• Title/Summary/Keyword: 노면 균열

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The Extacting Crack in Asphalt Concrete Pavement by Digital Image Processing (수치영상처리에 의한 아스팔트 포장노면의 균열 검출)

  • Jang, Ji-Won
    • Journal of Korean Society for Geospatial Information Science
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    • v.10 no.4 s.22
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    • pp.77-84
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    • 2002
  • Recently, damage of pavement represented by crack is depened by the increase of traffic demand up to ten million and wight, and interest about the efficient management of pavement is being increased gradually according to the growth of maintenance expense of road surface. In this study, the possibility of application for acquisition of crack information was tested by appling DCRP and digital image processing technique and measuring crack on road surface precisely. Based on this, objective and efficient road surface measurement was planned and done. Measuring crack width, acquire result of comparative high accuracy. So, it is considered that it can be utilized as plan draft data for deterioration estimation and repair reinforcement work of pavement.

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Road Crack Detection based on Object Detection Algorithm using Unmanned Aerial Vehicle Image (드론영상을 이용한 물체탐지알고리즘 기반 도로균열탐지)

  • Kim, Jeong Min;Hyeon, Se Gwon;Chae, Jung Hwan;Do, Myung Sik
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.18 no.6
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    • pp.155-163
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    • 2019
  • This paper proposes a new methodology to recognize cracks on asphalt road surfaces using the image data obtained with drones. The target section was Yuseong-daero, the main highway of Daejeon. Furthermore, two object detection algorithms, such as Tiny-YOLO-V2 and Faster-RCNN, were used to recognize cracks on road surfaces, classify the crack types, and compare the experimental results. As a result, mean average precision of Faster-RCNN and Tiny-YOLO-V2 was 71% and 33%, respectively. The Faster-RCNN algorithm, 2Stage Detection, showed better performance in identifying and separating road surface cracks than the Yolo algorithm, 1Stage Detection. In the future, it will be possible to prepare a plan for building an infrastructure asset-management system using drones and AI crack detection systems. An efficient and economical road-maintenance decision-support system will be established and an operating environment will be produced.

Extraction of Information on Road Surface Using Digital Video Camera (디지털 비디오카메라를 이용한 도로노면정보 추출)

  • Jang Ho Sik
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.23 no.1
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    • pp.9-17
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    • 2005
  • The objective of the study is to extract information about the road surfaces to be studied by analyzing asphalt concrete-paved road surface images photographed with a digital video camera. To analyze the accuracy of road surface information gained using a digital imagery processing method, it was compared and analyzed with the outcomes of control surveying. As a result, an average error of 0.0427 m in the X-axis direction, that of 0.0527 m in the Y-axis direction, and that of 0.1539 m in the Z-axis direction were found, good enough for mapping at a scale of 1:1,000 or less and GIS data. Besides, information on road surface assessment factors such as crack ratio, the amount of rutting and profile index was gained by analyzing processed digital imagery. This information made it possible to conduct road surface assessment by generating PSI and MCI. As quality digital image information has been gathered from roads and stored, important fundamental data on PMS (Pavement Management System) will become available in the future.

Open-Source Based Smartphone Image Analysis for Pavement Crack Detection (노면 균열 검출을 위한 오픈소스 기반 스마트폰 영상해석)

  • Kim Tae-Hyun;Lee Yong-Chang
    • Journal of Urban Science
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    • v.13 no.1
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    • pp.43-52
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    • 2024
  • This study evaluates the feasibility and accuracy of using smartphones for road crack detection through open-source and commercial image analysis tools. High-resolution images were captured with Galaxy and iPhone smartphones, and their accuracy was enhanced using ground control points (GCPs) determined by Network RTK surveying. The study utilized Reality Capture and Pix4DMatic for image analysis, comparing their results with actual measurements. Pix4DMatic effectively converted smartphone images into precise 3D models, detecting even small cracks with minimal error. The findings indicate that smartphones offer a cost-effective and efficient solution for road maintenance, providing high precision and convenience without the need for frequent site visits. Future research should validate this method under various conditions and enhance data collection and analysis automation.

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Development of Road Surface Management System using Digital Imagery (수치영상을 이용한 도로 노면관리시스템 개발)

  • Seo, Dong-Ju
    • Journal of the Korean Association of Geographic Information Studies
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    • v.10 no.1
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    • pp.35-46
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    • 2007
  • In the study digital imagery was used to examine asphalt concrete pavements. With digitally mastered-image information that was filmed with a video camera fixed on a car travelling on road at a consistent speed, a road surface management system that can gain road surface information (Crack, Rutting, IRI) was developed using an object-oriented language "Delphi". This system was designed to improve visualized effects by animations and graphs. After analyzing the accuracy of 3-D coordinates of road surfaces that were decided using multiple image orientation and bundle adjustment method, the average of standard errors turned out to be 0.0427m in the X direction, 0.0527m in the Y direction and 0.1539m in the Z direction. As a result, it was found to be good enough to be put to practical use for maps drawn on scales below 1/1000, which are currently producted and used in our country, and GIS data. According to the analysis of the accuracy in crack width on 12 spots using a digital video camera, the standard error was found to be ${\pm}0.256mm$, which is considered as high precision. In order to get information on rutting, the physically measured cross sections of 4 spots were compared with cross sections generated from digital images. Even though a maximum error turned out to be 10.88mm, its practicality is found in work efficiency.

