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.

This paper aims to quantify the retrofit effect of the Bolt Prefabricated Concrete-Filled Tube reinforcement method on non-seismic school reinforced concrete building through static cyclic loading experiments. To achieve the objective, two-story specimens including a non-retrofitted frame(NRF) and a Bolt Prefabricated Concrete-Filled Tube Reinforcement Frame(BCRF) were tested under static cyclic loading, and the lateral resistant capacities were compared in terms of maximum strength, initial stiffness, effective stiffness, and total energy dissipation. In addition, the load-displacement curves were compared to the story drift limit specified in Seismic Performance Evaluation and Retrofit Manual for School Facilities to investigate if the retrofitted frame was satisfied in target performance(life safety). Experimental results showed that BCRF successfully met the target performance, with a 200% increase in maximum strength and a 300% increase in energy dissipation capacity. Additionally, both initial stiffness and effective stiffness improved by more than 30% compared to NRF. Furthermore, BCRF exhibited an effect that delayed the occurrence of bond failure.

Structures compromised by a seismic event may be susceptible to aftershocks or subsequent occurrences within a particular duration. Considering that the shape ratios of sections, such as column shape ratio (CSR) and wall shape ratio (WSR), significantly influence the behavior of reinforced concrete (RC) piloti structures, it is essential to determine the best appropriate methodology for these structures. The seismic evaluation of piloti structures was conducted to measure seismic performance based on section shape ratios and inter-story drift ratio (IDR) standards. The diverse machine-learning models were trained and evaluated using the dataset, and the optimal model was chosen based on the performance of each model. The optimal model was employed to predict seismic performance by adjusting section shape ratios and output parameters, and a recommended approach for section shape ratios was presented. The optimal section shape ratios for the CSR range from 1.0 to 1.5, while the WSR spans from 1.5 to 3.33, regardless of the inter-story drift ratios.

Many school buildings are vulnerable to earthquakes because they were built before mandatory seismic design was applied. This study uses machine learning to develop an algorithm that rapidly constructs an optimal reinforcement scheme with simple information for non-ductile reinforced concrete school buildings built according to standard design drawings in the 1980s. We utilize a decision tree (DT) model that can conservatively predict the failure type of reinforced concrete columns through machine learning that rapidly determines the failure type of reinforced concrete columns with simple information, and through this, a methodology is developed to construct an optimal reinforcement scheme for the confinement ratio (CR) for ductility enhancement and the stiffness ratio (SR) for stiffness enhancement. By examining the failure types of columns according to changes in confinement ratio and stiffness ratio, we propose a retrofit scheme for school buildings with masonry walls and present the maximum applicable stiffness ratio and the allowable range of stiffness ratio increase for the minimum and maximum values of confinement ratio. This retrofit scheme construction methodology allows for faster construction than existing analysis methods.

Due to seismically deficient details, existing reinforced concrete structures have low lateral resistance capacities. Since these building structures suffer an increase in axial loads to the main structural element due to the green retrofit (e.g., energy equipment/device, roof garden) for CO2 reduction and vertical extension, building capacities are reduced. This paper proposes a machine-learning-based methodology for allowable ranges of axial loading ratio to reinforced concrete columns using simple structural details. The methodology consists of a two-step procedure: (1) a machine-learning-based failure detection model and (2) column damage limits proposed by previous researchers. To demonstrate this proposed method, the existing building structure built in the 1990s was selected, and the allowable range for the target structure was computed for exterior and interior columns.

In South Korea, over 400,000 Non-building Structures are inadequately managed and exposed to potential risks due to insufficient inspection systems, leading to an increase in accidents and significant losses of life and property. Therefore, it is crucial for users to conduct proactive self-inspections to identify and mitigate potential hazards. This study reclassified Non-building Structures into four main categories by analyzing their structural characteristics and associated risks through statistical analysis. Among these, retaining walls, which account for the largest proportion, were systematically analyzed to identify common damage patterns. Based on this analysis, self-inspection checklists were developed for both non-experts and experts. The proposed process involves an initial visual inspection using a simple non-expert checklist, followed by a more detailed expert-level inspection if any anomalies are detected. The reliability of this process was validated through approximately 120 validation processes.

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.

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.

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.

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.

In this study, in order to establish a strategy for developing an fire following earthquake risk assessment method that can utilize domestic public databases(building datas, etc.), the method of calculating the ignition and fire-spread among the fire following earthquake risk assessment methodologies proposed by past researchers is investigated After investigating and analyzing the methodology used in the HAZUS-MH earthquake model in the United States and the fire following earthquake risk assessment methodology in Japan, based on this, a database such as a domestic building data utilized to an fire following earthquake risk assessment method suitable for domestic circumstances (planned) was suggested.

