• 한국안전학회지
• /
• 제33권5호
• /
• pp.150-156
• /
• 2018

As the types and usage of chemical increase, modern countries should protect their health and environment from the risk of hazardous chemical. Chemical accidents not only affect humans but also cause huge losses to the environment. Moreover, since its effects do not end in a short period of time, it is necessary to identify the extent of the damage and establish a prevention and response system in advance. In 2015, the Chemical Substances Management Act provided a system for assessing the impact on the people and the environment around the workplace. However, it is difficult to quantitatively evaluate the impact on environmental factors such as vegetation and aquatic, with the current hazard assessment methods. The purpose of this study is to analyze the quantitative risk of environmental receptors. This study improved the existing risk assessment formula by using the environmental vulnerability index and established the end point concentration criterion which can estimate the damage range to environmental media. To verify the results of the study, a virtual accident scenario was selected and a case study was conducted. As a result, the extent of impact on the environmental medium can be calculated, and the degree of environmental risk of the zone can be quantified through the risk analysis considering the environmental vulnerability. This study is expected to increase the reliability of the reliability of the existing risk anaylsis method beacause it is a risk analysis method that can be applied when the environmental factors are absolutely necessary and when the residents and environment are complex.

• Structural Engineering and Mechanics
• /
• 제32권4호
• /
• pp.489-500
• /
• 2009

The aim of this paper is to compare wavelet, kurtosis and pseudofractal based techniques for structural health monitoring in the presence of measurement noise. A detailed comparison and assessment of these techniques have been carried out in this paper through numerical experiments for the calibration of damage extent of a simply supported beam with an open crack serving as an illustrative example. The numerical experiments are deemed critical due to limited amount of experimental data available in the field of singularity based detection of damage. A continuous detectibility map has been proposed for comparing various techniques qualitatively. Efficiency surfaces have been constructed for wavelet, kurtosis and pseudofractal based calibration of damage extent as a function of damage location and measurement noise level. Levels of noise have been identified for each technique where a sudden drop of calibration efficiency is observed marking the onset of damage masking regime by measurement noise.

• Structural Engineering and Mechanics
• /
• 제91권5호
• /
• pp.443-457
• /
• 2024

At present, the traditional concrete surface inspection methods based on artificial vision have the problems of high cost and insecurity, while the computer vision methods rely on artificial selection features in the case of sensitive environmental changes and difficult promotion. In order to solve these problems, this paper introduces deep learning technology in the field of computer vision to achieve automatic feature extraction of structural damage, with excellent detection speed and strong generalization ability. The main contents of this study are as follows: (1) A method based on DeepLabV3+ convolutional neural network model is proposed for surface detection of post-earthquake structural damage, including surface damage such as concrete cracks, spaling and exposed steel bars. The key semantic information is extracted by different backbone networks, and the data sets containing various surface damage are trained, tested and evaluated. The intersection ratios of 54.4%, 44.2%, and 89.9% in the test set demonstrate the network's capability to accurately identify different types of structural surface damages in pixel-level segmentation, highlighting its effectiveness in varied testing scenarios. (2) A semantic segmentation model based on DeepLabV3+ convolutional neural network is proposed for the detection and evaluation of post-earthquake structural components. Using a dataset that includes building structural components and their damage degrees for training, testing, and evaluation, semantic segmentation detection accuracies were recorded at 98.5% and 56.9%. To provide a comprehensive assessment that considers both false positives and false negatives, the Mean Intersection over Union (Mean IoU) was employed as the primary evaluation metric. This choice ensures that the network's performance in detecting and evaluating pixel-level damage in post-earthquake structural components is evaluated uniformly across all experiments. By incorporating deep learning technology, this study not only offers an innovative solution for accurately identifying post-earthquake damage in civil engineering structures but also contributes significantly to empirical research in automated detection and evaluation within the field of structural health monitoring.

