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Mapping Burned Forests Using a k-Nearest Neighbors Classifier in Complex Land Cover (k-Nearest Neighbors 분류기를 이용한 복합 지표 산불피해 영역 탐지)

  • Lee, Hanna ;Yun, Konghyun;Kim, Gihong
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.43 no.6
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    • pp.883-896
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    • 2023
  • As human activities in Korea are spread throughout the mountains, forest fires often affect residential areas, infrastructure, and other facilities. Hence, it is necessary to detect fire-damaged areas quickly to enable support and recovery. Remote sensing is the most efficient tool for this purpose. Fire damage detection experiments were conducted on the east coast of Korea. Because this area comprises a mixture of forest and artificial land cover, data with low resolution are not suitable. We used Sentinel-2 multispectral instrument (MSI) data, which provide adequate temporal and spatial resolution, and the k-nearest neighbor (kNN) algorithm in this study. Six bands of Sentinel-2 MSI and two indices of normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as features for kNN classification. The kNN classifier was trained using 2,000 randomly selected samples in the fire-damaged and undamaged areas. Outliers were removed and a forest type map was used to improve classification performance. Numerous experiments for various neighbors for kNN and feature combinations have been conducted using bi-temporal and uni-temporal approaches. The bi-temporal classification performed better than the uni-temporal classification. However, the uni-temporal classification was able to detect severely damaged areas.

Analysis of Runoff Reduction Effect of Flood Mitigation Policies based on Cost-Benefit Perspective (비용-편익을 고려한 홍수 대응 정책의 유출 저감 효과 분석)

  • Jee, Hee Won;Kim, Hyeonju;Seo, Seung Beom
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.43 no.6
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    • pp.721-733
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    • 2023
  • As the frequency of extreme rainfall events increase due to climate change, climate change adaptation measures have been proposed by the central and local governments. In order to reduce flood damage in urban areas, various flood response policies, such as low impact development techniques and enhancement of the capacity of rainwater drainage networks, have been proposed. When these policies are established, regional characteristics and policy-effectiveness from the cost-benefit perspective must be considered for the flood mitigation measures. In this study, capacity enhancement of rainwater pipe networks and low impact development techniques including green roof and permeable pavement techniques are selected. And the flood reduction effect of the target watershed, Gwanak campus of Seoul National University, was analyzed using SWMM model which is an urban runoff simulation model. In addition, along with the quantified urban flooding reduction outputs, construction and operation costs for various policy scenarios were calculated so that cost-benefit analyses were conducted to analyze the effectiveness of the applied policy scenarios. As a result of cost-benefit analysis, a policy that adopts both permeable pavement and rainwater pipe expansion was selected as the best cost-effective scenario for flood mitigation. The research methodology, proposed in this study, is expected to be utilized for decision-making in the planning stage for flood mitigation measures for each region.

A Comparative Study on Prediction Performance of the Bankruptcy Prediction Models for General Contractors in Korea Construction Industry

