Hybrid knowledge and data driven approach for prioritizing sewer sediment cleaning

Chen Li, Ke Chen, Zhikang Bao, S. Thomas Ng

Research output: Contribution to journalArticlepeer-review


The efficient assessment of sewer sediment condition is important for municipalities to formulate prioritization strategies for cleaning initiatives. However, manual assessment methods are plagued by inherent subjective and inaccuracy. To address these deficiencies, this paper introduces a hybrid approach integrating both knowledge-based principles and data-driven techniques for Sewer Sediment Cleaning Priority Assessment (SSCPA). The proposed approach exhibits a notable level of assessment accuracy, achieving macro-average precision, recall, and F1-score metrics of 87.9%, 88.0%, and 88.0%, respectively. These findings underscore the efficacy of SSCPA as a valuable tool for evaluating sewer sediment conditions, thereby enhancing the decision-making process for cleaning prioritization efforts. Future research should incorporate the probability of failure as a pivotal factor and explore the temporal dynamics of sewer sediment for more comprehensive insight.
Original languageEnglish
Article number105577
JournalAutomation in Construction
Early online date25 Jun 2024
Publication statusE-pub ahead of print - 25 Jun 2024


  • Copula
  • Influencing factor
  • Pattern recognition net
  • Sewer maintenance
  • Sewer sediment condition assessment

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Building and Construction
  • Civil and Structural Engineering


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