Journal of The Korean Society of Agricultural Engineers (한국농공학회논문집)

The Korean Society of Agricultural Engineers (한국농공학회)
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Development of Snow Load Sensor and Analysis of Warning Criterion for Heavy Snow Disaster Prevention Alarm System in Plastic Greenhouse

비닐온실 폭설 방재 예·경보 시스템을 위한 설하중 센서 개발과 적설 경보 기준 분석

  • Kim, Dongsu (Department of Rural Systems Engineering, Seoul National University) ;
  • Jeong, Youngjoon (Department of Rural Systems Engineering, Global Smart Farm Convergence Major, Seoul National University) ;
  • Lee, Sang-ik (Department of Rural Systems Engineering, Global Smart Farm Convergence Major, Seoul National University) ;
  • Lee, Jonghyuk (Department of Rural Systems Engineering, Global Smart Farm Convergence Major, Seoul National University) ;
  • Hwang, Kyuhong (STA Corporation Ltd) ;
  • Choi, Won (Department of Rural Systems Engineering, Research Institute of Agriculture and Life Sciences, Global Smart Farm Convergence Major, Seoul National University)
  • Received : 2021.01.24
  • Accepted : 2021.03.08
  • Published : 2021.03.31
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Abstract

As the weather changes become frequent, weather disasters are increasing, causing more damage to plastic greenhouses. Among the damage caused by various disasters, damage by snow to the greenhouse takes a relatively long time, so if an alarm system is properly prepared, the damage can be reduced. Existing greenhouse design standards and snow warning systems are based on snow depth. However, even in the same depth, the load on the greenhouse varies depending on meteorological characteristics and snow density. Therefore, this study aims to secure the structural safety of greenhouses by developing sensors that can directly measure snow loads, and analysing the warning criteria for load using a stochastic model. Markov chain was applied to estimate the failure probability of various types of greenhouses in various regions, which let users actively cope with heavy snowfall by selecting an appropriate time to respond. Although it was hard to predict the precise snow depth or amounts, it could successfully assess the risk of structures by directly detecting the snow load using the developed sensor.

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References

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