Existing reinforced concrete (RC) frame buildings have seismic vulnerabilities because of seismically deficient details. In particular, since cumulative damage caused by successive earthquakes causes serious damage, repair/retrofit rehabilitation studies for successive earthquakes are needed. This study investigates the repair effect of fiber-reinforced polymer jacketing system for the seismically-vulnerable building structures under successive earthquakes. The repair modeling method developed and validated from the previous study was implemented to the building models. Additionally, the main parameters of the FRP jacketing system were selected as the number of FRP layers associated with the confinement effects and the installation location. To define the repair effects of the FRP jacketing system with the main parameters, this study conducted nonlinear time-history analyses for the building structural models with the various repairing scenarios. Based on this investigation, the repair effects of the damaged building structures were significantly affected by the damage levels induced from the mainshocks regardless of the retrofit scenarios.

Existing reinforced concrete building structures constructed before 1988 have seismically-deficient reinforcing details, which can lead to the premature failure of the columns and beam-column joints. The premature failure was resulted from the inadequate bonding performance between the reinforcing bars and surrounding concrete on the main structural elements. This paper aims to quantify the bond-slip effect on the dynamic responses of reinforced concrete frame models using finite element analyses. The bond-slip behavior was modeled using an one-dimensional slide line model in LS-DYNA. The bond-slip models were varied with the bonding conditions and failure modes, and implemented to the well-validated finite element models. The dynamic responses of the frame models with the several bonding conditions were compared to the validated models reproducing the actual behavior. It verifies that the bond-slip effects significantly affected the dynamic responses of the reinforced concrete building structures.

In this study, to verify the structural performance of the Composite Joint System (CJS) hybrid structural model, a cyclic load test was performed and evaluated and verified through the test. To verify the structural performance of the CJS hybrid structural systems’ joint and evaluate the seismic performance, four three-dimensional real-size specimens were developed with three internal beam-column specimens and one external beam-column specimen. The three interior column specimens were classified by different methods of joining the upper column and lower column, and the same bonding method as the primary specimen was used for the exterior column. The structural performances in terms of drift, strength, and energy dissipation capacity were analyzed and compared based on the experimental results. From the displacement-based loading experiment, all specimens showed a lateral drift of 4.0% without any significant strength drop and stable energy dissipation capacity.

This paper presents the effect on the inelastic behavior and structural performance of concrete and filled steel pipe through a numerical method for reliable judgment under various load conditions of the CJS composite structural system. Variable values optimized for the CJS synthetic structural system and the effects of multiple variables used for finite element analysis to present analytical modeling were compared and analyzed with experimental results. The Winfrith concrete model was used as a concrete material model that describes the confinement effect well, and the concrete structure was modeled with solid elements. Through geometric analysis of shell and solid elements, rectangular steel pipe columns and steel elements were modeled as shell elements. In addition, the slip behavior of the joint between the concrete column and the rectangular steel pipe was described using the Surface-to-Surface function. After finite element analysis modeling, simulation was performed for cyclic loading after assuming that the lower part of the foundation was a pin in the same way as in the experiment. The analysis model was verified by comparing the calculated analysis results with the experimental results, focusing on initial stiffness, maximum strength, and energy dissipation capability.

This paper aims to develop numerical models for seismically-deficient reinforced concrete columns retrofitted using a fiber-reinforced polymer jacketing system under blast loading scenarios. To accomplish the research goal, a coupling model reproducing blast loads was developed and implemented to the column model. The column model was validated with a past experimental study, and the blast responses were compared to the numerical responses produced by past researchers. The validated modeling method was implemented to the non-retrofitted and retrofitted column models to estimate the effectiveness of the retrofit system. Based on the numerical responses, the retrofit system can significantly reduce the peak dynamic responses under a given blast loading scenario.

This study develops finite element models for seismically-deficient reinforced concrete building frame retrofitted using fiber-reinforced polymer jacketing system and validates the finite element models with full-scale dynamic test for as-built and retrofitted conditions. The bond-slip effects measured from a past experimental study were modeled using one-dimensional slide line model, and the bond-slip models were implemented to the finite element models. The finite element model can predict story displacement and inter-story drift ratio with slight simulation variation compared to the measured responses from the full-scale dynamic tests.

A new lighting support structure composing of two-way wires and pulley, a pulley-type wireway system, was developed to improve the seismic performance of a ceiling type lighting equipment. This study verifies the seismic performance of the pulley-type wireway system using a numerical approach. A theoretical model fitted to the physical features of the newly-developed system was proposed, and it was utilized to compute a frictional coefficient between the wire and pulley sections under tension forces. The frictional coefficient was implemented to a finite element model representing the pulley-type wireway system. Using the numerical model, the seismic responses of the pulley-type wireway system were compared to those of the existing lighting support structure, a one-way wire system. The addition of the pulley component resulted in the increasement of energy absorption capacity as well as friction effect and showed in significant reduction in maximum displacement and oscillation after the peak responses. Thus, the newly-developed wireway system can minimize earthquake-induced vibration and damage on electric equipment.