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2023 Vol.22, Issue 1 Preview Page

Research Article

Two-Phase Approach for Data Quality Management for Slope Stability Monitoring

경사면의 안정성 모니터링 데이터의 품질관리를 위한 2 단계 접근방안

Junhyuk Choi1
,
Yongjin Kim2
,
Junhwi Cho3
,
Woocheol Jeong3
,
Songhee Suk3
,
Song Choi3
,
Yongseong Kim4
,
Bongjun Ji5*
최 준혁1
,
김 용진2
,
조 준휘3
,
정 우철3
,
석 송희3
,
최 송3
,
김 용성4
,
지 봉준5*
1Member, Graduate Student, Department of Industrial Management Engineering, Pohang University of Science and Technology, 77 Cheongam-Ro, Pohang, Gyeongsangbuk-do 37673, Republic of Korea
2Non-Member, CEO, Smart Geotech, 1 Kangwondaehak-gil, Chuncheon-si, Gangwon-do 24341, Republic of Korea
3Non-Member, Graduate Student, Department of Regional Infrastructure Engineering, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon-si, Gangwon-do 24341, Republic of Korea
4Member, Professor, Department of Regional Infrastructure Engineering, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon-si, Gangwon-do 24341, Republic of Korea
5Member, Assistant Professor, Department of Regional Infrastructure Engineering, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon-si, Gangwon-do 24341, Republic of Korea
*Corresponding Author
30 March 2023. pp. 67-74
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Abstract

In order to monitor the stability of slopes, research on data-based slope failure prediction and early warning is increasing. However, most papers overlook the quality of data. Poor data quality can cause problems such as false alarms. Therefore, this paper proposes a two-step hybrid approach consisting of rules and machine learning models for quality control of data collected from slopes. The rule-based has the advantage of high accuracy and intuitive interpretation, and the machine learning model has the advantage of being able to derive patterns that cannot be explicitly expressed. The hybrid approach was able to take both of these advantages. Through a case study, the performance of using the two methods alone and the case of using the hybrid approach was compared, and the hybrid method was judged to have high performance. Therefore, it is judged that using a hybrid method is more appropriate than using the two methods alone for data quality control.

Keywords
Slope failure
Data quality
Machine learning
Anomaly detection
Rule-based method

경사면의 안정성을 모니터링 하기 위해 데이터 기반으로 사면의 붕괴를 예측, 경보를 하려는 연구가 증가하고 있다. 하지만 대부분의 논문에서는 데이터의 품질에 대해 간과하고 있다. 이는 오경보와 같은 문제를 발생시킬 수 있다. 이에 본 논문에서는 사면에서 수집된 데이터의 품질관리를 위한 규칙과 기계학습 모델로 구성된 2 단계의 접근 방안을 제안하였다. 규칙 기반은 높은 정확도와 직관적인 해석이 가능하다는 장점이 있으며 기계학습 모델은 명시적으로 표현할 수 없는 패턴을 도출할 수 있다는 장점이 있으며 2단계의 접근 방안은 이 두 장점을 모두 취할 수 있었다. 사례연구를 통해 두 방법을 단독으로 사용하였을 경우와 2단계의 접근 방안을 사용하였을 때의 성능을 비교하였고 2단계 접근 방안이 높은 성능을 보이는 것으로 판단되었다. 따라서 데이터의 품질관리를 위해 단독으로 두 방법을 사용하는 것보다 2단계 접근 방안 방법을 사용하는 것이 적절할 것으로 판단된다.

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Information
  • Publisher :Korean Geosythetics Society
  • Publisher(Ko) :한국지반신소재학회
  • Journal Title :Journal of the Korean Geosynthetics Society
  • Journal Title(Ko) :한국지반신소재학회 논문집
  • Volume : 22
  • No :1
  • Pages :67-74
  • Received Date : 2023-02-23
  • Revised Date : 2023-03-16
  • Accepted Date : 2023-03-24
  • DOI :https://doi.org/10.12814/jkgss.2023.22.1.067
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