Rapid post-earthquake retrofit decisions require reliable estimates of interstory drift ratio. Conventional field practices either depend on instrumented measurements constrained by sparse sensor coverage or rely on qualitative expert judgment. This study aims to develop a CNN-based interstory drift ratio prediction method for reinforced concrete columns using strain-derived damage images. Reinforced concrete columns are modeled and analyzed in OpenSees to obtain strains and displacements. Strain fields are converted into strain-derived damage images through threshold-based staging that encodes discrete damage states. Structural parameters are concatenated to the damage image by adding fixed-value columns so the network can read structural context in a single two-dimensional input. We design systematic comparisons to isolate the benefit of structural information and section coverage. First, models without structural parameters are trained. Second, single-parameter variants are trained where only one attribute is provided. Third, full-parameter models include all attributes. For each setting, both single-section and multi-section inputs are evaluated. Samples are split by case and then divided 80/20 into training and validation sets. Model performance is reported using RMSE, MAE, and R-squared. The proposed approach achieves accurate inter-story drift ratio prediction overall, with improved performance when all structural parameters and multi-section inputs are used.

In reinforced concrete (RC) structures, steel corrosion can be occurred due to carbonation and chloride penetration, and these phenomenon lead to a degradation of the structural performance. Existing analytical models for corroded RC columns depend on complex empirical formulas based on specific experimental data. This study proposes a macro analytical model to predict the lateral load-resisting behavior of corroded RC columns. The proposed model is composed of a force-based nonlinear beam–column element that simulates the flexural behavior and a zero-length section element that represents the bond–slip deformation at the column base. To validate the proposed model, its results were compared and analyzed with the experimental results from existing literature. The results showed that the proposed model evaluated the maximum strength and the residual strength at a 4% drift ratio similarly to the experimental values. Furthermore, the model effectively predicted the pinching phenomenon and the hysteretic behavior under cyclic loading.

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 연구는 기계학습 및 설명 가능한 인공지능(xAI) 기법을 활용하여 폭발 하중을 받는 철근콘크리트 기둥의 보강 단계(Retrofit Level, RL)를 신속하게 평가하는 종합적 프레임워크를 제시한다. 파괴 유형와 보강 요구사항을 예측하기 위한 다단계 기계학습 접근 법을 개발하였으며, 이후 부분 의존성 그래프(Partial Dependence Plot, PDP) 분석을 통해 데이터 기반 보강 전략을 수립하였다. 제안 된 프레임워크는 두 가지 주요 프로세스로 구성된다: (1) 파괴유형 분류 및 RL 예측을 위한 다단계 기계학습 모델을 활용한 폭발 성능 평가, (2) 입력 변수 효과의 체계적 분석을 통한 PDP 기반 보강 전략 개발. RL 예측 모델은 광범위한 폭발 손상 평가 데이터를 바탕으 로 학습되었으며, 휨 및 전단 파괴유형에 대해 세 가지 손상 조건(심각, 보통, 경미)에서 검증되었다. PDP 분석 결과, 파괴유형과 손상 조건에 따라 서로 다른 보강 특성이 나타남을 확인하였다. PDP 기반 분석을 통해 주철근비 및 전단철근비에 대한 보강 가능 구간과 불가능 구간을 성공적으로 식별하였다.

본 논문은 고온 환경에 노출된 TRM 보강 RC 보의 잔존강도를 예측하기 위한 해석적 연구결과를 제시한다. 연구를 위해 상용 유한요소해석 프로그램인 ABAQUS가 사용되었으며, 콘크리트, 철근, CFRP grid, 모르타르에 대한 재료모델이 제안되었다. 본 연구에서 제안된 유한요소해석 모델의 검증을 위해 선행 연구결과에 대한 재현 해석이 수행되었다. 제안된 유한요소해석 모델의 예측 된 결과는 실험결과와 비교하여 잔존 극한하중과 극한하중 시점에서 각각 약 97.6%, 90.58%의 정확도를 보이는 것으로 나타났다. 또한, 유한요소해석을 통한 균열양상은 실험결과와 비교적 정확하게 예측되었다. 따라서 본 연구에서 제안된 해석모델은 고온 환경에 노출된 TRM 보강 RC 보의 잔존강도를 예측하기 위해 효과적으로 사용될 수 있을 것으로 판단된다.

