various advanced Tapered shapes have been applied, such as having a cross-section that varies only in part of the height or combining different shapes. In this study, an analysis model was selected by applying three types of Tapered part locations(Bottom, Middle, Top) and angles as design variables. Equivalent static seismic loads and historical earthquake records were applied to compare and analyze the seismic response of the Tapered models with regular-shaped models. As a result of the analysis, positioning the partial taper in the middle shows the lowest seismic response. Additionally, a larger taper angle decreased the story drift ratio, top-story displacement, shear wall shear force, and column bending moment, while increasing absolute acceleration and column axial force.

Reinforced concrete (RC) columns exhibit cyclic damage, such as strength degradation, under cyclic lateral loading, such as earthquakes. Considering the cyclic damage, the nonlinear load-deformation response of RC columns can be simulated using a lumped plasticity model. Based on an experimental database, this study calibrates lumped plasticity model parameters for 371 rectangular and 290 circular RC columns. The model parameters for adequate flexural rigidity, plastic rotation capacity, post-capping rotation capacity, moment strength, and cyclic strength degradation parameter are adjusted to match each experimentally observed load-deformation response. We have developed predictive equations that accurately relate the model parameters to the design characteristics of RC columns through regression analyses, providing a reliable tool for engineers and researchers. To demonstrate their application, the proposed and existing models numerically simulate the earthquake response of a bridge pier in a metropolitan railway bridge. The pier is subjected to several ground motions, increasing intensity until collapse occurs. The proposed lumped plasticity model showed about 41% less vulnerable to collapse.

The dome structure is suitable as a roof for large spatial structures because it can maintain the shape without installing columns in the internal space. However, the structure characteristics of the lower and upper structures of the dome structure are different, and damage may occur when an earthquake occurs. Therefore, in this study, mid-story isolation system was applied to the ribbed dome and geodesic dome structures to analyze the seismic response of the lower and upper structures according to the dome shape. As a result of the analysis, the displacement of the ribbed dome increased, but the deformation of the ribbed dome and the response of the lower structure decreased, and the seismic response of the geodesic dome decreased overall. From this result, the effect of the isolator according to the shape of the dome structure was confirmed, and the mid-story isolation is considered effective in reducing the seismic response of the upper and lower structures.

The diagrid structural system has a braced frame that simultaneously resists lateral and vertical loads, and is being applied to many atypical high-rise buildings for aesthetic effects. In this study, a 60-story structure with twisted degrees of 0° to 180° was selected to determine seismic response control performance of twisted high-rise structures whether the diagrid system was applied and according to the reduction of braced frame material quantity. For this purpose, ‘Nor’ model without the diagrid system and the ‘DS’ model with the diagrid system, which was modeled by reducing braced frame member section to 700~400, were modeled. As a result, the 'DS' model showed an seismic response control effect in all Twisted models even when the quantity was reduced, and especially, the Twisted shape model was found to have an superior response control effect compared to the regular structure. In addition, the ‘600DS’ analysis model, which matched the ‘Nor’ model by 99.0% in quantity, showed an increase in seismic response control performance as the rotation angle increased.

본 연구는 수소 탱크를 고정하는 강재 볼트의 부식으로 인한 성능 저하 문제를 해결하기 위해 내부식성 복합재료로 알려진 Glass Fiber Reinforced Polymer (GFRP) 및 Carbon Fiber Reinforced Polymer (CFRP)를 활용한 앵커 시스템을 제안하고, 이를 지진 하 중 하에서의 안전성 평가를 통한 적용 타당성 검토를 수행하였다. 연구에서는 현장 조사를 통해 실제 사용 중인 수소 탱크의 설계 제 원을 확보한 후 이를 바탕으로 유한요소해석을 수행하였으며, AC 156 인공 지진파를 적용하여 FRP 앵커 볼트와 기존 강재 앵커의 성 능을 비교 분석하였다. 주요 분석 결과, FRP 앵커 볼트를 적용한 수소 탱크는 강재 앵커 볼트에 비해 고유 진동수가 21% 증가하여 구 조적 강성이 향상됨을 확인하였다. 또한, 가속도 응답 분석 결과 FRP 앵커 볼트는 상부 가속도를 감소시켜 지진 하중에 대한 저항성을 증진하는 것으로 나타났다. 응력 해석에서는 FRP 앵커 볼트가 강재 앵커 볼트에 비해 유효 응력이 약 91% 감소하여, 구조적 안전성이 크게 개선되었다. 그러나, FRP 앵커 볼트 적용 시 기초 콘크리트에 가해지는 쪼갬 인장 응력이 강재 앵커 대비 최대 3.5배 증가하는 것으로 나타났으며, 이에 따라 FRP 앵커 볼트 사용 시 기초 콘크리트의 보강이 필요할 것으로 사료된다. 이러한 연구 결과는 수치해석 에 국한된 결과로, 향후 실제 지진 하중을 모사한 실험적 검증이 필요하다. FRP 앵커 볼트의 적용 가능성은 향후 연구를 통해 광범위 하게 평가될 것이며, 이를 통해 수소 인프라의 내구성과 안전성을 더욱 강화할 수 있을 것으로 기대된다.