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Performance Evaluation of Asphalt Concrete Pavements at Korea Expressway Corporation Test Road (시험도로 아스팔트 포장의 공용성 변화 분석)

  • Seo, Youngguk;Kwon, Soon-Min
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.28 no.1D
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    • pp.35-43
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    • 2008
  • This paper mainly deals with the performance evaluation of 33 asphalt sections of Korea Expressway Corporation Test Road (KECTR) during the past four years. Since the construction of the KECTR in December 2002, key performance indicators of asphalt pavements have been collected five times with an Automatic Road Analyzer (ARAN), and have been analyzed for permanent deformation, surface distress, and road roughness. Linear viscoelastic characteristics of four dense graded HMAs used in KECTR were investigated with a series of complex modulus test. The effect of air void in HMAs on dynamic modulus was investigate at two air void contents for a surface course HMA (19 mm Nominal Maximum Size of Aggregate). Layer densification due to traffic was estimated from air void contents of field cored samples, and was correlated with pavement distresses and performances. One of findings of this study was that both permanent deformation and cracking were suspectible to pavement temperatures, rather than traffic. However, it was found that road roughness was mostly affected by traffic loading.

Asphalt Concrete Pavement Surface Crack Detection using Convolutional Neural Network (합성곱 신경망을 이용한 아스팔트 콘크리트 도로포장 표면균열 검출)

  • Choi, Yoon-Soo;Kim, Jong-Ho;Cho, Hyun-Chul;Lee, Chang-Joon
    • Journal of the Korea institute for structural maintenance and inspection
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    • v.23 no.6
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    • pp.38-44
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    • 2019
  • A Convolution Neural Network(CNN) model was utilized to detect surface cracks in asphalt concrete pavements. The CNN used for this study consists of five layers with 3×3 convolution filter and 2×2 pooling kernel. Pavement surface crack images collected by automated road surveying equipment was used for the training and testing of the CNN. The performance of the CNN was evaluated using the accuracy, precision, recall, missing rate, and over rate of the surface crack detection. The CNN trained with the largest amount of data shows more than 96.6% of the accuracy, precision, and recall as well as less than 3.4% of the missing rate and the over rate.

알칼리 골재반응성 평가시험 방법의 이모저모

  • Lee, Jong-Yeol
    • Cement
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    • s.190
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    • pp.32-38
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    • 2011
  • 콘크리트에서 알칼리골재반응은 내구성에 악 영향을 주는 일종의 암이라고 표현할 수 있다. 잠복기간이 길고, 균열이 나타나는 시기도 매우 오래 걸리기 때문이다. 이러한 현상이 1940 년대 알려지면서, 미국 ASTM에는 1950년에 모르타르봉 시험방법이, 1952년에 화학법이 각각 시험방법 규격으로 제정되었다. 국내에서는 한국도로교통연구원을 비롯한 전문연구기관 등에서 화학법 및 모르타르봉 방법으로 연구한 결과, 화학법에서는 일부 골재가 반응성이 있는 것으로 보고 되었으나, 모르타르봉 방법에서는 대상 골재에서 유해가능성이 낮은 것으로 보고되었다. 또한, 그동안은 구조물에서 알칼리골재반응에 의한 피해사례도 보고되지 않았고, 골재의 품질도 양호한 것으로 알려져 왔다. 그러나, 최근들어 서해안 고속도로 일부 구간에서 알칼리골재반응에 의한 포장노면에 균열 및 스폴링 등 심각한 피해사례가 보고되면서 국내에서도 관심이 높아지기 시작하였다. 특히 일본에서는 제 63회 시멘트기술대회 (2009년 5월 22일)에서 팽창기구의 재검토에 대한 이야기가 패널토의에서 제기되었고, 일부 시험방법의 이야기도 나왔다. 그동안의 골재는 현재의 규격만으로도 설명이 가능했는데, 최근의 골재들은 설명이 잘 안 되는 경우가 종종 있다는 이야기다. 이런 이야기들은 일본 지인들과 기술교류를 하면서 많은 이야기를 나누었고, 또한 우연히 문헌들을 독해하던 중 이런 이야기들을 경험한 문헌인 일본 태평양시벤트에서 발간되는 CEM'S 자료를 찾았기에 발췌 정리한 것이다.

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