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A Study on Intelligent Damage Detection System Considering Seasonal Failure Probability

  • Journal of Knowledge Information Technology and Systems
  • Abbr : JKITS
  • 2020, 15(5), pp.795-805
  • DOI : 10.34163/jkits.2020.15.5.022
  • Publisher : Korea Knowledge Information Technology Society
  • Research Area : Interdisciplinary Studies > Interdisciplinary Research
  • Received : September 8, 2020
  • Accepted : October 13, 2020
  • Published : October 31, 2020

Byoung-Chan Jeon 1,  Hong,In-Sik 2

1청운대학교
2순천향대학교

Accredited

ABSTRACT

In the underground, facilities such as various types of water supply pipes, sewer pipes, gas pipes, and communication pipes are buried. These underground facilities provide convenience for us to live in the world. The major loss factor in underground facilities is water leakage or pipe breakage. Leakage or damage that occurs among the various types of pipes buried underground is the cause of aging, corrosion, improper work, and subsidence of the pipe. In Korea, the current summer and winter weather is more pronounced than before, so long rainy seasons and typhoons are increasing in summer, and heavy snow and cold waves are occurring in winter. Therefore, although facilities are buried underground, the frequency of occurrence of abnormalities in underground buried materials varies by season. In particular, in summer and winter, when temperature changes are large, the frequency of abnormalities in underground burials increases. In particular, landslides may occur in the basement during certain seasons due to the influence of rainy seasons, typhoons, and heavy snow, or many maintenance problems may occur due to ground subsidence. Despite these problems, existing underground facility management systems are out of date and cannot be prevented in advance, and there is a problem in coping with water leakage or ground subsidence. In addition, there are many problems that the accuracy of monitoring data is also poor. In this paper, we propose an intelligent damage detection system using real-time processing of monitoring data and the probability of seasonal failures to improve the damage detection performance of the existing monitoring system.

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