Strong ground motions at specific sites can cause severe damage to structures. Understanding the influence of site characteristics on the dynamic response of structures is crucial for evaluating their seismic performance and mitigating the potential damage caused by site effects. This study investigates the impact of the average shear wave velocity, as a site characteristic, on the seismic response of low-to-medium-rise reinforced concrete buildings. To explore them, one-dimensional soil column models were generated using shear wave velocity profile from California, and nonlinear site response analyses were performed using bedrock motions. Nonlinear dynamic structural analyses were conducted for reinforced concrete moment-resisting frame models based on the regional information. The effect of shear wave velocity on the structural response and surface ground motions was examined. The results showed that strong ground motions tend to exhibit higher damping on softer soils, reducing their intensity, while on stiffer soils, the ground motion intensity tends to amplify. Consequently, the structural response tended to increase on stiffer soils compared to softer soils.

The damage to structures during an earthquake can be varied depending on the frequency characteristics of seismic waves and the geological properties of the ground. Therefore, considering such attributes in the design ground motions is crucial. The Korean seismic design standard (KDS 17 10 00) provides design response spectra for various ground classifications. If required for time-domain analysis, ground motion time series can be either selected and adjusted from motions recorded at rock sites in intraplate regions or artificially synthesized. Ground motion time series at soil sites should be obtained from site response analysis. However, in practice, selecting suitable ground motion records is challenging due to the overall lack of large earthquakes in intraplate regions, and artificially synthesized time series often leads to unrealistic responses of structures. As an alternative approach, this study provides a case study of generating ground motion time series based on the hybrid broadband ground motion simulation of selected scenario earthquakes at sites in the Nakdonggang delta region. This research is significant as it provides a novel method for generating ground motion time series that can be used in seismic design and response analysis. For large-magnitude earthquake scenarios close to the epicenter, the simulated response spectra surpassed the 1000-year design response spectra in some specific frequency ranges. Subsequently, the acceleration time series at each location were used as input motions to perform nonlinear 1D site response analysis through the PySeismoSoil Package to account for the site response characteristics at each location. The results of the study revealed a tendency to amplify ground motion in the mid to long-period range in most places within the study area. Additionally, significant amplification in the short-period range was observed in some locations characterized by a thin soil layer and relatively high shear wave velocity soil near the upper bedrock.

High-rise buildings are equipped with TMD (Tuned Mass Damper), a vibration control device that ensure the stability and usability of the building. In this study, the seismic response control performance was evaluated by selecting the design variables of the TMD based on the installation location of the twisted irregular building. To this end, we selected analysis models of 60, 80, and 100 floors with a twist angle of 1 degree per floor, and performed time history analysis by applying historical seismic loads and resonant harmonic loads. The total mass ratio of TMDs was set to 1.0%, and the distributed installation locations of TMDs were selected through mode analysis. The analysis results showed that the top-floor displacement responses of all analysis models increased, but the maximum story drift ratio decreased. In order to secure the seismic response control performance by distributed installation of TMDs in twisted irregular buildings, it is judged that the mass ratio distribution of TMDs will act as a key variable.

Dynamic responses of nuclear power plant structure subjected to earthquake loads should be carefully investigated for safety. Because nuclear power plant structure are usually constructed by material of reinforced concrete, the aging deterioration of R.C. have no small effect on structural behavior of nuclear power plant structure. Therefore, aging deterioration of R.C. nuclear power plant structure should be considered for exact prediction of seismic responses of the structure. In this study, a machine learning model for seismic response prediction of nuclear power plant structure was developed by considering aging deterioration. The OPR-1000 was selected as an example structure for numerical simulation. The OPR-1000 was originally designated as the Korean Standard Nuclear Power Plant (KSNP), and was re-designated as the OPR-1000 in 2005 for foreign sales. 500 artificial ground motions were generated based on site characteristics of Korea. Elastic modulus, damping ratio, poisson’s ratio and density were selected to consider material property variation due to aging deterioration. Six machine learning algorithms such as, Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Artificial Neural Networks (ANN), eXtreme Gradient Boosting (XGBoost), were used t o construct seispic response prediction model. 13 intensity measures and 4 material properties were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks present good prediction performance considering aging deterioration.

