AI:Liner

AI:Liner is a European research and innovation project addressing the challenge of ageing sewer networks through data-driven, AI-based asset management. This page provides an overview of the project’s purpose, scope, structure and expected impact.

Across Europe, sewer networks are ageing while inspection resources remain limited and fragmented. Reliable condition data is often incomplete or underused, making it difficult to anticipate failures, prioritise interventions and plan investments effectively.

AI:Liner's mission is to support utilities and cities in moving from reactive maintenance practices towards informed, predictive decision-making. By combining advanced monitoring technologies with artificial intelligence, the project aims to reduce uncertainty, lower costs and minimise risks for people and the environment.

Our goal at the UT will be to investigate the occupational health and safety implications of introducing Artificial Intelligence (AI), robots, and Unmanned Aerial Vehicles (UAVs) into sewer inspection in minority and majority of the world scenario. Traditionally, sewer workers are exposed to confined spaces, toxic gases, biological hazards, and physically demanding tasks. While AI-enabled technologies have the potential to reduce these exposures by enabling remote inspections and data-driven decision-making, they may also introduce new challenges such as automation failures, cybersecurity risks, reduced situational awareness, and increased reliance on intelligent systems.

The research aims to understand how these technologies transform the work environment, identify both the safety benefits and emerging risks, and develop practical strategies for their safe adoption. Using literature reviews, stakeholder interviews, worker perception studies, and risk assessment methods, the project will contribute to the development of a human-centred occupational safety framework for AI-enabled sewer management. The expected outcome is to support utilities in implementing AI, robots, and UAVs in a way that improves worker safety, enhances trust in technology, and prepares the workforce for the digital transformation of critical infrastructure management.

The project led to the following student theses and ongoing assignments:

• the BSc thesis in Mechanical Engineering of Sude Ozkan,🎓 successfully defended on 15th July 2026. 
• The MSc thesis in Mechanical Engineering of Saikiran Samudrala <ongoing>

The AI:Liner project is funded by the Europen Eunion through the Horizon Europe research scheme.

For more information: Dr. Alberto Martinetti (a.martinetti@utwente.nl) or visit the main website at the link below!