• Korean Journal of Remote Sensing
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• v.36 no.2_1
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• pp.179-197
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• 2020

Topographic normalization reduces the terrain effects on reflectance by adjusting the brightness values of the image pixels to be equal if the pixels cover the same land-cover. Topographic effects are induced by the imaging conditions and tend to be large in high mountainousregions. Therefore, image analysis on mountainous terrain such as estimation of wildfire damage assessment requires appropriate topographic normalization techniques to yield accurate image processing results. However, most of the previous studies focused on the evaluation of topographic normalization on satellite images with moderate-low spatial resolution. Thus, the alleviation of topographic effects on multi-temporal high-resolution images was not dealt enough. In this study, the evaluation of terrain normalization was performed for each band to select the optimal technical combinations for rapid and accurate wildfire damage assessment using PlanetScope images. PlanetScope has considerable potential in the disaster management field as it satisfies the rapid image acquisition by providing the 3 m resolution daily image with global coverage. For comparison of topographic normalization techniques, seven widely used methods were employed on both pre-fire and post-fire images. The analysis on bi-temporal images suggests the optimal combination of techniques which can be applied on images with different land-cover composition. Then, the vegetation index was calculated from the images after the topographic normalization with the proposed method. The wildfire damage detection results were obtained by thresholding the index and showed improvementsin detection accuracy for both object-based and pixel-based image analysis. In addition, the burn severity map was constructed to verify the effects oftopographic correction on a continuous distribution of brightness values.

• Journal of Environmental Health Sciences
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• v.44 no.4
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• pp.301-314
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• 2018

Objectives: The aim of this study was to introduce the overall progress of exposure assessment to humidifier disinfectant (HD); to present participants' demographic characteristics, exposure characteristics to humidifier disinfectant, and exposure classification; and furthermore to compare those characteristics between survivors and non-survivors. Methods: An assessment of environmental exposure to HD was conducted using modified HD-specific questionnaires that had been previously validated. We analyzed the data from 4,482 participants who had been potentially exposed to HD and had registered with the KEITI (Korea Environmental Industry & Technology Institute) from September 2016 to May 2018 (the fourth survey). Environmental exposure assessments were performed as follows: 1) contact with participants, 2) environmental exposure assessment though face-to-face interviews, 3) assessment review and coding, and 4) exposure rating. Results: Overall, survivors made up 77.1% (3,457 subjects) and non-survivors made up 22.9% (1,025 subjects). When compared with the survivors, non-survivors had a higher proportion of subjects aged >60 years and subjects who answered as suffering lung damage and having purchased HD because it is "Beneficial to health" (p<0.05). For the exposure characteristics compared to survivors, non-survivors had a higher proportion of cases of distance from humidifier to face being less that one meter and the spray direction being toward the face (p<0.05). Overall, respondents who used the "Oxy Ssak Ssak New Gaseupgi Dangbun", "Aekyung Gaseupgi Mate", "Homeplus Gaseupgi Chungjungje", and "E-Mart Gaseupgi Salgyunje" products made up 66.1, 12.3, 4.0, and 3.6%, respectively, and 72.5% of respondents used products with PHMG as the active chemical. When compared with survivors, non-survivors had a higher proportion of use of "Oxy Ssak Ssak New Gaseupgi Dangbun" but a lower proportion of use of products with CMIT/MIT, PGH, or PHMG as the active chemical. Conclusions: This study provided demographic characteristics and exposure assessment for applicants who have been injured by HD. In spite of the limitations of performing past exposure assessment through a questionnaire survey, such as recall bias, useful results may be obtained by comparing survivors with non-survivors. Further studies such as the exposure rating method and so on are necessary to assess past exposure to HD.

• Smart Structures and Systems
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• v.24 no.5
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• Computers and Concrete
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• v.33 no.4
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• pp.341-348
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• 2024

