AI-Assisted Hydraulic Analysis Layer for EPANET-UI

[razaali10]

Proposal

I would like to propose an idea for discussion regarding the future evolution of EPANET-UI: integrating an AI-assisted hydraulic analysis layer directly into the interface.

The goal is not to replace EPANET’s hydraulic engine or engineering judgment, but to augment the user’s ability to interpret, diagnose, and interact with water distribution system models more efficiently.

Motivation

As water distribution models become larger and more complex, engineers often spend significant time manually reviewing:

• pressure and flow results,
• tank behavior,
• headloss patterns,
• fire flow deficiencies,
• pump operations,
• and extended period simulation outputs.

While EPANET provides excellent computational capabilities, interpretation of results still relies heavily on manual inspection of tables, plots, and reports.

An integrated AI assistant could help bridge the gap between raw simulation outputs and engineering understanding.

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Proposed Concept

The idea is to integrate a chatbot-style engineering assistant within EPANET-UI that has controlled access to:

• the INP model,
• simulation outputs,
• report files,
• network topology,
• selected network elements,
• and potentially GIS-linked information.

The assistant would function as an interpretation and reasoning layer on top of the EPANET engine.

Importantly:

EPANET would remain the authoritative hydraulic solver.
The AI layer would assist with analysis, explanation, diagnostics, and engineering insight.

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Example Capabilities

Network Understanding

Users could ask:

• “Summarize this network.”
• “Identify pressure zones.”
• “Find dead-end mains.”
• “Which pipes experience the highest headloss?”

The assistant could automatically generate concise engineering summaries of the model structure and behavior.

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Hydraulic Diagnostics

Example queries:

• “Why is pressure low at Junction J-120?”
• “Which pipes are operating above recommended velocity?”
• “Explain why Tank T-2 is cycling rapidly.”
• “Identify bottlenecks affecting fire flow.”

Instead of only presenting numerical outputs, the assistant could provide causal explanations based on hydraulic relationships.

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Resilience and Criticality Analysis

Future integration with tools such as WNTR could support:

• critical pipe identification,
• resilience metrics,
• failure scenario analysis,
• service deficit evaluation,
• and redundancy assessment.

This could be especially valuable for utilities exploring infrastructure resilience and asset management.

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Suggested Architecture

Rather than allowing a large language model to directly interpret raw EPANET files, a safer and more engineering-focused approach may be:

EPANET Engine
      ↓
Analysis Layer
      ↓
Structured Hydraulic Features
      ↓
LLM Assistant

The intermediate analysis layer could compute:

• minimum/maximum pressures,
• critical links,
• abnormal velocities,
• fire flow deficiencies,
• tank turnover statistics,
• pump operating summaries,
• and resilience indicators.

These structured engineering features would then be passed to the AI assistant for interpretation and explanation.

This approach could help:

• reduce hallucination risk,
• improve explainability,
• and maintain engineering rigor.

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Potential Long-Term Extensions

Possible future directions could include:

• WNTR integration,
• TSNet transient analysis support,
• GIS integration,
• SCADA/digital twin connectivity,
• operator training assistance,
• report generation,
• and municipal design criteria review.

For example, an assistant could eventually help evaluate models against utility standards such as:

• minimum pressure criteria,
• fire flow requirements,
• storage requirements,
• or velocity limits.

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Why This May Be Valuable

I believe this concept could help make EPANET:

• more accessible for new users,
• more efficient for experienced engineers,
• and more useful as a decision-support platform for utilities.

The water industry is increasingly interested in explainable AI and digital infrastructure tools, and EPANET-UI could become an excellent platform for exploring these ideas in an open and engineering-centered way.

I would be very interested in hearing feedback from the community regarding:

• technical feasibility,
• preferred architecture,
• possible use cases,
• and whether similar ideas are already being explored within OpenWaterAnalytics.

Thank you for your time and consideration.