This study analyzes the impact of climate change on the performance of continuous reinforced concrete pavement (CRCP) and proposes a method to improve the existing KPRP–CRCP design procedure. Our analysis of monthly mean temperature data from the Seoul Meteorological Station revealed a general increase in temperature from 2001 to 2034, with a more significant increase observed during summer and winter. The existing KPRP–CRCP design method uses the drop temperature (DT) as a key variable. Notably, the increasing monthly mean temperatures owing to climate change tend to decrease the DT that in turn lowers the maximum stress on the pavement slab. This leads to a significant problem: if the traditional design method based on outdated data is used, the predicted number of punchouts will be lower than expected. This can result in an over-reduction in the reinforcement ratio and slab thickness, leading to premature failure and increased maintenance costs. To solve this issue, we introduced a predictive model for the final setting temperature that accounts for monthly and regional characteristics. Applying this model showed that as the temperature increased, the DT and maximum stress proportionally increased. This provided a more realistic prediction of the number of punchouts and addressed the flaws of the existing design method. Furthermore, our analysis of punchout counts based on the construction start month using this predictive model revealed that punchouts were more frequent in summer (July–August) and less frequent in winter (January–February). Based on this, we determined that the optimal seasons for placing continuous reinforced concrete pavements were spring (March–June) and fall (September–November). In situations where the actual construction start month was unknown, we recommended using a conservative design approach based on the design in August, when punchouts were most likely to occur.

This study aims to quantitatively evaluate the life cycle carbon emissions of continuously reinforced concrete pavements on Korean expressways. The analysis focuses on assessing the effect of the changes in pavement design life and maintenance frequency on total carbon emissions to provide a basis for effective carbon reduction strategies. In accordance with ISO 14040 and ISO 14044, carbon emissions were calculated using actual design documents, including bills of quantities and unit price lists. National emission factors were applied to each life cycle stage, including the maintenance stage that was modeled based on the standard maintenance scenarios of the Korea Expressway Corporation. The study also conducted a scenario-based evaluation to examine the impact of extending the pavement design life from 20 to 30 years on maintenance-related emissions. The usage stage accounted for the largest share of total emissions, followed by the material production and maintenance stages. Notably, repeated asphalt overlay maintenance contributed significantly to emissions. Extending the design life reduced the number of high-emission maintenance activities, leading to a significant reduction in the total life cycle emissions. Extending the pavement design life and optimizing maintenance cycles were effective strategies for reducing the life cycle carbon emissions in road infrastructure. Furthermore, applying eco-design principles—such as incorporating recycled aggregates or low-carbon cement during the design stage—could further enhance sustainability. Future research should include various case studies and support the development of standardized national life cycle inventory databases for road infrastructure systems.

본 논문에서는 OpenSees 프로그램을 사용하여 축력과 폭발하중을 받는 철근콘크리트 기둥의 SDOF 해석 절차를 제시하였다. 해석 의 정확도를 검증하기 위해 저항함수 검증과 축력 및 폭발하중을 받는 철근콘크리트 기둥의 동적 거동을 비교하였다. 192개 단면에 대해 재료 강도, 철근비, 단면비, 축하중비를 주요 매개변수로 사용하여 매개변수 해석을 수행하였다. 축력을 고려하지 않을 경우 최 대 변위에 있어서 최대 50%의 오차가 발생하였다. 연성비 3을 기준으로 축하중비 30% 미만에서는 연성 거동을, 30% 이상에서는 취 성 거동을 보였다. 본 연구는 축력과 폭발하중을 받는 철근콘크리트 부재의 SDOF 해석 절차를 개발하고, 축하중비에 따른 내폭 설계 기준을 제시함으로써 내폭 설계에 널리 활용될 수 있다.