  • Seung-Kyu Yoo;Jae-Kyu Choi;Ju-Hyung Kim;Jae-Jun Kim
    • International conference on construction engineering and project management
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    • 2011.02a
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    • pp.432-438
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    • 2011
  • The purpose of the present thesis is to develop bankruptcy prediction models capable of being applied to the Korean construction industry and to deduce an optimal model through comparative evaluation of final developed models. A study population was selected as general contractors in the Korean construction industry. In order to ease the sample securing and reliability of data, it was limited to general contractors receiving external audit from the government. The study samples are divided into a bankrupt company group and a non-bankrupt company group. The bankruptcy, insolvency, declaration of insolvency, workout and corporate reorganization were used as selection criteria of a bankrupt company. A company that is not included in the selection criteria of the bankrupt company group was selected as a non-bankrupt company. Accordingly, the study sample is composed of a total of 112 samples and is composed of 48 bankrupt companies and 64 non-bankrupt companies. A financial ratio was used as early predictors for development of an estimation model. A total of 90 financial ratios were used and were divided into growth, profitability, productivity and added value. The MDA (Multivariate Discriminant Analysis) model and BLRA (Binary Logistic Regression Analysis) model were used for development of bankruptcy prediction models. The MDA model is an analysis method often used in the past bankruptcy prediction literature, and the BLRA is an analysis method capable of avoiding equal variance assumption. The stepwise (MDA) and forward stepwise method (BLRA) were used for selection of predictor variables in case of model construction. Twenty two variables were finally used in MDA and BLRA models according to timing of bankruptcy. The ROC-Curve Analysis and Classification Analysis were used for analysis of prediction performance of estimation models. The correct classification rate of an individual bankruptcy prediction model is as follows: 1) one year ago before the event of bankruptcy (MDA: 83.04%, BLRA: 93.75%); 2) two years ago before the event of bankruptcy (MDA: 77.68%, BLRA: 78.57%); 3) 3 years ago before the event of bankruptcy (MDA: 84.82%, BLRA: 91.96%). The AUC (Area Under Curve) of an individual bankruptcy prediction model is as follows. : 1) one year ago before the event of bankruptcy (MDA: 0.933, BLRA: 0.978); 2) two years ago before the event of bankruptcy (MDA: 0.852, BLRA: 0.875); 3) 3 years ago before the event of bankruptcy (MDA: 0.938, BLRA: 0.975). As a result of the present research, accuracy of the BLRA model is higher than the MDA model and its prediction performance is improved.

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Identifying Degradation Causes of Endangered Freshwater Fish, Microphysogobio rapidus Using Habitat-Environmental Characteristics (멸종위기 야생생물 I급 여울마자 서식지 환경 특성 파악을 통한 훼손 원인 분석)

  • Ju-Duk Yoon;Keun-Sik Kim;Chang-Deuk Park;Dong-Won Kang;Heung-Heon Lee;Chi-Hong Lim;Nam-Shin Kim
    • Korean Journal of Ecology and Environment
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    • v.56 no.3
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    • pp.229-241
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    • 2023
  • Microphysogobio rapidus is designated as endangered species class I by Ministry of Environment, and its distribution and population have been gradually declining, and it is now limited to the Nam River and some tributary streams of the Nakdong River Watershed. For the restoration of this highly endangered species, it is important to identify the causes of the decline and establish appropriate restoration plans. However, due to lack of basic data and ecological research, most steps are stagnant. Therefore, in this study, we identified the differences in the physical, biological, and sociological habitats between current and past distributed sites through field surveys and literature reviews. As a result of the field survey, there were differences in conductivity between the current and past distributed sites, and fish communities were also showed differences. The literature data also showed that the physico-chemical values of the past distributed sites were generally unfavorable, which generated negative consequences on biological factors. In particular, the effects of urbanization were found to be a major factor affecting the habitat of M. rapidus. Habitat stabilization is crucial for the recovery of this endangered species. However, in the past distributed sites, disturbances such as stream development and weir construction have altered streams physico-chemically and result in changes of M. rapidus. Therefore, a comprehensive plan that considers both stream connectivity and water quality is needed to manage and restore the habitat of M. rapidus.

Characteristics of Asbestos Occurrence in the Vicinity of Serpentine Mines in the Western Part of Chungnam: A Study Based on the Hongseong and Bibong Mine Areas (충남 서부 사문석 광산 인근에서의 석면 산출 특성: 홍성 및 비봉광산을 중심으로)

