Numerous factors contribute to the deterioration of reinforced concrete structures. Elevated temperatures significantly alter the composition of the concrete ingredients, consequently diminishing the concrete's strength properties. With the escalation of global CO2 levels, the carbonation of concrete structures has emerged as a critical challenge, substantially affecting concrete durability research. Assessing and predicting concrete degradation due to thermal effects and carbonation are crucial yet intricate tasks. To address this, multiple prediction models for concrete carbonation and compressive strength under thermal impact have been developed. This study employs seven machine learning algorithms—specifically, multiple linear regression, decision trees, random forest, support vector machines, k-nearest neighbors, artificial neural networks, and extreme gradient boosting algorithms—to formulate predictive models for concrete carbonation and thermal impact. Two distinct datasets, derived from reported experimental studies, were utilized for training these predictive models. Performance evaluation relied on 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 analytical outcomes demonstrate that neural networks and extreme gradient boosting algorithms outshine the remaining five machine learning approaches, showcasing outstanding predictive performance for concrete carbonation and thermal effect modeling.

Due to the aging of a building, 38.8% (about 2.82 million buildings) of the total buildings are old for more than 30 years after completion and are located in a blind spot for an inspection, except for buildings subject to regular legal inspection (about 3%). Such existing buildings require users to self-inspect themselves and make efforts to take preemptive risks. The scope of this study was defined as the general public's visual self-inspection of buildings and was limited to structural members that affect the structural stability of old buildings. This study categorized possible damage to reinforced concrete to check the structural safety of buildings and proposed a checklist to prevent the damage. A damage assessment methodology was presented during the inspection, and a self-inspection scenario was tested through a chatbot connection. It is believed that it can increase the accessibility and convenience of non-experts and induce equalized results when performing inspections, according to the chatbot guide.

In this study, the structural performance of the specimen fabricated through 3D printing was evaluated through monotonic loading experiments analysis to apply to 3D printed structures. The compression and flexural experiments were carried out, and the experimental results were compared to the finite element model results. The loading directions of specimens were investigated to consider the capacity of specimens with different curing periods, such as 7 and 28 days. As a result, the strength tended to increase slightly depending on the stacking direction. Also, between the 3D-printed panel composite and the non-reinforced panel, the bending performance depended on the presence or absence of composite reinforcement.

Existing reinforced concrete buildings with seismically deficient details have premature failure under earthquake loads. The fiber-reinforced polymer column jacket enhances the lateral resisting capacities with additional confining pressures. This paper aims to quantify the retrofit effect varying the confinement and stiffness-related parameters under three earthquake scenarios and establish the retrofit strategy. The retrofit effects were estimated by comparing energy demands between non-retrofitted and retrofitted conditions. The retrofit design parameters are determined considering seismic hazard levels to maximize the retrofit effects. The critical parameters of the retrofit system were determined by the confinement-related parameters at moderate and high seismic levels and the stiffness-related parameters at low seismic levels.

본 논문에서는 유한요소해석 프로그램을 통해 파괴 거동 유형별 철근콘크리트 기둥 및 폭발 하중을 모델링하였으며, 실제 실험과 의 동적 응답을 비교하여 모델의 적합성을 입증하였다. 개발한 모델을 이용하여 폭발 하중에 대한 부재의 동적 응답을 확인하기 위해 폭발 하중 시나리오를 설정하였으며 해당 시나리오별 폭발 하중에 대한 시간에 따른 변위 및 응력 결과를 도출하였다. 동적 응답을 통 해 폭발 하중에 대한 기둥의 성능평가(Ductility, Residual)를 수행하였으며 이를 비교 및 분석하였다.

PURPOSES : The purpose of this study was to improve the performance of concrete pavements by decreasing measurement deviations using an Internet of Things (IoT)-based air content measurement device. METHODS : We calculated the properties of concrete which varied according to the air content. For a low measurement deviation, the concrete pavement performed according to the design standard. To confirm the difference in the performance of the concrete pavement for various air contents, we verified the change in the relative dynamic modulus according to the number of freeze–thaw cycles for each value of the air content. In addition, we analyzed the number of durability cracks according to the freeze–thaw cycles in the field. RESULTS : We confirmed that IoT-based measurement equipment improved the performance of pavements without changing their mixing designs or specifications. We confirmed that the performance of concrete pavements changed even with variations in air content within the range of quality standards. Using IoT-based air content management, we confirmed the reduction in concrete pavement durability cracks without changing the mixing design. CONCLUSIONS : We confirmed that IoT-based air-content management improved pavement performance. The feasibility of extending this concept to manage other concrete properties such as the chloride content should be acknowledged. Future research will require laboratory tests to understand the variation in concrete properties with varying air contents and to consider diverse load conditions.

