• Title/Summary/Keyword: 건설기술정보시스템

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A Study on the Real-time Recognition Methodology for IoT-based Traffic Accidents (IoT 기반 교통사고 실시간 인지방법론 연구)

  • Oh, Sung Hoon;Jeon, Young Jun;Kwon, Young Woo;Jeong, Seok Chan
    • The Journal of Bigdata
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    • v.7 no.1
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    • pp.15-27
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    • 2022
  • In the past five years, the fatality rate of single-vehicle accidents has been 4.7 times higher than that of all accidents, so it is necessary to establish a system that can detect and respond to single-vehicle accidents immediately. The IoT(Internet of Thing)-based real-time traffic accident recognition system proposed in this study is as following. By attaching an IoT sensor which detects the impact and vehicle ingress to the guardrail, when an impact occurs to the guardrail, the image of the accident site is analyzed through artificial intelligence technology and transmitted to a rescue organization to perform quick rescue operations to damage minimization. An IoT sensor module that recognizes vehicles entering the monitoring area and detects the impact of a guardrail and an AI-based object detection module based on vehicle image data learning were implemented. In addition, a monitoring and operation module that imanages sensor information and image data in integrate was also implemented. For the validation of the system, it was confirmed that the target values were all met by measuring the shock detection transmission speed, the object detection accuracy of vehicles and people, and the sensor failure detection accuracy. In the future, we plan to apply it to actual roads to verify the validity using real data and to commercialize it. This system will contribute to improving road safety.

Application of Linear Schedule Chart by Linking Location Information of Construction Project with Horizontal Work Space (수평작업공간을 갖는 건설프로젝트의 위치정보 연동에 의한 선형공정표 적용방안)

  • Han, Seon Ju;Kim, Hyeon Seung;Park, Sang Mi;Kang, Leen Seok
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.38 no.4
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    • pp.601-610
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    • 2018
  • Since the building construction works are repeated vertically in a limited space, there is not a great need for the location information of each activity in the schedule management. On the other hand, civil engineering works such as road and railway projects consist of a large number of earthworks, long bridges, and long tunnels. These types of work should be controlled in a horizontal space according to the linear axis of several tens of kilometers. In other words, since most of the activities are managed in the unit of distance from the start point to the end point, it is possible to improve the efficiency of the schedule management by linking the location information of the activity with the schedule data in the schedule management system. This study presents a methodology for creating a linear schedule chart specific to a project with horizontal work space and compares the convenience with the existing Gantt chart. In addition, the methodology of linking linear schedule chart to the 4D CAD system, which is a typical BIM technology in the construction phase, is presented to improve the usability of BIM. The practical applicability of the proposed methodology was verified statistically.

Assessment Items for the Level of Service for Tunnel Facilities from an Asset Management Perspective (자산관리관점에서 터널시설물의 서비스 수준 평가항목에 관한 연구)

  • Jeong, Seong-Yun;Nah, Hei-Suk;Choi, Won-Sik
    • The Journal of the Korea Contents Association
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    • v.12 no.1
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    • pp.68-79
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    • 2012
  • Korea was followed by a rapid increase in road construction investment in late 1980s with the help of strong economic conditions. As a result of such investment, it will be expected that there must be a rapid increase in demand for maintenance or rehabilitation of the facilities after 2010. We are developing asset management information systems for enhancing user's satisfaction and innovating the facility management techniques within a limited budget. We suggested the assessment items for the Level of Service for tunnel facility's operation and maintenance(O&M), in this study. We have surveyed for the importance of assessment items by considering both user's viewpoints and facility management agency's viewpoints. We came to realize the relative importance of the items based on this survey using the Analytic Hierarchy Process(AHP) method proposed by Satty. The relative importance of assessment items in terms of the user's viewpoint was identified in descending order such as safety, mobility, accessibility, convenience, O&M efficiency, comfort, rural revitalization, environment, and information. Also, the importance of the items in terms of the management agency's viewpoint was identified in descending order such as safety, mobility, accessibility, environment, rural revitalization, convenience, O&M efficiency, information, and comfort. It means that safety of tunnel facilities is more important than mobility, although which is the basic function of the road facility, both in agency's and user's concern. Therefore, the agencies have to pay more attentions and efforts to ensure safety of tunnel facilities when they establish the investment plans for the tunnel facility O&M.