  • Seokhwan Song
    • Korean Journal of Mineralogy and Petrology
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    • v.36 no.4
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    • pp.233-257
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    • 2023
  • Asbestos minerals are found at rocks and soils of the Hongseong and Bibong serpentine mines, western part of Chungnam. The area consists of and metasediment, and Mesozoic igneous intrusives with minor age-known gneiss complexes and Mesozoic sediments. With detailed geological investigations, rock samples for the serpentinite and amphibolite areas are collected at sites containing asbestos. Representative asbestos and rock samples are analysed by PLM, XRD, SEM and EPMA. Serpentinites are found as steeply dipping faults with adjacent gneiss complex to the NNE direction. Repeated alteration, including serpenitization and talcification, is found at the emplacement direction for the serpentinite body. Amphibollites occur as intrusives and stratiforms within the Precambrian gneiss complex. Serpentinite and amphibolite (or amphibole schist) contain amphiboles either as asbestiform or non-asbestiform. Varying amounts of asbestos minerals, including chrysotile, tremolite asbestos and actinolite asbestos, are found within the serpentinites. The asbestos minerals are found near the cracks or fractures and along the bedding plane. They occur as cross fiber, slip fiber and mass fiber types. Varying amounts of amphibole asbestos minerals, such as tremolite and actinolite asbestos, are found within amphibolites and as a mass fiber type. Overall results suggest that rocks of the serpentine mines contain serpentine and amphibole type asbestos minerals originated from the hydrothermal alteration. Considering construction nearby the mines and environmental risks by the asbestos, additional land management plans are required.

Analysis on Design Change for Backfilling Solution of the Disposal Tunnel in the Deep Geological Repository for High-Level Radioactive Waste in Finland (핀란드 고준위방사성폐기물 심층처분시설 처분터널 뒤채움 설계 변경을 위한 연구사례 분석)

  • Heekwon Ku;Sukhoon Kim;Jeong-Hwan Lee
    • Tunnel and Underground Space
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    • v.33 no.6
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    • pp.435-444
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    • 2023
  • In the licensing application for the deep geological disposal system of high-level radioactive waste in Finland, the disposal tunnel backfilling has been changed from the block/pellet (for the construction) to the granular type (for the operation). Accordingly, for establishing the design concept for backfilling, it is necessary to examine applicability to the domestic facility through analyzing problems of the existing method and improvements in the alternative design. In this paper, we first reviewed the principal studies conducted for changing the backfill method in the licensing process of the Finnish facility, and identified the expected problems in applying the block/pellet backfill method. In addition, we derived the evaluation factors to be considered in terms of technical and operational aspects for the backfilling solution, and then conducted a comparative analysis for two types of backfill methods. This analysis confirmed the overall superiority of the design change. It is expected that these results could be utilized as the technical basis for deriving the optimum design plan in development process of the Korean-specific deep disposal facility. However, applicability should be reviewed in advance based on the latest technical data for the detailed evaluation factors that must be considered for selecting the backfilling method.

Deep Learning Approach for Automatic Discontinuity Mapping on 3D Model of Tunnel Face (터널 막장 3차원 지형모델 상에서의 불연속면 자동 매핑을 위한 딥러닝 기법 적용 방안)

  • Chuyen Pham;Hyu-Soung Shin
    • Tunnel and Underground Space
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    • v.33 no.6
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    • pp.508-518
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    • 2023
  • This paper presents a new approach for the automatic mapping of discontinuities in a tunnel face based on its 3D digital model reconstructed by LiDAR scan or photogrammetry techniques. The main idea revolves around the identification of discontinuity areas in the 3D digital model of a tunnel face by segmenting its 2D projected images using a deep-learning semantic segmentation model called U-Net. The proposed deep learning model integrates various features including the projected RGB image, depth map image, and local surface properties-based images i.e., normal vector and curvature images to effectively segment areas of discontinuity in the images. Subsequently, the segmentation results are projected back onto the 3D model using depth maps and projection matrices to obtain an accurate representation of the location and extent of discontinuities within the 3D space. The performance of the segmentation model is evaluated by comparing the segmented results with their corresponding ground truths, which demonstrates the high accuracy of segmentation results with the intersection-over-union metric of approximately 0.8. Despite still being limited in training data, this method exhibits promising potential to address the limitations of conventional approaches, which only rely on normal vectors and unsupervised machine learning algorithms for grouping points in the 3D model into distinct sets of discontinuities.

