Self Healing Cities

This project is an exciting, strongly interdisciplinary collaboration, led by colleagues at the University of Leeds and involving academics at the University of Birmingham and University of Southampton, as well as local councils and industrial partners.

This project aims to tackle the Grand Challenge of zero disruption from streetworks in UK cities by 2050 by developing robots that will identify, diagnose and repair street-works through minimally invasive techniques, starting with three case studies:

1. “Perch and Repair" - drones that can perform repairs tasks, such as remote maintenance and modernisation of street lights.
2. "Perceive and Patch" - swarms of flying vehicles for autonomous inspection, diagnostics, repair and prevention of highway defects (e.g. potholes).
3. "Fire and Forget" - hybrid robots designed to operate indefinitely within live utility pipes performing inspection, repair, metering and reporting tasks.

It is anticipated that this project will lead to a wide range of benefits, including:

• Improving health, wellbeing, happiness and economic prosperity of people living within cities by reducing the impact of infrastructure engineering on natural systems.
• Stimulating UK research in the area of future cities through exploring the concept of sensing and automated repairs.
• Developing technologies for autonomous defect detection and diagnosis compatible with new and existing infrastructure.

As part of this project, the Institute of Making will be carrying out research into materials and 3D printing technologies for minimally-invasive sensing, maintenance and repair of city infrastructure. This includes assessing non-conventional materials for additive manufacturing for suitability in 3D repair of infrastructure, and mechanical testing of a range of materials suitable for 3D printing, scaffolds and inserts to assess their suitability and reliability for the task.

This year research focused on optimising crack repair durability through the 3D printing of functionally graded asphalt. We also developed novel visual crack detection approaches. These include: a noise tolerant and accurate crack tracking system; new noise tolerant edge detection from camera oscillations; and a hyper-spectral crack detection system.

See our new publication here:  Functionally graded 3D printed asphalt composites, in Materials Letters: X (March 2020)