본 연구는 바이오차를 혼입한 콘크리트를 구조용 재료로 활용할 가능성을 검토하기 위해, 보강근의 종류에 따른 부착 성능 차이를 실험적으로 분석하였다. 이를 위해 직접 인발 실험을 수행하였으며, 실험 변수로는 콘크리트의 종류(일반/바이오차), 보강근의 종류(철근/GFRP), 보강근 직경(D13/D16)을 설정하였다. 실험 결과, 일반 철근을 적용한 실험체에서는 바이오차 혼입이 부착강도 저 하를 유발하였으며, 특히 D13 보강근에서 약 30%의 감소가 확인되었다. 반면, GFRP 보강근을 적용한 실험체에서는 바이오차 콘크리 트를 적용한 경우 부착성능이 소폭 향상되는 경향을 보였다. 또한 보강근의 직경이 증가할수록 최대 인발하중 및 평균 부착강도가 증가하는 양상이 일관되게 관찰되었다. 이러한 결과는 GFRP 보강근과 바이오차 콘크리트의 조합이 기계적 결합력 증대를 통해 긍정적인 구조 성능을 발휘할 수 있음을 시사하며, 향후 지속가능한 콘크리트 구조물의 보강설계에 기초자료로 활용될 수 있을 것으로 기대된다.

Seismically deficient reinforced concrete(RC) structures experience reduced structural capacity and lateral resistance due to the increased axial loads resulting from green retrofitting and vertical extensions. To ensure structural safety, traditional performance assessment methods are commonly employed. However, the complexity of these evaluations can act as a barrier to the application of green retrofitting and vertical extensions. This study proposes a methodology for rapidly calculating the allowable axial force range of RC buildings by leveraging simplified structural details and seismic wave information. The methodology includes three machine-learning-based models: (1) predicting column failure modes, (2) assessing seismic performance under current conditions, and (3) evaluating seismic performance under amplified mass conditions. A machine learning model was specifically developed to predict the seismic performance of an RC moment frame building using structural details, gravity loads, failure modes, and seismic wave data as input variables, with dynamic response-based seismic performance evaluations as output data. Classifiers developed using various machine learning methodologies were compared, and two optimal ensemble models were selected to effectively predict seismic performance for both current and increased mass scenarios.

Existing reinforced concrete buildings with seismically deficient columns experience reduced structural capacity and lateral resistance due to increased axial loads from green remodeling or vertical extensions aimed at reducing CO2 emissions. Traditional performance assessment methods face limitations due to their complexity. This study aims to develop a machine learning-based model for rapidly assessing seismic performance in reinforced concrete buildings using simplified structural details and seismic data. For this purpose, simple structural details, gravity loads, failure modes, and construction years were utilized as input variables for a specific reinforced concrete moment frame building. These inputs were applied to a computational model, and through nonlinear time history analysis under seismic load data with a 2% probability of exceedance in 50 years, the seismic performance evaluation results based on dynamic responses were used as output data. Using the input-output dataset constructed through this process, performance measurements for classifiers developed using various machine learning methodologies were compared, and the best-fit model (Ensemble) was proposed to predict seismic performance.

Most reinforced concrete (RC) school buildings constructed in the 1980s have seismic vulnerabilities due to low transverse reinforcement ratios in columns and beam-column joints. In addition, the building structures designed for only gravity loads have the weak-columnstrong- beam (WCSB) system, resulting in low lateral resistance capacity. This study aims to investigate the lateral resistance capacities of a two-story, full-scale school building specimen through cyclic loading tests. Based on the experimental responses, load-displacement hysteresis behavior and story drift-strain relationship were mainly investigated by comparing the responses to code-defined story drift limits. The test specimen experienced stress concentration at the bottom of the first story columns and shear failure at the beam-column joints with strength degradation and bond failure observed at the life safety level specified in the code-defined drift limits for RC moment frames with seismic details. This indicates that the seismically vulnerable school building test specimen does not meet the minimum performance requirements under a 1,400-year return period earthquake, suggesting that seismic retrofitting is necessary.

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.

Automated structural design methods for reinforced concrete (RC) beam members have been widely studied with various techniques to date. Recently, artificial intelligence has been actively applied to various engineering fields. In this study, machine learning (ML) is adopted to make automated structural design model for RC beam members. Among various machine learning methods, a supervised learning was selected. When a supervised learning is applied to development of ML-based prediction model, datasets for training and test are required. Therefore, the datasets for rectangular and t-shaped RC beams was constructed by commercial structural design software of MIDAS. Five supervised learning algorithms, such as Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN), Artificial Neural Networks (ANN), eXtreme Gradient Boosting (XGBoost) were used to develop the automated structural design model. Design moment (Mu), design shear force (Vu), beam length, uniform load (wu) were used for inputs of structural design model. Width and height of the designed section, diameter of top and bottom bars, number of top and bottom bars, diameter of stirrup bar were selected for outputs of structural design model. Performance evaluation of the developed structural design models was conducted using metrics sush as root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), and coefficient of determination (R2). This study presented that random forest provides the best structural design results for both rectangular and t-shaped RC beams.