The Relationship between Internet Search Volumes and Stock Price Changes: An Empirical Study on KOSDAQ Market (개별 기업에 대한 인터넷 검색량과 주가변동성의 관계: 국내 코스닥시장에서의 산업별 실증분석)

  • Jeon, Saemi;Chung, Yeojin;Lee, Dongyoup
    • Journal of Intelligence and Information Systems
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    • v.22 no.2
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    • pp.81-96
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    • 2016
  • As the internet has become widespread and easy to access everywhere, it is common for people to search information via online search engines such as Google and Naver in everyday life. Recent studies have used online search volume of specific keyword as a measure of the internet users' attention in order to predict disease outbreaks such as flu and cancer, an unemployment rate, and an index of a nation's economic condition, and etc. For stock traders, web search is also one of major information resources to obtain data about individual stock items. Therefore, search volume of a stock item can reflect the amount of investors' attention on it. The investor attention has been regarded as a crucial factor influencing on stock price but it has been measured by indirect proxies such as market capitalization, trading volume, advertising expense, and etc. It has been theoretically and empirically proved that an increase of investors' attention on a stock item brings temporary increase of the stock price and the price recovers in the long run. Recent development of internet environment enables to measure the investor attention directly by the internet search volume of individual stock item, which has been used to show the attention-induced price pressure. Previous studies focus mainly on Dow Jones and NASDAQ market in the United States. In this paper, we investigate the relationship between the individual investors' attention measured by the internet search volumes and stock price changes of individual stock items in the KOSDAQ market in Korea, where the proportion of the trades by individual investors are about 90% of the total. In addition, we examine the difference between industries in the influence of investors' attention on stock return. The internet search volume of stocks were gathered from "Naver Trend" service weekly between January 2007 and June 2015. The regression model with the error term with AR(1) covariance structure is used to analyze the data since the weekly prices in a stock item are systematically correlated. The market capitalization, trading volume, the increment of trading volume, and the month in which each trade occurs are included in the model as control variables. The fitted model shows that an abnormal increase of search volume of a stock item has a positive influence on the stock return and the amount of the influence varies among the industry. The stock items in IT software, construction, and distribution industries have shown to be more influenced by the abnormally large internet search volume than the average across the industries. On the other hand, the stock items in IT hardware, manufacturing, entertainment, finance, and communication industries are less influenced by the abnormal search volume than the average. In order to verify price pressure caused by investors' attention in KOSDAQ, the stock return of the current week is modelled using the abnormal search volume observed one to four weeks ahead. On average, the abnormally large increment of the search volume increased the stock return of the current week and one week later, and it decreased the stock return in two and three weeks later. There is no significant relationship with the stock return after 4 weeks. This relationship differs among the industries. An abnormal search volume brings particularly severe price reversal on the stocks in the IT software industry, which are often to be targets of irrational investments by individual investors. An abnormal search volume caused less severe price reversal on the stocks in the manufacturing and IT hardware industries than on average across the industries. The price reversal was not observed in the communication, finance, entertainment, and transportation industries, which are known to be influenced largely by macro-economic factors such as oil price and currency exchange rate. The result of this study can be utilized to construct an intelligent trading system based on the big data gathered from web search engines, social network services, and internet communities. Particularly, the difference of price reversal effect between industries may provide useful information to make a portfolio and build an investment strategy.

Crack detection in concrete using deep learning for underground facility safety inspection (지하시설물 안전점검을 위한 딥러닝 기반 콘크리트 균열 검출)

  • Eui-Ik Jeon;Impyeong Lee;Donggyou Kim
    • Journal of Korean Tunnelling and Underground Space Association
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    • v.25 no.6
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    • pp.555-567
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    • 2023
  • The cracks in the tunnel are currently determined through visual inspections conducted by inspectors based on images acquired using tunnel imaging acquisition systems. This labor-intensive approach, relying on inspectors, has inherent limitations as it is subject to their subjective judgments. Recently research efforts have actively explored the use of deep learning to automatically detect tunnel cracks. However, most studies utilize public datasets or lack sufficient objectivity in the analysis process, making it challenging to apply them effectively in practical operations. In this study, we selected test datasets consisting of images in the same format as those obtained from the actual inspection system to perform an objective evaluation of deep learning models. Additionally, we introduced ensemble techniques to complement the strengths and weaknesses of the deep learning models, thereby improving the accuracy of crack detection. As a result, we achieved high recall rates of 80%, 88%, and 89% for cracks with sizes of 0.2 mm, 0.3 mm, and 0.5 mm, respectively, in the test images. In addition, the crack detection result of deep learning included numerous cracks that the inspector could not find. if cracks are detected with sufficient accuracy in a more objective evaluation by selecting images from other tunnels that were not used in this study, it is judged that deep learning will be able to be introduced to facility safety inspection.

