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.

The electric discharge experiment, known as the Miller-Urey experiment, is one of the experiments to understand the origin of life on Earth. The experiment involved simulating the Earth’s early atmosphere by introducing methane(CH4), ammonia(NH3), and nitrogen(N2) gases, and applying energy through electric discharge. Resulting solution was found to contain amino acids such as glycine(C2H5NO2), alanine( C3H7NO2), histidine(C6H9N3O2), proline(C5H9NO2), and valine(C5H11NO2). These amino acids were compared with the results of the recent experiment (Parker et al. 2014). Interestingly, the electric discharge produced C2 swan band and CN emission and it was newly found in gas phase. These two emission bands are commonly observed in comets.

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.

본 연구는 배수지에서 저수조를 포함한 대수용가의 수도꼭지에 이르는 구간에서 탁도와 잔류염소 농도의 시간적⋅공간적 변화를 분석하였다. 모니터링은 배수지, 중블록 유입 지점, 대수용가 유입 지점, 저수조 유출 지점, 수도꼭지 등 5개 지점에서 수행되었으며, 유량, 잔류염소, 탁도, pH, 전기전도도, 온도를 측정하였다. 연구 결과, 수돗물 이동 경로를 따라 잔류염소 농도는 점차 감소하고 탁도는 증가하는 경향을 보였다. 특히, 비업무시간대에는 수돗물 정체로 인한 수질 저하가 확인되었다. 또한, 저수조의 건전성을 평가하기 위해 반응계수를 산출한 결과, 시간에 따른 저수조 내부 건전성 저하와 수질 악화 가능성이 확인되었다. 본 연구는 수돗물의 이동 거리, 사용 시간대, 유량 변화 및 공급 방식에 따라 수질이 달라질 수 있음을 보여주며, 저수조에 의존하는 지역에서는 안전한 수돗물 공급을 위해 지속적인 모니터링과 관리가 필요함을 시사한다.

2023년 발표된 해양사고 통계에 따르면 해양 사고로 인한 인명피해는 소폭 감소하였지만, 해양 사고 발생 건수는 2021 ~ 2023년 까지 꾸준히 증가하고 있다. 이러한 해양사고의 예방 및 조치를 위해 정부 기관, 지자체 등에서는 안전 사업 추진 및 CCTV 관제 시스템을 구축하고 있다. 그 중 CCTV 관제 시스템은 야간 환경에서 가시거리 감소, 객체 식별의 어려움 등의 한계를 가지고 있다. 이러한 한계를 극복하기 위해 본 연구에서는 저조도 개선 알고리즘을 활용한 야간 해양 환경 개선에 관한 연구를 진행했다. 연구 진행간 저조도 개선 성 능과 실시간 안전 모니터링을 위한 영상 데이터의 처리에 초점을 맞췄다. 총 3가지의 저조도 개선 알고리즘과 2가지의 딥러닝 모델 경량 화 기법을 활용하여 최적의 실시간 저조도 개선 알고리즘을 선정하였다. 본 연구에서는 Bread 알고리즘에 Tensor RT 기법을 적용한 경우 에서 SSIM 0.7, FPS 100을 기록하며 저조도 개선 성능 및 실시간 데이터 처리에 가장 적합한 방안인 것을 입증하였다.