• Wind and Structures
• /
• 제2권3호
• /
• pp.133-150
• /
• 1999

Significant costs to the public and private sectors due to recent extreme wind events have motivated the need for systematic post-hurricane damage data collection and analysis. Current post disaster data are collected by many different interested groups such as government agencies, voluntary disaster relief agencies, representatives of media companies, academicians and companies in the private sector. Each group has an interest in a particular type of data. However, members of each group collect data using different techniques. This disparity in data is not conducive to quantifying damage data and, therefore, inhibits the statistical and spatial description of damage and comparisons of damage among different extreme wind events. The data collection does not allow comparisons of data or results of analyses within a group and also prohibits comparison of damage data and information among different groups. Typically, analyses of data from a given event lead to different conclusion depending upon the definition of damage used by individual investigators and the type of data collected making it difficult for members of groups to compare the results of their analyses with a common language and basis. A formal method of data collection and analysis-within any single group-would allow comparisons to be made among different individuals, hazardous events and eventually among different groups, thus facilitating the management and reduction of damage due to future disaster. This research introduces a definition of damage to single family dwellings, and a common method of data collection and analysis suited for groups interested in regional characterization of damage. The current state-of-data is presented and a method for data collection is recommended based on these existing data collection methods. A fixed-scale damage index is proposed to consider the damage to a dwelling's feature. Finally, the damage index is applied to three dwellings damaged by Hurricane Iniki (1992). The damage index reflects the reduced functionality of a structure as a single family detached dwelling and provides a means to evaluate regional damage due to a single event or to compare damage due to events of different severity. Evaluation of the damage index and the data available support recommendation for future data collection efforts.

• Structural Engineering and Mechanics
• /
• 제23권1호
• /
• pp.75-95
• /
• 2006

Two-step identification approaches for effective bridge health monitoring are proposed to alleviate the issues associated with many unknown parameters faced in real structures and to improve the accuracy in the estimate results. It is suitable for on-line monitoring scheme, since the damage assessment is not always needed to be carried out whereas the alarming for damages is to be continuously monitored. In the first step for screening potentially damaged members, a damage indicator method based on modal strain energy, probabilistic neural networks and the conventional neural networks using grouping technique are utilized and then the conventional neural networks technique is utilized for damage assessment on the screened members in the second step. The effectiveness of the proposed methods is investigated through a field test on the northern-most span of the old Hannam Grand Bridge over the Han River in Seoul, Korea.

• 환경영향평가
• /
• 제28권6호
• /
• pp.616-625
• /
• 2019

건강영향평가는 개발사업 시행에 따른 사람의 건강에 미치는 영향을 예측하여 건강피해를 최소화하기 위한 목적으로 환경영향평가 내에서 시행되고 있다. 하지만 건강영향평가 시 유해대기오염물질 배출량 산정방법에 대한 일관성 부족으로 건강영향평가 매뉴얼 개선이 필요하나, 아직 개정은 되지 않고 있다. 본 연구는 실제 산업단지 개발 사례 및 기 수행된 건강영향평가를 중심으로 유해대기오염물질 배출량 산정 시 매뉴얼에 제시된 원단위 산정 방법을 다르게 적용한 4가지 사례를 선정하여 각각 배출량을 산정하였다. 각 산정된 배출량을 토대로 CALPUFF 모델을 이용한 확산농도 예측 후, 노출농도를 기준으로 위해성 평가를 시행하였다. 위해성 평가 결과, 배출량 산정 방법별 위해도 수준의 차이가 비교적 크게 나타남에 따라, 배출량 산정시 원단위 적용에 대한 매뉴얼 개선이 필요한 것으로 검토되었다. 또한 배출량 산정 시 근거자료 활용에 대한 일관성, 건강영향평가에 최적화된 배출계수 개발, 현황 조사에 대한 신뢰성 향상이 필요할 것으로 판단된다.