A Study on the Application of BIPV for the Spread of Zero Energy Building (제로에너지 건축물 확산을 위한 건물 일체형 태양광 적용방안 연구)

  • Park, Seung-Joon;Jeon, Hyun-Woo;Lee, Seung-Joon;Oh, Choong-Hyun
    • Journal of Digital Convergence
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    • v.19 no.9
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    • pp.189-199
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    • 2021
  • In order to increase the self-reliance rate of new and renewable energy in order to respond to the mandatory domestic zero-energy buildings, the taller the building, the more limited the site area, and installing PV modules on the roof is not enough. Therefore, BIPV (Building integrated photovoltaic, hereinafter BIPV) is the industry receiving the most attention as a core energy source that can realize zero-energy buildings. Therefore, this study conducted a survey on the problems of the BIPV industry in a self-discussing method for experts with more than 10 years of experience of designers, builders, product manufacturers, and maintainers in order to suggest the right direction and revitalize the BIPV industry. Industrial problems of BIPV adjustment are drawn extention range of standard and certification for products, range improvement for current small condition of various kind productions, need to revise standards for capable of accomodating roof-type, color-module and louver-module, necessary of barrier in flow of foreign modules into korea through domestic certification mandatory, difficulty in obtaining BIPV information, request to prevent confusion among participants by exact guidelime about architectural application part of BIPV, and lack of the BIPV definition clearness, support policy, etc. Based on the improvements needed for the elements, giving change and competitiveness impacts aims to present and propose counter measures and direction.

Development of Plant BIM Library according to Object Geometry and Attribute Information Guidelines (객체 형상 및 속성정보 지침에 따른 수목 BIM 라이브러리 개발)

  • Kim, Bok-Young
    • Journal of the Korean Institute of Landscape Architecture
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    • v.52 no.2
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    • pp.51-63
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    • 2024
  • While the government policy to fully adopt BIM in the construction sector is being implemented, the construction and utilization of landscape BIM models are facing challenges due to problems such as limitations in BIM authoring tools, difficulties in modeling natural materials, and a shortage in BIM content including libraries. In particular, plants, fundamental design elements in the field of landscape architecture, must be included in BIM models, yet they are often omitted during the modeling process, or necessary information is not included, which further compromises the quality of the BIM data. This study aimed to contribute to the construction and utilization of landscape BIM models by developing a plant library that complies with BIM standards and is applicable to the landscape industry. The plant library of trees and shrubs was developed in Revit by modeling 3D shapes and collecting attribute items. The geometric information is simplified to express the unique characteristics of each plant species at LOD200, LOD300, and LOD350 levels. The attribute information includes properties on plant species identification, such as species name, specifications, and quantity estimation, as well as ecological attributes and environmental performance information, totaling 24 items. The names of the files were given so that the hierarchy of an object in the landscape field could be revealed and the object name could classify the plant itself. Its usability was examined by building a landscape BIM model of an apartment complex. The result showed that the plant library facilitated the construction process of the landscape BIM model. It was also confirmed that the library was properly operated in the basic utilization of the BIM model, such as 2D documentation, quantity takeoff, and design review. However, the library lacked ground cover, and had limitations in those variables such as the environmental performance of plants because various databases for some materials have not yet been established. Further efforts are needed to develop BIM modeling tools, techniques, and various databases for natural materials. Moreover, entities and systems responsible for creating, managing, distributing, and disseminating BIM libraries must be established.

A Study on the Use of GIS-based Time Series Spatial Data for Streamflow Depletion Assessment (하천 건천화 평가를 위한 GIS 기반의 시계열 공간자료 활용에 관한 연구)

  • YOO, Jae-Hyun;KIM, Kye-Hyun;PARK, Yong-Gil;LEE, Gi-Hun;KIM, Seong-Joon;JUNG, Chung-Gil
    • Journal of the Korean Association of Geographic Information Studies
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    • v.21 no.4
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    • pp.50-63
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    • 2018
  • The rapid urbanization had led to a distortion of natural hydrological cycle system. The change in hydrological cycle structure is causing streamflow depletion, changing the existing use tendency of water resources. To manage such phenomena, a streamflow depletion impact assessment technology to forecast depletion is required. For performing such technology, it is indispensable to build GIS-based spatial data as fundamental data, but there is a shortage of related research. Therefore, this study was conducted to use the use of GIS-based time series spatial data for streamflow depletion assessment. For this study, GIS data over decades of changes on a national scale were constructed, targeting 6 streamflow depletion impact factors (weather, soil depth, forest density, road network, groundwater usage and landuse) and the data were used as the basic data for the operation of continuous hydrologic model. Focusing on these impact factors, the causes for streamflow depletion were analyzed depending on time series. Then, using distributed continuous hydrologic model based DrySAT, annual runoff of each streamflow depletion impact factor was measured and depletion assessment was conducted. As a result, the default value of annual runoff was measured at 977.9mm under the given weather condition without considering other factors. When considering the decrease in soil depth, the increase in forest density, road development, and groundwater usage, along with the change in land use and development, and annual runoff were measured at 1,003.5mm, 942.1mm, 961.9mm, 915.5mm, and 1003.7mm, respectively. The results showed that the major causes of the streaflow depletion were lowered soil depth to decrease the infiltration volume and surface runoff thereby decreasing streamflow; the increased forest density to decrease surface runoff; the increased road network to decrease the sub-surface flow; the increased groundwater use from undiscriminated development to decrease the baseflow; increased impervious areas to increase surface runoff. Also, each standard watershed depending on the grade of depletion was indicated, based on the definition of streamflow depletion and the range of grade. Considering the weather, the decrease in soil depth, the increase in forest density, road development, and groundwater usage, and the change in land use and development, the grade of depletion were 2.1, 2.2, 2.5, 2.3, 2.8, 2.2, respectively. Among the five streamflow depletion impact factors except rainfall condition, the change in groundwater usage showed the biggest influence on depletion, followed by the change in forest density, road construction, land use, and soil depth. In conclusion, it is anticipated that a national streamflow depletion assessment system to be develop in the future would provide customized depletion management and prevention plans based on the system assessment results regarding future data changes of the six streamflow depletion impact factors and the prospect of depletion progress.

