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Failure Prediction for Large-scale Water Pipe Networks Using GNN and Temporal Failure Series

Published: 30 October 2021 Publication History

Abstract

Pipe failure prediction in the water industry aims to prioritize the pipes that are at high risk of failure for proactive maintenance. However, existing statistical or machine learning models that rely on historical failures and asset attributes can hardly leverage the structure information of pipe networks. In this work, we develop a failure prediction framework for pipe networks by jointly considering the pipes' features, the network structure, the geographical neighboring effect, and the temporal failure series. We apply a multi-hop Graph Neural Network (GNN) to failure prediction. We propose a method of constructing a geographical graph structure depending on not only the physical connections but also geographical distances between pipes. To differentiate the pipes with diverse properties, we employ an attention mechanism in the neighborhood aggregation process of each GNN layer. Also, residual connections and layer-wise aggregation are used to avoid the over-smoothing issue in deep GNNs. The historical failures exhibit a strong temporal pattern. Inspired by point process, we develop a module to learn the pipes' evolutionary effect and the time-decayed excitement of historical failures on the current state of the pipe. The proposed framework is evaluated on two real-world large-scale pipe networks. It outperforms the existing statistical, machine learning, and state-of-the-art GNN baselines. Our framework provides the water utility with core data-driven support for proactive maintenance including regular pipe inspection, pipe renewal planning, and sensor system deployment. It can be extended to other infrastructure networks in the future.

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Cited By

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  • (2025)A survey on massive IoT for water distribution systems: Challenges, simulation tools, and guidelines for large-scale deploymentAd Hoc Networks10.1016/j.adhoc.2024.103714168(103714)Online publication date: Mar-2025
  • (2024)Pipe Failure Prediction in the Water Distribution System Using a Deep Graph Convolutional Network and Temporal Failure SeriesACS ES&T Engineering10.1021/acsestengg.4c002344:9(2252-2262)Online publication date: 27-Aug-2024
  • (2024)Graph Neural Networks for building and civil infrastructure operation and maintenance enhancementAdvanced Engineering Informatics10.1016/j.aei.2024.10286862(102868)Online publication date: Oct-2024
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      cover image ACM Conferences
      CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management
      October 2021
      4966 pages
      ISBN:9781450384469
      DOI:10.1145/3459637
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Published: 30 October 2021

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      Author Tags

      1. failure prediction
      2. gnns
      3. infrastructure networks
      4. point process
      5. proactive maintenance
      6. temporal failure pattern

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      View all
      • (2025)A survey on massive IoT for water distribution systems: Challenges, simulation tools, and guidelines for large-scale deploymentAd Hoc Networks10.1016/j.adhoc.2024.103714168(103714)Online publication date: Mar-2025
      • (2024)Pipe Failure Prediction in the Water Distribution System Using a Deep Graph Convolutional Network and Temporal Failure SeriesACS ES&T Engineering10.1021/acsestengg.4c002344:9(2252-2262)Online publication date: 27-Aug-2024
      • (2024)Graph Neural Networks for building and civil infrastructure operation and maintenance enhancementAdvanced Engineering Informatics10.1016/j.aei.2024.10286862(102868)Online publication date: Oct-2024
      • (2024)A novel hybrid fuzzy analytical hierarchy process–game theory model for prioritizing factors affecting the deterioration of water pipelinesApplied Water Science10.1007/s13201-024-02274-414:12Online publication date: 26-Nov-2024
      • (2023)Machine Learning and Computer Vision Applications in Civil Infrastructure Inspection and MonitoringInfrastructure Robotics10.1002/9781394162871.ch4(59-80)Online publication date: 15-Dec-2023

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