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AUTOMATIC CRACK WIDTH DETERMINATION IN CONCRETE STRUCTURES

Jakub Rubint

First published: 2019-06-20https://doi.org/10.5593/sgem2019/1.2/s02.011View metrics

Abstract

A significant part of geotechnical structures such as parts of tunnels, adits, retaining walls, etc., is made of reinforced concrete. In reinforced concrete structures there are often cracks in places where they were not considered in the design. It is up to the engineer performing the fault diagnosis to determine the nature of the cracks, the cause of the occurrence and the follow-up measures to prevent their further development or eventual sanitation. The principles of conventional methods of visual diagnosis of such structures are simple, but possible disturbances may occur in inaccessible places. Typically, performing visual diagnostics requires stopping the operation in the surrounding areas of structure under investigation to ensure the safety of the persons performing it. As a result, a device that ensures continuous monitoring of cracks even in hard-to-reach areas or in locations with increased operation has been developed. This device can continuously monitor the evolution of the crack over time, store the evaluated data chronologically or send it to the server, and notify the competent authorities about changes.3

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Publication details

Title
AUTOMATIC CRACK WIDTH DETERMINATION IN CONCRETE STRUCTURES
Authors
Jakub Rubint
Proceedings
SGEM International Multidisciplinary Scientific GeoConference EXPO Proceedings; 19th SGEM International Multidisciplinary Scientific GeoConference EXPO Proceedings19th, Science and Technologies in Geology, Exploration And Mining
Publisher
STEF92 Technology
Year
2019
Pages
79-86
SWS Citekey
Rubint201927986
ISSN
1314-2704
ISBN
978-619-7408-77-5
Language
en
Publication type
Conference Paper
Keywords
References5
  1. DIAGNOSTIC METHODS FOR DETECTING BRIDGE FAULTS, Technical regulation, Slovakia, 2012, pp 3-4;

  2. Flea3 GigE, Technical Reference, Canada, 2013, pp 11-12.

  3. A. Jain, Fundamentals of Digital Image Processing, Prentice-Hall, USA, 1986, pp 408.

  4. C.K. Chow, T. Kaneko, Automatic Boundary Detection of the Left Ventricle from Cineangiograms, Comp. Biomed. Res.(5), USA, 1972, pp. 388-410.

  5. E. Davies, Machine Vision: Theory, Algorithms and Practicalities, Academic Press, USA, 1990, pp 91 - 96.

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Number of times cited according to Crossref: 1

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