Mean Teacher Learning Structure Optimization for Semantic Segmentation of Crack Detection (균열 탐지의 의미론적 분할을 위한 Mean Teacher 학습 구조 최적화 )

  • Seungbo Shim
    • Journal of the Korea institute for structural maintenance and inspection
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    • v.27 no.5
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    • pp.113-119
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    • 2023
  • Most infrastructure structures were completed during periods of economic growth. The number of infrastructure structures reaching their lifespan is increasing, and the proportion of old structures is gradually increasing. The functions and performance of these structures at the time of design may deteriorate and may even lead to safety accidents. To prevent this repercussion, accurate inspection and appropriate repair are requisite. To this end, demand is increasing for computer vision and deep learning technology to accurately detect even minute cracks. However, deep learning algorithms require a large number of training data. In particular, label images indicating the location of cracks in the image are required. To secure a large number of those label images, a lot of labor and time are consumed. To reduce these costs as well as increase detection accuracy, this study proposed a learning structure based on mean teacher method. This learning structure was trained on a dataset of 900 labeled image dataset and 3000 unlabeled image dataset. The crack detection network model was evaluated on over 300 labeled image dataset, and the detection accuracy recorded a mean intersection over union of 89.23% and an F1 score of 89.12%. Through this experiment, it was confirmed that detection performance was improved compared to supervised learning. It is expected that this proposed method will be used in the future to reduce the cost required to secure label images.

Difference Factors Analysis of between Quantity Take-off Using BIM Model and Using 2D Drawings in Reinforced Concrete Building Frame (건물 골조수량 산출 시 BIM모델 기반 수량과 2D도면 기반 수량 차이 요인 분석)

  • Kim, Gwang-Hee
    • Journal of the Korea Institute of Building Construction
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    • v.23 no.5
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    • pp.651-662
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    • 2023
  • Recently, research on the use of Building Information Modeling(BIM) for various construction management activities is being actively conducted, and interest in 3D model-based estimation is increasing because it has the advantage of being able to be automatically performed using the attribute information of the 3D model. Therefore, this study aimed that the difference in the quantities is calculated the quantity based on the 2D drawing of a building and is extracted from the 3D model created by the Revit software was compared and tried to find out the cause. The difference in the quantity calculated by the two methods was the largest in the formwork, followed by the smallest in the order of the quantity of rebar and concrete. The reason for this difference is that there is a part where the quantity extraction in the 3D model is not suitable for the quantity calculation standard, and in particular, in the case of formwork, it was difficult to separate only the quantity of the necessary part. In addition, since the quantity of rebar was not separated by member, it was impossible to accurately compare the quantity and identify the cause of the difference. Therefore, it is considered to be the most reasonable to use application software that imports only the numerical information necessary for quantity calculation from the 3D model and applies a separate calculation formula.

Bearing Capacity Evaluation of Hybrid Suction Bucket Foundations on Clay Under Horizontal Loads Using a Centrifuge (원심모형실험을 활용한 점토지반에 설치된 하이브리드 석션 버켓기초의 수평방향 지지력 평가)

  • Kim, Jae-Hyun;Lee, Cheol-Ju;Shin, Hee Jeong;Kim, Seong Hwan;Goo, Jeong Min;Jung, Chung Yeol;Jeon, Young-Jin
    • Journal of the Korean Geotechnical Society
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    • v.39 no.12
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    • pp.61-73
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    • 2023
  • Suction buckets are feasible options for offshore foundations to support subsea structures in deep water, enabling suction-induced installation by pumps. Recently, hybrid suction bucket foundations that combine single or multiple suction buckets with a mat foundation have been considered. The foundations effectively increase the load capacity while reducing construction costs. However, there is still insufficient experimental validation of hybrid suction bucket foundations regarding their bearing capacity. Furthermore, research on the horizontal load capacity under low vertical and moment loads is inadequate. In this study, we investigate the feasibility of using a hybrid suction bucket foundation for subsea installations in clay. We considered two types of hybrid suction bucket foundations: a circular mat with a single suction bucket and a square mat with multiple buckets. Centrifuge tests were performed to understand the hybrid suction bucket foundation characteristics under horizontal loads and their corresponding bearing capacity. Particularly, we verified the effect of the mat foundation and bucket embedment depth on the horizontal bearing mechanism and capacities. Results confirmed that the hybrid suction bucket foundation outperforms the single suction bucket.