Analysis of Co-registration Performance According to Geometric Processing Level of KOMPSAT-3/3A Reference Image (KOMPSAT-3/3A 기준영상의 기하품질에 따른 상호좌표등록 결과 분석)

  • Yun, Yerin;Kim, Taeheon;Oh, Jaehong;Han, Youkyung
    • Korean Journal of Remote Sensing
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    • v.37 no.2
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    • pp.221-232
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    • 2021
  • This study analyzed co-registration results according to the geometric processing level of reference image, which are Level 1R and Level 1G provided from KOMPSAT-3 and KOMPSAT-3A images. We performed co-registration using each Level 1R and Level 1G image as a reference image, and Level 1R image as a sensed image. For constructing the experimental dataset, seven Level 1R and 1G images of KOMPSAT-3 and KOMPSAT-3A acquired from Daejeon, South Korea, were used. To coarsely align the geometric position of the two images, SURF (Speeded-Up Robust Feature) and PC (Phase Correlation) methods were combined and then repeatedly applied to the overlapping region of the images. Then, we extracted tie-points using the SURF method from coarsely aligned images and performed fine co-registration through affine transformation and piecewise Linear transformation, respectively, constructed with the tie-points. As a result of the experiment, when Level 1G image was used as a reference image, a relatively large number of tie-points were extracted than Level 1R image. Also, in the case where the reference image is Level 1G image, the root mean square error of co-registration was 5 pixels less than the case of Level 1R image on average. We have shown from the experimental results that the co-registration performance can be affected by the geometric processing level related to the initial geometric relationship between the two images. Moreover, we confirmed that the better geometric quality of the reference image achieved the more stable co-registration performance.

Comparison of performance of automatic detection model of GPR signal considering the heterogeneous ground (지반의 불균질성을 고려한 GPR 신호의 자동탐지모델 성능 비교)

  • Lee, Sang Yun;Song, Ki-Il;Kang, Kyung Nam;Ryu, Hee Hwan
    • Journal of Korean Tunnelling and Underground Space Association
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    • v.24 no.4
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    • pp.341-353
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    • 2022
  • Pipelines are buried in urban area, and the position (depth and orientation) of buried pipeline should be clearly identified before ground excavation. Although various geophysical methods can be used to detect the buried pipeline, it is not easy to identify the exact information of pipeline due to heterogeneous ground condition. Among various non-destructive geo-exploration methods, ground penetration radar (GPR) can explore the ground subsurface rapidly with relatively low cost compared to other exploration methods. However, the exploration data obtained from GPR requires considerable experiences because interpretation is not intuitive. Recently, researches on automated detection technology for GPR data using deep learning have been conducted. However, the lack of GPR data which is essential for training makes it difficult to build up the reliable detection model. To overcome this problem, we conducted a preliminary study to improve the performance of the detection model using finite difference time domain (FDTD)-based numerical analysis. Firstly, numerical analysis was performed with homogeneous soil media having single permittivity. In case of heterogeneous ground, numerical analysis was performed considering the ground heterogeneity using fractal technique. Secondly, deep learning was carried out using convolutional neural network. Detection Model-A is trained with data set obtained from homogeneous ground. And, detection Model-B is trained with data set obtained from homogeneous ground and heterogeneous ground. As a result, it is found that the detection Model-B which is trained including heterogeneous ground shows better performance than detection Model-A. It indicates the ground heterogeneity should be considered to increase the performance of automated detection model for GPR exploration.