Blow-up in jointed concrete pavements refers to a type of distress caused by the excessive accumulation of compressive stress within concrete slabs, primarily resulting from internal expansion and elevated environmental temperatures. This phenomenon frequently leads to slab buckling and is challenging to predict in terms of both timing and location, thereby significantly threatening the long-term structural stability of the pavement. In the present study, the pavement growth and blow-up analysis (PGBA) model was employed to quantitatively predict the timing of blow-up events in jointed concrete pavements. The model estimates the maximum compressive stress within the slab throughout the pavement’s service life using input parameters such as reliability, climatic conditions, pavement structure, material properties, and expansion joint configurations. Subsequently, the model compares the estimated stress to the threshold stress associated with blow-up to determine the likely time of occurrence. A sensitivity analysis was performed on a range of design and environmental factors, including annual maximum temperature, annual maximum precipitation, coefficient of thermal expansion, ASR, pavement thickness, geometric imperfection, and expansion joint spacing and width. The influence of each factor on the predicted blow-up occurrence time was quantitatively evaluated. The analysis demonstrated that climatic conditions, pavement structure, material properties, and expansion joint characteristics, as considered in the PGBA model, collectively govern the timing of blow-up events. These findings offer critical insights for informing the design and maintenance strategies of jointed concrete pavements.

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.

This study proposes an improved method for updating finite element models (FEM) by incorporating the random field characteristics of concrete material properties in reinforced concrete structures. Traditional FEM often assumes homogeneous material properties, which can lead to significant discrepancies between predicted and actual dynamic responses, especially in structures where the Young’s modulus (E) of concrete varies due to factors like curing conditions, material composition, and construction methods. We employed a Gaussian random field model and a system identification (SI) technique to address this limitation to optimize sensor placement. We developed an FEM updating method that incorporates the spatial variability of concrete elasticity. This optimization allowed for a more accurate capture of dynamic properties across various structural locations, resulting in FEM updates that reflect concrete’s inherent heterogeneity. The proposed method was validated through numerical examples, comparing dynamic response accuracy in models before and after updating. Results demonstrated that error values, measured in terms of maximum value error and normalized root mean squared Error (NRMSE), were significantly reduced in the updated models compared to the pre-update model. This approach effectively addresses the limitations of homogeneous assumptions in FEM.

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.

Over the past decade, a global emphasis has been placed on reducing carbon emissions, with the construction industry given particular attention in this regard. In reinforced concrete structures, one proposed method to reduce carbon emissions during manufacturing is to replace steel bars with fiber reinforced polymer (FRP) reinforcing bars. Accordingly, this study conducts flexural tests on concrete FRP (CFRP)-reinforced prefabricated slabs to investigate the joints between these slabs. The experimental variables include the types of connecting reinforcing bars used at the joints of two prefabricated slabs. The experimental results revealed the following: 1) the moment capacity of the slabs did not reach the nominal moment due to the insufficient length of the CFRP reinforcing bars, and 2) steel bars were found to be more suitable than CFRP reinforcing bars for connecting prefabricated slabs, as they promote ductile failure.

본 연구에서는 EBR 및 EBROG 기법으로 부착된 CFRP판과 콘크리트 모체 간 부착성능을 평가하였다. 실험 변수로는 콘크리 트 압축강도, 홈의 개수 및 깊이를 고려하였으며, 총 21개의 시편을 대상으로 단일 랩 전단 실험을 수행하였다. 실험 결과, EBROG 기법을 적용한 시편은 EBR 기법을 적용한 시편보다 최대 62% 높은 부착 강도를 보였다. 또한, 홈의 개수와 깊이가 증가할수록 부착강 도도 증가했으나, 홈이 3개일 때 가장 높은 증가율을 기록하였다. 한편, 콘크리트 압축강도가 증가할수록 부착강도도 상승했지만, 압축 강도가 가장 높은 시편에서는 오히려 부착강도 증가율이 가장 낮았다. 아울러, EBROG 기법으로 부착된 CFRP 판의 유효 변형률을 예측하는 모델을 개발하기 위해 실험 데이터를 기반으로 회귀 분석을 수행하였다. 제안된 모델의 예측값과 실험값의 비의 평균과 표준 편차는 각각 1.002 및 0.032로 나타나, 해당 모델이 유효 변형률을 정확하게 예측할 수 있음을 확인하였다.

전 세계 이산화탄소 배출량이 지속적으로 증가하면서, 환경 개선 및 탄소 격리를 위한 다양한 연구들이 진행되고 있 다. 건설 산업에서도 탄소를 줄이기 위한 연구로 바이오차를 건설 자재에 사용하여, 탄소 격리를 위한 방법으로 진행되고 있다. 바이오차는 바이오매스를 열분해하여 생성한 숯으로, 높은 탄소 함량과 다공성 구조가 특징이며, 탄소 격리를 위한 물질로 떠오 르고 있다. 본 연구에서는 시멘트 사용량을 줄이고 바이오차를 혼입한 콘크리트를 건설 자재로써 가능성을 확인하고자 하였다. 이를 위해 시멘트의 일부를 바이오차로 치환하여 혼입한 콘크리트의 역학적 특성(슬럼프, 공기량, 압축강도)과 질량 기반 특성 (흡수율, 밀도, 공극률)을 평가하였다. 바이오차의 시멘트 치환율은 0%, 5%, 10%로 설정하였다. 바이오차의 수분 흡수 및 보유 력에 따라 바이오차의 시멘트 치환율이 증가할수록 슬럼프는 감소하였다. 바이오차의 다공성 구조를 SEM 실험으로 확인하였으 며, 이에 따라 콘크리트에서의 공극 형성으로 바이오차의 시멘트 치환율이 증가할수록 공기량과 흡수율이 증가하였다. 바이오차 의 시멘트 치환율 5%에서 압축강도와 비강도가 가장 높은 값으로 나타났으며, 탄소 격리를 위한 방법으로 건설 자재 활용의 가능성을 확인하였다.

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.