이 논문은 포트 받침이 설치된 3경간 연속 콘크리트 거더교를 대상으로 받침의 노후화가 교량의 지진응답에 미치는 영향을 분석하 였다. 포트 받침은 고정단 및 가동단의 강성을 반영해 교축방향, 교축직각방향, 수직방향의 탄성 스프링으로 모델링하였다. 포트 받침 의 노후화를 고정단 받침의 수평강성 저하와 가동단 받침의 수평강성 증가로 나누어 받침의 노후화가 교량의 지진 거동에 미치는 영 향을 분석하였다. 수치해석으로부터 구한 고유진동수 값과 이 교량의 설계 고유진동수를 비교하여 해석 대상 3경간 연속 거더교의 유 한요소모델이 타당함을 보였으며, 국내 도로교설계기준(한계상태설계법)의 설계응답스펙트럼에 부합하는 인공지진파를 이용해 받 침 강성의 변화에 따른 교량 상부 및 받침의 지진응답을 계산하였다. 지진해석 결과, 받침의 노후화로 인해 고정단 받침의 수평강성이 감소할 경우 지진으로 인한 고정단 받침의 절대최대 상대변위가 증가하였다. 이는 받침을 지지하는 모르타르의 균열과 앵커 볼트의 탈락 가능성이 커짐을 의미한다. 받침의 노후화로 인해 가동단 받침의 수평강성이 증가하는 경우에는 지진으로 인한 고정단 받침의 절대최대 전단력이 감소하였다. 그러나 이 경우 가동단 바로 위 거더 단면에 추가의 휨인장응력이 발생할 수 있으며, 이러한 받침 성능 의 변화로 인하여 지진 시 연속 콘크리트교에 예기치 못한 구조적 손상이 발생할 수 있다.

In the seismic evaluation of underground utility tunnels, selecting an analytical method is critical to estimating reasonable seismic responses. In simplified pseudo-static analysis methods widely applied to typical seismic design and evaluation of underground tunnels in practice, it is essential to check whether the methods provide valid results for cut-and-cover tunnels buried in shallow to medium depth. The differences between the two simplified pseudo-static methods are discussed in this study, and the analysis results are compared to those obtained from FLAC models. In addition to the analysis methods, seismic site classification, overburden soil depth, and sectional configuration are considered variables to examine their effects on the seismic response of underground utility tunnels. Based on the analysis results, the characteristics derived from the concepts and details of each simplified model are discussed. Also, general observations are made for the application of simplified analysis methods.

This study investigated the impact of soil-structure interaction on multi-degree-of-freedom structures using the shallow-foundation Winkler model, known as the BNWF model. The model’s period was determined through eigenvalue analysis and compared to results obtained from FEMA’s formula. Results indicated that considering the soil, the structure’s period increased by up to 8.7% compared to the fixed-base model, aligning with FEMA’s calculations. Furthermore, with adequate ground acceleration, roof displacement increased by 3.4% to 3.8%, while base shear decreased by 4% to 10%. However, roof displacement and base shear increased in some earthquake scenarios due to spectral shape effects in regions with extended structural periods. Foundation damping effects, determined through the foundation’s moment-rotation history, grew with higher ground acceleration. This suggests that accounting for period elongation and foundation damping can enhance the seismic design of multi-degree-of-freedom structures.

In this paper, machine learning models were applied to predict the seismic response of steel frame structures. Both geometric and material nonlinearities were considered in the structural analysis, and nonlinear inelastic dynamic analysis was performed. The ground acceleration response of the El Centro earthquake was applied to obtain the displacement of the top floor, which was used as the dataset for the machine learning methods. Learning was performed using two methods: Decision Tree and Random Forest, and their efficiency was demonstrated through application to 2-story and 6-story 3-D steel frame structure examples.

Machine learning is widely applied to various engineering fields. In structural engineering area, machine learning is generally used to predict structural responses of building structures. The aging deterioration of reinforced concrete structure affects its structural behavior. Therefore, the aging deterioration of R.C. structure should be consider to exactly predict seismic responses of the structure. In this study, the machine learning based seismic response prediction model was developed. To this end, four machine learning algorithms were employed and prediction performance of each algorithm was compared. A 3-story coupled shear wall structure was selected as an example structure for numerical simulation. Artificial ground motions were generated based on domestic site characteristics. Elastic modulus, damping ratio and density were changed to considering concrete degradation due to chloride penetration and carbonation, etc. Various intensity measures were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks and extreme gradient boosting algorithms present good prediction performance.

This study aims to investigate the seismic response of a large span thin shell structures and assess their displacement under seismic loads. The study employs finite element analysis to model a thin shell structure subjected to seismic excitation. The analysis includes eigenvalue analysis and time history analysis to evaluate the natural frequencies and displacement response of the structure under seismic loads. The findings show that the seismic response of the large span thin shell structure is highly dependent on the frequency content of the seismic excitation. The eigenvalue analysis reveals that the tenth mode of vibration of the structure corresponds to a large-span mode. The time history analysis further demonstrates, with 5% damping, that the displacement response of the structure at the critical node number 4920 increases with increasing seismic intensity, reaching a maximum displacement of 49.87mm at 3.615 seconds. Nevertheless, the maximum displacement is well below the allowable limit of the thin shell. The results of this study provide insight into the behaviour of complex large span thin shell structures as elevated foundations for buildings under seismic excitation, based on the displacement contours on different modes of eigenvalues. The findings suggest that the displacement response of the structure is significant for this new application of thin shell, and it is recommended to enhance the critical displacement area in the next design phase to align with the findings of this study to resist the seismic impact.