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Summary
This summary is machine-generated.

This study introduces a deep reinforcement learning (DRL) approach for optimal water pipe rehabilitation planning. DRL policies significantly reduce costs and failures compared to traditional methods, with offline learning showing further improvements.

Keywords:
Conservative Q-learningDeep Q-networksDeep reinforcement learningMaintenance planningOffline DRLWater distribution systems

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Area of Science:

  • Asset Management
  • Artificial Intelligence
  • Civil Engineering

Background:

  • Cost-effective asset management is crucial across industries.
  • Deteriorating water pipes pose significant infrastructure challenges.
  • Optimizing rehabilitation strategies is essential for extending asset lifespan and reducing failures.

Purpose of the Study:

  • To develop a deep reinforcement learning (DRL) solution for optimal water pipe rehabilitation.
  • To compare online and offline DRL approaches for rehabilitation planning.
  • To evaluate DRL policy performance against traditional methods.

Main Methods:

  • Implemented online DRL with an agent interacting in a simulated pipe environment.
  • Utilized deep Q-learning (DQN) for policy optimization in the online setting.
  • Employed conservative Q-learning with static data (DQN replay) for offline DRL.

Main Results:

  • DRL-based policies outperformed standard preventive, corrective, and greedy planning.
  • Offline DRL, using fixed replay data, demonstrated enhanced performance.
  • Water pipe deterioration data proved valuable for offline policy learning.

Conclusions:

  • DRL offers a powerful tool for optimizing water pipe rehabilitation strategies.
  • Offline DRL presents a promising approach, leveraging existing data for improved policy learning.
  • Further fine-tuning with simulation can enhance offline DRL policies.