Ultra-high-performance concrete (UHPC) has received remarkable attentions in civil infrastructure due to its unique mechanical characteristics and durability. UHPC gains increasingly dominant in essential structural elements, while its unique properties pose challenges for traditional inspection methods, as damage may not always manifest visibly on the surface. As such, the need for robust inspection techniques for detecting cracks in UHPC members has become imperative as traditional methods often fall short in providing comprehensive and timely evaluations. In the era of artificial intelligence, computer vision has gained considerable interest as a powerful tool to enhance infrastructure condition assessment with image and video data collected from sensors, cameras, and unmanned aerial vehicles. This paper presents a computer vision-based approach employing deep learning to detect cracks in UHPC beams, with the aim of addressing the inherent limitations of traditional inspection methods. This work leverages computer vision to discern intricate patterns and anomalies. Particularly, a convolutional neural network architecture employing transfer learning is adopted to identify the presence of cracks in the beams. The proposed approach is evaluated with image data collected from full-scale experiments conducted on UHPC beams subjected to flexural and shear loadings. The results of this study indicate the applicability of computer vision and deep learning as intelligent methods to detect major and minor cracks and recognize various damage mechanisms in UHPC members with better efficiency compared to conventional monitoring methods. Findings from this work pave the way for the development of autonomous infrastructure health monitoring and condition assessment, ensuring early detection in response to evolving structural challenges. By leveraging computer vision, this paper contributes to usher in a new era of effectiveness in autonomous crack detection, enhancing the resilience and sustainability of UHPC civil infrastructure.

• Journal of Korean Society of Water and Wastewater
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• v.35 no.6
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• pp.389-403
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• 2021

In this study, dual drainage system based runoff model was established for W-drainage area in G-si, and considering the various rainfall characteristics determined using Huff and Mononobe methods, the degree of flooding in the target area was analyzed and the risk was compared and analyzed through the risk matrix method. As a result, the Monobe method compared to the Huff method was analyzed to be suitable analysis for flooding of recent heavy rain, and the validity of the dynamic risk assessment considering the weight of the occurrence probability as the return period was verified through the risk matrix-based analysis. However, since the definition and estimating criteria of the flood risk matrix proposed in this study are based on the return period for extreme rainfall and the depth of flooding according to the results of applying the dual drainage model, there is a limitation in that it is difficult to consider the main factors which are direct impact on inland flooding such as city maintenance and life protection functions. In the future, if various factors affecting inland flood damage are reflected in addition to the amount of flood damage, the flood risk matrix concept proposed in this study can be used as basic information for preparation and prevention of inland flooding, as well as it is judged that it can be considered as a major evaluation item in the selection of the priority management area for sewage maintenance for countermeasures against inland flooding.

• Journal of Environmental Health Sciences
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• v.40 no.5
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• pp.355-366
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• 2014

Objectives: This study seeks to evaluate the vulnerability assessment of the human health sector for $PM_{10}$, which is reflected in the regional characteristics and related disease mortality rates for $PM_{10}$ in Busan over the period of 2006-2010. Methods: According to the vulnerability concept suggested by the Intergovernmental Panel on Climate Change (IPCC), vulnerability to $PM_{10}$ is comprised of the categories of exposure, sensitivity, and adaptive capacity. The indexes of the exposure and sensitivity categories indicate positive effects, while the adaptive capacity index indicates a negative effect on vulnerability to $PM_{10}$. Variables of each category were standardized by the rescaling method, and each regional relative vulnerability was computed through the vulnerability index calculation formula. Results: The regions with a high exposure index are Jung-Gu (transportation region) and Saha-Gu (industrial region). Major factors determining the exposure index are the $PM_{10}$ concentration, days of $PM_{10}{\geq}50$, ${\mu}g/m^3$, and $PM_{10}$ emissions. The regions that show a high sensitivity index are urban and rural regions; these commonly have a high mortality rate for related disease and vulnerable populations. The regions that have a high adaptive capacity index are Jung-Gu, Gangseo-Gu, and Busanjin-Gu, all of which have a high level of economic/welfare/health care factors. The high-vulnerability synthesis of the exposure, sensitivity, and adaptive capacity indexes show that Dong-Gu and Seo-Gu have a risk for $PM_{10}$ potential effects and a low adaptive capacity. Conclusions: This study presents the vulnerability index to $PM_{10}$ through a relative comparison using quantitative evaluation to draw regional priorities. Therefore, it provides basic data to reflect environmental health influences in favor of an adaptive policy limiting damage to human health caused by vulnerability to $PM_{10}$.

