• 한국지진공학회논문집
• /
• 제20권4호
• /
• pp.211-222
• /
• 2016

Reinforced concrete shear walls with deficient reinforcement details are tested under cyclic loading. The deficiency of reinforcement details includes insufficient splice length in U-stirrups at the ends of horizontal reinforcement and boundary column dowel bars found in existing low- to mid-rise Korean buildings designed non-seismically. Three test specimens have rectangular, babel and flanged sections, respectively. Flexure- and shear-controlled models for reinforced concrete shear walls specified in ASCE/SEI 41-13 are compared with the flexural and shear components of force-displacement relation extracted separately from the top displacement of the specimen based on the displacement data measured at diverse locations. Modification of the shear wall models in ASCE/SEI 41-13 is proposed in order to account for the effect of bar slip, cracking loads in flexure and shear. The proposed modification shows better approximation of the test results compared to the original models.

• 한국지진공학회논문집
• /
• 제28권4호
• /
• pp.193-203
• /
• 2024

Existing reinforced concrete (RC) building frames constructed before the seismic design was applied have seismically deficient structural details, and buildings with such structural details show brittle behavior that is destroyed early due to low shear performance. Various reinforcement systems, such as fiber-reinforced polymer (FRP) jacketing systems, are being studied to reinforce the seismically deficient RC frames. Due to the step-by-step modeling and interpretation process, existing seismic performance assessment and reinforcement design of buildings consume an enormous amount of workforce and time. Various machine learning (ML) models were developed using input and output datasets for seismic loads and reinforcement details built through the finite element (FE) model developed in previous studies to overcome these shortcomings. To assess the performance of the seismic performance prediction models developed in this study, the mean squared error (MSE), R-square (R²), and residual of each model were compared. Overall, the applied ML was found to rapidly and effectively predict the seismic performance of buildings according to changes in load and reinforcement details without overfitting. In addition, the best-fit model for each seismic performance class was selected by analyzing the performance by class of the ML models.

• 한국지진공학회논문집
• /
• 제27권6호
• /
• pp.293-301
• /
• 2023

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.

• Computers and Concrete
• /
• 제2권5호
• /
• pp.367-380
• /
• 2005

Recent earthquakes have shown that many of existing buildings in Iran sustain heavy damage due to defective seismic details. To assess vulnerability of one common type of buildings, which consists of low rise framed concrete structures, three defective and three standard columns have been tested under reversed cyclic load. The substandard specimens suffered in average 37% loss of strength and 45% loss of energy dissipation capacity relative to standard specimens, and this was mainly due to less lateral and longitudinal reinforcement and insufficient sectional dimensions. A relationship has been developed to introduce variation of plastic length under increasing displacement amplitude. At ultimate state, the length of plastic hinge is almost equal to full depth of section. Using calibrated hysteresis models, the response of different specimens under two earthquakes has been analyzed. The analysis indicated that the ratio between displacement demand and capacity of standard specimens is about unity and that of deficient ones is about 1.7.

• 한국지진공학회논문집
• /
• 제24권1호
• /
• pp.19-27
• /
• 2020

Reinforced concrete (RC) buildings built in the 1980s are vulnerable to seismic behavior because they were designed without any consideration of seismic loads. These buildings have widely spaced transverse reinforcements and a short lap splice length of longitudinal reinforcements, which makes them vulnerable to severe damage or even collapse during earthquakes. The purpose of this study is to investigate the impact of bidirectional lateral loads on RC columns with deficient reinforcement details. An experimental test was conducted for two full-scale RC column specimens. The test results of deficient RC columns revealed that bidirectional loading deteriorates the seismic capacity when compared with a column tested unidirectionally. Modeling parameters were extracted from the tested load-displacement response and compared with those proposed in performance-based design standards. The modeling parameters proposed in the standards underestimated the deformation capacity of tested specimens by nearly 50% and overestimated the strength capacity by 15 to 20%.

• 한국지진공학회논문집
• /
• 제22권5호
• /
• pp.281-289
• /
• 2018

Existing reinforced concrete building structures have seismic vulnerabilities due to their seismically-deficient details resulting in non-ductile behavior. The seismic vulnerabilities can be mitigated by retrofitting the buildings using a fiber-reinforced polymer column jacketing system, which can provide additional confining pressures to existing columns to improve their lateral resisting capacities. This study presents dynamic responses of a full-scale non-ductile reinforced concrete frame retrofitted using a fiber-reinforced polymer column jacketing system. A series of forced-vibration testing was performed to measure the dynamic responses (e.g. natural frequencies, story drifts and column/beam rotations). Additionally, the dynamic responses of the retrofitted frame were compared to those of the non-retrofitted frame to investigate effectiveness of the retrofit system. The experimental results demonstrate that the retrofit system installed on the first story columns contributed to reducing story drifts and column rotations. Additionally, the retrofit scheme helped mitigate damage concentration on the first story columns as compared to the non-retrofitted frame.