PURPOSES : This study aims to understand the effect of defective dowel bar installation on jointed concrete pavement (JCP), which can cause joint freezing, spalling, cracking, and faulting and finally shorten the lifespan of the pavement. METHODS : A comprehensive field survey was undertaken at an expressway construction site in South Korea to assess dowel bar installation conditions. In addition, finite element (FE) analysis was used to simulate JCP behavior with both vertical and horizontal dowel misalignments. Different temperature conditions, including a change of -55 °C and gradient of -0.1 °C/mm, were integrated into the FE model to examine horizontal slab contraction and simultaneous slab curling. RESULTS : The analysis revealed pronounced slab behaviors under specific temperature changes, particularly when combined with dowel misalignments. The simultaneous effects of horizontal contraction and slab curling owing to temperature changes and gradients became more evident in the presence of dowel misalignments. CONCLUSIONS : The results confirmed that dowel bar misalignment considerably affected the behavior of the JCP, thereby emphasizing the importance of the proper installation of dowel bars.

PURPOSES : Previously, the expansion state of the concrete pavement in which AAR occurred could not be determined. Because the current situation has not been evaluated, it has been difficult to prepare an appropriate response. In this study, a method for calculating the expansion amount of concrete pavement using the stiffness damage test (SDT) is proposed. METHODS : The SDT method was examined through a literature review. For the laboratory tests, specimens that generated AAR were produced based on the mix design (2018) of the Korea Expressway Corporation. SDT was used to calculate various mechanical properties, and their correlation with the expansion amount was reviewed. RESULTS : Using the SDT, various mechanical properties(elastic modulus, hysteresis area, plastic deformation, plastic deformation index, stiffness damage index, and nonlinear index) were calculated based on the expansion rate of the AAR. The elastic modulus was evaluated as the best predictor of the expansion rate. Thus, if the elastic modulus is calculated using SDT, a prediction equation can be used to calculate the amount of AAR expansion. This equation will need to be supplemented by further research. CONCLUSIONS : SDT was used to confirm that the expansion state due to the AAR of the concrete pavement could be indirectly evaluated. Among the mechanical properties related to SDT, the elastic modulus was found to be the most suitable for predicting the amount of expansion.

PURPOSES : The objectives of this study are to evaluate the condition of concrete bridge decks using the multi-channel ground penetrating radar (GPR) testing and compare the value of its dielectric constant value with actual concrete condition. METHODS : The reflection coefficient method was used to measure the dielectric properties of concrete bridge decks. Air-coupled step-frequency GPR testing was used to measure the time taken for reflection from the interfaces between the layers. Specimens of the asphalt mixture and concrete bridge-deck were collected by field coring. GPR testing was conducted on two bridges with different concrete bridge deck conditions on national highways. After the GPR tests, the actual conditions of the concrete bridge deck were investigated using specimen coring. RESULTS : GPR testing indicated that the dielectric constants of concrete bridge decks in good condition ranged from 8 to 10, whereas those corresponding to poor condition ranged from 4 to 6. The results of GPR testing can determine the actual condition and degree of distress of concrete bridge decks determined from the specimen coring data. Therefore, GPR testing is appropriate for nondestructively evaluating the condition of a concrete bridge deck. CONCLUSIONS : The analysis results of the dielectric constants of the concrete bridge deck obtained from multichannel GPR testing were consistent with the actual bridge deck conditions. In the near future, an additional verification process for this approach under different bridge conditions will be required to improve its precision and ensure reliability.

철근콘크리트는 가장 널리 사용되는 건축자재로 최근 노후 시설물이 증가하면서 노후 구조물에 대한 안전성 검토가 매우 중요한 문제로 대두되고 있다. 본 연구에서는 대표적인 열화 인자인 동결융해와 철근부식 그리고 동결융해와 철근부식의 복합적 열화에 따른 RC 휨 부재의 거동을 실험적으로 평가하였다. 4개의 철근콘크리트 휨 부재를 제작하였으며 각 열화 인자에 따른 RC 휨 부재의 거동을 평가하기 위해 4점 재하법을 이용하여 정적실험을 수행하였다. 동결융해는 총 300 사이클의 급속동 결융해실험을 수행하였으며, 부식은 전위차부식촉진실험을 수행하였다. 실험 결과, 동결융해로 인해 콘크리트의 압축강도가 12% 감소하였으며 RC 보 부재의 상부 압축부의 파쇄 범위가 증가하였고 최대강도가 6% 감소하였다. 철근부식으로 인해 RC 휨 부 재의 항복강도가 1.2%, 최대강도가 7% 감소하였으며, 복합열화로 인해 RC 휨 부재의 항복강도가 2.4%, 최대강도가 9% 감소하 였다.