2022년 기준 국내 폐타이어 발생량은 약 37만톤으로 그 중 88.9% 인 약 32만 9천톤이 재활용되는 것으로 조사되었다. 하지만 이 중 약 75%가 시멘트소성로용 등 열이용 분야에 사용되었다. 폐타이어는 대부분 고무와 플라스틱으로 이루어져 있기 때문에, 고온에서 분 해되면서 다양한 유해가스와 오염물질이 발생할 수 있고, 이러한 공해물질은 적극적으로 관리되지 않으면 대기오염, 수질 오염 등 다 양한 환경문제를 발생시킬 수 있다. 때문에 친환경적이고 지속적인 재활용에 대한 필요성이 대두되고 있다. 폐타이어 고무 분말을 아스팔트 혼합물의 골재 일부로 치환하여 재활용하는 접근 방식은 환경에 미치는 영향을 완화할 뿐만 아니라 천연 자원의 고갈 측면에서도 긍정적인 영향을 미치는 것으로 판단된다. 따라서 타이어분말을 아스팔트 혼합물에 적용하는 것은 환경 문제를 해결하고 자원 효율성을 높이는 두 가지 이점을 가지고 있다. 폐타이어 분말을 아스팔트 바인더와 아스팔트 혼합물에 적용할 경우 미치는 영향을 평가하기 위하여 TTI의 반사균열 저항성 시험, FN Test를 진행하였다.

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.

섬중베짱이(Tettigonia jungi Storozhenko, Kim & Jeon, 2015)는 제주도와 여서도 등의 남부 섬 지역에만 서식하는 종으로 알려져 있다. 본 연구는 야외조사와 형태 동정, 미토콘드리아 COI을 이용한 분자동정을 통해 서울특별시 마포구 상암동 하늘공원에서 섬중베짱이의 서식을 확인하였다. 유입된 섬중베짱이 개체군들은 주변 환경으로 분산되지 못하고 하늘공원에 고립되어 있다. 이 소규모 개체군들은 최소 하늘공원이 조성된 직후인 2003년부터 고립되어 서식해왔으며, 공원을 조성하는 과정에서 제주도산 억새와 함께 유입되어 정착한 것으로 추측된다.

Biocide dichlofluanid breaks down quickly and accumulates easily in sediment, potentially causing a persistent impact on various marine organisms. We analyzed the potential toxicity of dichlofluanid on major aquaculture species in Korea, Undaria pinnatifida. Female gametophytes of U. pinnatifida were exposed to dichlofluanid at concentrations of 0, 1, 2, 4, 8, 16, and 32 mg L-1, and their survival and relative growth rate were analyzed. The no observed effect concentration (NOEC), lowest observed effect concentration (LOEC), and median lethal concentration (LC50) for female gametophyte survival were determined as 1, 2, and 10.82 (95% CI: 8.87-13.23) mg L-1, respectively. The NOEC, LOEC, and median effective concentration (EC50) for relative growth rate were 1, 2, and 6.58 (95% CI: 6.03-7.17) mg L-1, respectively. Female gametophytes of U. pinnatifida were expected to experience toxic effects at concentrations above 2 mg L-1 of dichlofluanid. These research findings are expected to serve as important reference data for evaluating the toxicity effects of U. pinnatifida in its early life stages when exposed to dichlofluanid.

Organoleptic parameters such as color, odor, and flavor influence consumer perception of drinking water quality. This study aims to evaluate the taste of the selected bottled and tap water samples using an electronic tongue (E-tongue) instead of a sensory test. Bottled and tap water's mineral components are related to the overall preference for water taste. Contrary to the sensory test, the potentiometric E-tongue method presented in this study distinguishes taste by measuring the mineral components in water, and the data obtained can be statistically analyzed. Eleven bottled water products from various brands and one tap water from I city in Korea were evaluated. The E-tongue data were statistically analyzed using multivariate statistical tools such as hierarchical clustering analysis (HCA), principal component analysis (PCA), and partial least squares discriminant analysis (PLS-DA). The results show that the E-tongue method can clearly distinguish taste discrimination in drinking water differing in water quality based on the ion-related water quality parameters. The water quality parameters that affect taste discrimination were found to be total dissolved solids (TDS), sodium (Na+), calcium (Ca2+), magnesium (Mg2+), sulfate (SO4 2-), chloride (Cl-), potassium (K+) and pH. The distance calculation of HCA was used to quantify the differences between 12 different types of drinking water. The proposed E-tongue method is a practical tool to quantitatively evaluate the differences between samples in water quality items related to the ionic components. It can be helpful in quality control of drinking water.

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.