• Smart Structures and Systems
• /
• 제29권1호
• /
• pp.105-116
• /
• 2022

Stay cables play an essential role in cable-stayed bridges. Severe vibrations and/or harsh environment may result in cable failures. Therefore, an efficient structural health monitoring (SHM) solution for cable damage detection is necessary. This study proposes a data-driven method for immediately detecting cable damage from measured cable forces by recognizing pattern transition from the intact condition when damage occurs. In the proposed method, pattern recognition for cable damage detection is realized by time series classification (TSC) using a deep learning (DL) model, namely, the long short term memory fully convolutional network (LSTM-FCN). First, a TSC classifier is trained and validated using the cable forces (or cable force ratios) collected from intact stay cables, setting the segmented data series as input and the cable (or cable pair) ID as class labels. Subsequently, the classifier is tested using the data collected under possible damaged conditions. Finally, the cable or cable pair corresponding to the least classification accuracy is recommended as the most probable damaged cable or cable pair. A case study using measured cable forces from an in-service cable-stayed bridge shows that the cable with damage can be correctly identified using the proposed DL-TSC method. Compared with existing cable damage detection methods in the literature, the DL-TSC method requires minor data preprocessing and feature engineering and thus enables fast and convenient early detection in real applications.

• 한국환경보건학회지
• /
• 제35권4호
• /
• pp.239-248
• /
• 2009

The Institute of Medicine of the National Academies of Science in the United States concluded in its 2004 report that excessive indoor dampness is a public health hazard and that its prevention should be a public health goal. Water damage in buildings, such as leaks from roofs, walls, or windows, may increase indoor moisture levels. Excessive dampness may promote microbial proliferation in indoor environments, increase occupants' exposure to microbial agents, and eventually produce adverse health effects in building occupants. Epidemiological studies to demonstrate the causal association between exposure to indoor microbial agents and health effects require reliable exposure assessment tools. In this review, I discuss various sampling and analytical methods to assess human exposure to biological agents in indoor environments, their strengths and weaknesses, and recent trends in research and practice in the USA.

• 대한토목학회논문집
• /
• 제43권2호
• /
• pp.165-173
• /
• 2023

교량에 발생하는 손상에 대한 관리 및 평가는 정기적인 점검으로 작성된 보고서와 외관조사망도 및 손상물량표를 포함한 점검 및 진단자료에 기초한다. 이러한 자료 대부분은 2D 기반의 문서형식으로 작성되어 있고, 표준화된 방식으로 디지털화하기 어려워 정해진 목적 외의 활용이 쉽지 않다. 이에 본 연구에서는 점검자료를 기반으로 손상을 포함한 BIM 기반 교량모델을 구축하는 방법을 제시하고, BIM 표준을 준용하는 유지관리용 건설정보교환표준인 COBie (Construction Operations Building Information Exchange)를 사용하여 모델로부터 도출한 스프레드시트 데이터 형식의 손상정보들을 교량모델과 연계하여 관리하고 활용하는 방법을 제시하였다. 또한 전술한 방법들을 이용해 손상이 발생한 교량 상부구조 각 부위별 상태등급을 디지털 데이터 기반으로 자동화된 방법으로 평가하는 방법을 제안하였다. 본 연구에서 제안된 방법들은 PSC I형 콘크리트 교량의 상부구조를 대상으로 검증이 이루어졌으며 그 실효성이 검증되었다.

• 비파괴검사학회지
• /
• 제27권6호
• /
• pp.483-500
• /
• 2007

Emerging sensor-based structural health monitoring (SHM) technology can play an important role in inspecting and securing the safety of aging civil infrastructure, a worldwide problem. However, implementation of SHM in civil infrastructure faces a significant challenge due to the lack of suitable sensors and reliable methods for interpreting sensor data. This paper reviews recent efforts and advances made in addressing this challenge, with example sensor hardware and software developed in the author's research center. It is proposed to integrate real-time continuous monitoring using on structure sensors for global structural integrity evaluation with targeted NDE inspection for local damage assessment.