국내 도심지에 적용하고 있는 중앙버스정류장의 포장은 주로 아스팔트 포장으로 시공되어 있으나 중차량인 버스의 하 중으로 인해 포장 파손 사례가 증가하여 시민들의 안전에 악영향을 미치고 있으며 유지보수 비용이 매년 증가하고 있다. 서울시에서는 이러한 문제를 해결하기 위해 국내 최초로 중앙버스정류장 신설 구간에 현장타설 방식으로 연속철근 콘크 리트 포장(CRCP)을 시공하였다. 본 연구에서는 이러한 구간의 연속철근 콘크리트 포장에 대한 이동차량 하중에 의한 동 적 거동 특성을 분석하고자 포장 슬래브에 콘크리트 변형률계를 설치하고 덤프트럭을 통과시키며 동적 하중 재하 실험 을 수행하였다. 실험에서는 이동차량의 속도를 다양하게 변화시켜 차량 속도에 따른 포장 슬래브의 동적 거동을 비교 분 석하였으며 이동차량이 CRCP의 여러 위치에서 정지하도록 하여 정지 위치에 따른 거동도 분석하였다. 실험 결과, 차량 이 CRCP를 통행할 경우 차량 속도 및 정지 위치에 따른 포장 슬래브의 동적 변형률은 매우 유사한 것으로 분석되었다.

본 연구에서는 터널부의 환경조건을 고려한 터널 내부 연속철근 콘크리트 포장(CRCP)의 설계 방안을 수립하기 위하여 CRCP 전용 구조해석 프로그램을 이용하여 터널 내부와 토공부의 환경하중을 고려한 수치해석을 수행하였다. 수치해석 모델은 철근비를 0.6%와 0.68%로 고정하고 슬래브의 두께를 26cm, 28cm, 30cm로 변화시켜 구성하였다. 또한, 터널 내외 부의 환경하중과 차륜하중을 적용하여 분석을 수행하였다. 분석 결과, 터널 내외부 모든 경우에서 CRCP의 슬래브 두께 가 증가할수록 균열간격과 균열폭이 증가하게 되며 터널 내부 CRCP는 슬래브 두께를 감소시키더라도 토공부와 유사한 균열간격 및 균열폭이 형성되는 것을 확인하였다. 향후 보다 다양한 조건에서의 수치해석 및 시험시공을 통해 국내 터널 환경에 적합한 터널 내부 CRCP 설계 방안을 마련할 수 있을 것으로 기대된다.

국내의 도심지 도로는 대부분 아스팔트 포장으로 시공되어 있으며 아스팔트 포장의 공용수명은 콘크리트 포장의 공용 수명에 비해 짧아 잦은 재시공 및 유지보수 작업이 필요하다. 도심지 특성상 포장 재시공 및 유지보수를 실시할 경우 작 업 시간 동안 교통차단을 유발하여 도로 이용자의 불편을 초래하게 된다. 따라서 서울특별시에서는 신설구간인 헌릉로의 중앙버스정류장 구간에 도심지 최초로 현장타설 방식의 연속철근 콘크리트 포장을 시공하였다. 본 연구에서는 중앙버스 정류장 구간에 시공한 연속철근 콘크리트 포장의 철근 거동에 대한 분석을 수행하여 철근의 응력이 가장 크게 발생하는 균열부에서의 철근 응력의 적정성을 분석하였다. 분석 결과, 균열부에서 멀어질수록 철근의 변형률이 뚜렷하게 감소하는 것을 확인하였으며 균열부에서 약 15cm 정도만 이격되어도 철근의 변형률이 급격하게 감소하여 철근과 콘크리트 간의 부착이 적절한 것으로 분석되었다. 또한, 균열부에서 발생한 철근의 변형률을 응력으로 환산하면 약 50MPa 정도로 철근 의 항복강도인 400MPa에 비해 매우 작아서 연속철근 콘크리트 포장의 우수한 공용성을 확보한 것으로 분석되었다.