• Title/Summary/Keyword: 상수도관 파열

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Data-driven event detection method for efficient management and recovery of water distribution system man-made disasters (상수도관망 재난관리 및 복구를 위한 데이터기반 이상탐지 방법론 개발)

  • Jung, Donghwi;Ahn, Jaehyun
    • Journal of Korea Water Resources Association
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    • v.51 no.8
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    • pp.703-711
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    • 2018
  • Water distribution system (WDS) pipe bursts are caused from excessive pressure, pipe aging, and ground shift from temperature change and earthquake. Prompt detection of and response to the failure event help prevent large-scale service interruption and catastrophic sinkhole generation. To that end, this study proposes a improved Western Electric Company (WECO) method to improve the detection effectiveness and efficiency of the original WECO method. The original WECO method is an univariate Statistical Process Control (SPC) technique used for identifying any non-random patterns in system output data. The improved WECO method multiples a threshold modifier (w) to each threshold of WECO sub-rules in order to control the sensitivity of anomaly detection in a water distribution network of interest. The Austin network was used to demonstrated the proposed method in which normal random and abnormal pipe flow data were generated. The best w value was identified from a sensitivity analysis, and the impact of measurement frequency (dt = 5, 10, 15 min etc.) was also investigated. The proposed method was compared to the original WECO method with respect to detection probability, false alarm rate, and averaged detection time. Finally, this study provides a set of guidelines on the use of the WECO method for real-life WDS pipe burst detection.

Leakage Detection Method in Water Pipe using Tree-based Boosting Algorithm (트리 기반 부스팅 알고리듬을 이용한 상수도관 누수 탐지 방법)

  • Jae-Heung Lee;Yunsung Oh;Junhyeok Min
    • Journal of Internet of Things and Convergence
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    • v.10 no.2
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    • pp.17-23
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    • 2024
  • Losses in domestic water supply due to leaks are very large, such as fractures and defects in pipelines. Therefore, preventive measures to prevent water leakage are necessary. We propose the development of a leakage detection sensor utilizing vibration sensors and present an optimal leakage detection algorithm leveraging artificial intelligence. Vibrational sound data acquired from water pipelines undergo a preprocessing stage using FFT (Fast Fourier Transform), followed by leakage classification using an optimized tree-based boosting algorithm. Applying this method to approximately 260,000 experimental data points from various real-world scenarios resulted in a 97% accuracy, a 4% improvement over existing SVM(Support Vector Machine) methods. The processing speed also increased approximately 80 times, confirming its suitability for edge device applications.

Context-Awareness Scenario for Ubiquitous Environment : Case of Water Supply (유비쿼터스 환경기반의 상황인식 시나리오 연구 - 상수도 사례를 중심으로 -)

  • Jeong, Jin-Seok;Lee, Yong-Joo;Byun, In-Sun;Kim, Tae-Hoon;Song, Yong-Hak
    • Journal of Korean Society for Geospatial Information Science
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    • v.17 no.2
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    • pp.47-53
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    • 2009
  • Recently, context-awareness computing becomes prerequisite to pervasive computing, called ubiquitous paradigm. Especially, it plays much of roll as a part of the ubiquitous infrastructures, one of three ubiquitous city components. This paper proposes the optimized procedures to deal with the various hypothetical water-leaking situations in the water supply systems. These procedures suggested in this paper will be employed to develop the context-awareness modules or models for urban infrastructure maintenance in the future works.

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Analysis of Geological Factors for Risk Assessment in Deep Rock Excavation in South Korea (한국의 대심도 암반 굴착 위험도 산정을 위한 인자 분석)

  • Ihm, Myeong Hyeok;Lee, Hana
    • Tunnel and Underground Space
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    • v.31 no.4
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    • pp.211-220
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    • 2021
  • Tunnel collapse often occurs during deep underground tunneling (> 40 m depth) in South Korea. Natural cavities as well as water supply pipes, sewer pipes, electric power cables, artificial cavities created by subway construction are complexly distributed in the artificial ground in the shallow depths of the urban area. For deep tunnel excavation, it is necessary to understand the properties of the ground which is characterized by porous elements and various geological structures, and their influence on the stability of the ground. This study analyzed geological factors for risk assessment in deep excavation in South Korea based on domestic and overseas case study. As a result, a total of 7 categories and 38 factors were derived. Factors with high weights were fault and fault clay, differential stress, rock type, groundwater and mud inrush, uniaxial compressive strength, cross-sectional area of tunnel, overburden thickness, karst and valley terrain, fold, limestone alternation, fluctuation of groundwater table, tunnel depth, dyke, RQD, joint characteristics, anisotropy, rockburst and so forth.