• 한국지진공학회논문집
• /
• 제23권2호
• /
• pp.119-129
• /
• 2019

Many reinforced concrete (RC) buildings constructed prior to 1980's lack important features guaranteeing ductile response under earthquake excitation. Structural components in such buildings, especially columns, do not satisfy the reinforcement details demanded by current seismic design codes. Columns with deficient reinforcement details may suffer significant damage when subjected to cyclic lateral loads. They can also experience rapid lateral strength degradation induced by shear failure. The objective of this study is to accurately simulate the load-deformation response of RC columns experiencing shear failure. In order to do so, model parameters are calibrated to the load-deformation response of 40 RC column specimens failed in shear. Multivariate stepwise regression analyses are conducted to develop the relationship between the model parameters and physical parameters of RC column specimens. It is shown that the proposed predictive equations successfully estimated the model parameters of RC column specimens with great accuracy. The proposed equations also showed better accuracy than the existing ones.

• 한국지진공학회논문집
• /
• 제28권2호
• /
• pp.113-119
• /
• 2024

Existing reinforced concrete buildings with seismically deficient column details affect the overall behavior depending on the failure type of column. This study aims to develop and validate a machine learning-based prediction model for the column failure modes (shear, flexure-shear, and flexure failure modes). For this purpose, artificial neural network (ANN), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) models were used, considering previously collected experimental data. Using four machine learning methodologies, we developed a classification learning model that can predict the column failure modes in terms of the input variables using concrete compressive strength, steel yield strength, axial load ratio, height-to-dept aspect ratio, longitudinal reinforcement ratio, and transverse reinforcement ratio. The performance of each machine learning model was compared and verified by calculating accuracy, precision, recall, F1-Score, and ROC. Based on the performance measurements of the classification model, the RF model represents the highest average value of the classification model performance measurements among the considered learning methods, and it can conservatively predict the shear failure mode. Thus, the RF model can rapidly predict the column failure modes with simple column details.

• Earthquakes and Structures
• /
• 제18권1호
• /
• pp.113-127
• /
• 2020

In this study, the behavior of external beam-column joints reinforced by plain and deformed bars with non-seismic reinforcement details is investigated and compared. The beam-column joints represented in this study include a benchmark specimen by seismic details in accordance with ACI 318M-11 requirements and four other deficient specimens. The main defects of the non-seismic beam-column joints included use of plain bar, absence of transverse steel hoops, and the anchorage condition of longitudinal reinforcements. The experimental results indicate that using of plain bars in non-seismic beam-column joints has significantly affected the failure modes. The main failure mode of the non-seismic beam-column joints reinforced by deformed bars was the accumulation of shear cracks in the joint region, while the failure mode of the non-seismic beam-column joints reinforced by plain bars was deep cracks at the joint face and intersection of beam and column and there was only miner diagonal shear cracking at the joint region. In the other way, use of plain bars for reinforcing concrete can cause the behavior of the substructure to be controlled by slip of the beam longitudinal bars. The experimental results show that the ductility of non-seismic beam-column joints reinforced by plain bars has not decreased compared to the beam-column joints reinforced by deformed bars due to lack of mechanical interlock between plain bars and concrete. Also it can be seen a little increase in ductility of substructure due to existence of hooks at the end of the development length of the bars.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제18권4호
• /
• pp.980-997
• /
• 2024

To improve the brightness of images and reveal hidden information in dark areas is the main objective of low-light image enhancement (LLIE). LLIE methods based on deep learning show good performance. However, there are some limitations to these methods, such as the complex network model requires highly configurable environments, and deficient enhancement of edge details leads to blurring of the target content. Single-scale feature extraction results in the insufficient recovery of the hidden content of the enhanced images. This paper proposed an edge detection-based multi-scale feature enhancement network for LLIE (EDMFEN). To reduce the loss of edge details in the enhanced images, an edge extraction module consisting of a Sobel operator is introduced to obtain edge information by computing gradients of images. In addition, a multi-scale feature enhancement module (MSFEM) consisting of multi-scale feature extraction block (MSFEB) and a spatial attention mechanism is proposed to thoroughly recover the hidden content of the enhanced images and obtain richer features. Since the fused features may contain some useless information, the MSFEB is introduced so as to obtain the image features with different perceptual fields. To use the multi-scale features more effectively, a spatial attention mechanism module is used to retain the key features and improve the model performance after fusing multi-scale features. Experimental results on two datasets and five baseline datasets show that EDMFEN has good performance when compared with the stateof-the-art LLIE methods.