• Journal of Environmental Health Sciences
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• v.40 no.1
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• pp.27-37
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• 2014

Objectives: On September 27, 2012, leakage of anhydrous hydrofluoric acid occurred in a chemical plant in the Gumi National Industrial Complex. Following the accident, local factory workers and residents complained of abnormal health conditions. In addition, visual discolorations were widely observed in crops and trees in surrounding areas. The main objectives of the present study were to identify the area that was affected by the spill using data obtained from plants, soil, and water samples after the accident. Methods: Fluoride concentrations were analyzed in pine tree needles, soil, nearby streams, ponds and reservoirs collected from an area within a radius of three kilometers from the plant where the leak occurred. Fluoride concentrations in the air at the time of leakage were then estimated from fluoride concentrations that were measured in the pine tree needles. A Kriged map was developed to describe the spatial distribution of hydrofluoric acid at the time of the leakage and was compared with the area designated as a Special Disaster Zone by the government. Results: The Special Disaster Zone did not include all the affected area that was estimated by the Kriged map. Analytical results of the environmental samples also supported this discrepancy. Conclusion: Using plants, atmospheric concentrations of fluoride at the time of the leakage could be estimated. For the area that was identified as affected, further public health risk assessment and environmental risk assessment should be considered. Also, in the absence of air monitoring at the time of leakage, studies employing plants may be conducted in order to better understand the spatial extent and severity of the contamination.

• Computers and Concrete
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• v.25 no.1
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• pp.15-27
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• 2020

During the past decades, the application of acoustic emission techniques (AET) through the diagnosis and monitoring of the fracture process in materials has been attracting considerable attention. AET proved to be operative among the other non-destructive testing methods for various reasons including their practicality and cost-effectiveness. Concrete and rock structures often demand thorough and real-time assessment to predict and prevent their damage nucleation and evolution. This paper presents an overview of the work carried out on the use of AE as a monitoring technique to form a comprehensive insight into its potential application in brittle materials. Reported properties in this study are crack growth behavior, localization, damage evolution, dynamic character and structures monitoring. This literature review provides practicing engineers and researchers with the main AE procedures to follow when examining the possibility of failure in civil/resource structures that rely on brittle materials.

• Smart Structures and Systems
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• v.26 no.2
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• pp.175-184
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• 2020

Conventional Monte Carlo simulation-based methods for seismic risk assessment of water networks often require excessive computational time costs due to the hydraulic analysis. In this study, an Artificial Neural Network-based surrogate model was proposed to efficiently evaluate the flow-based system reliability of water distribution networks. The surrogate model was constructed with appropriate training parameters through trial-and-error procedures. Furthermore, a deep neural network with hidden layers and neurons was composed for the high-dimensional network. For network training, the input of the neural network was defined as the damage states of the k-dimensional network facilities, and the output was defined as the network system performance. To generate training data, random sampling was performed between earthquake magnitudes of 5.0 and 7.5, and hydraulic analyses were conducted to evaluate network performance. For a hydraulic simulation, EPANET-based MATLAB code was developed, and a pressure-driven analysis approach was adopted to represent an unsteady-state network. To demonstrate the constructed surrogate model, the actual water distribution network of A-city, South Korea, was adopted, and the network map was reconstructed from the geographic information system data. The surrogate model was able to predict network performance within a 3% relative error at trained epicenters in drastically reduced time. In addition, the accuracy of the surrogate model was estimated to within 3% relative error (5% for network performance lower than 0.2) at different epicenters to verify the robustness of the epicenter location. Therefore, it is concluded that ANN-based surrogate model can be utilized as an alternative model for efficient seismic risk assessment to within 5% of relative error.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.2
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• pp.149-160
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• 2024

Severe casualties and property damage are occurring due to urban floods caused by extreme rainfall. However, there is a lack of research on preparedness, appropriate estimation of flood damages, assessment of losses, and compensation. Particularly, the flood damage estimation methods used in the USA and Japan show significant differences from the domestic situation, highlighting the need for methods tailored to the Korean context. This study addresses these issues by developing an optimized flood damage estimation technique based on the building characteristics. Utilizing the flood prediction solution developed by the Korea Institute of Science and Technology Information (KISTI), we have established an optimal flood damage estimation technology. We introduced a methodology for flood damage estimation by incorporating vulnerability curves based on the inventory of structures and apply this technique to real-life cases. The results show that our approach yields more realistic outcomes compared to the flood damage estimation methods employed in the USA and Japan. This research can be practically applied to procedures for flood damage in urban basement residences, and it is expected to contribute to establishing appropriate response procedures in cases of public grievances.