<?xml version="1.0" encoding="UTF-8"?><ns2:project xmlns:ns1="http://gtr.rcuk.ac.uk/gtr/api" xmlns:ns2="http://gtr.rcuk.ac.uk/gtr/api/project" xmlns:ns3="http://gtr.rcuk.ac.uk/gtr/api/fund" xmlns:ns4="http://gtr.rcuk.ac.uk/gtr/api/person" xmlns:ns5="http://gtr.rcuk.ac.uk/gtr/api/project/outcome" xmlns:ns6="http://gtr.rcuk.ac.uk/gtr/api/organisation" ns1:created="2026-06-03T15:52:43Z" ns1:href="http://gtr.ukri.org/gtr/api/projects/8D2E4642-A585-4B56-8A77-F3C9863D36EB" ns1:id="8D2E4642-A585-4B56-8A77-F3C9863D36EB"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/D5E4C8E4-A42E-47FD-9C8B-541123475546" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/EEDC98D4-043E-41D7-A47A-DA484B733B8E" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/7B3A62F4-D635-4434-A899-9BCE884A9E88" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/47AC5C0C-6553-4E0E-B591-65899B6059E4" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/EEDC98D4-043E-41D7-A47A-DA484B733B8E" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2015-12-31T00:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/2F06C023-8D21-4E16-9812-2E9388B240BB" ns1:rel="FUND" ns1:start="2014-01-01T00:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">101704</ns2:identifier></ns2:identifiers><ns2:title>Intelligent Rail Defect Detection and Reporting in Real-time via Electromagnetics (i-R3D2)</ns2:title><ns2:status>Closed</ns2:status><ns2:grantCategory>Collaborative R&amp;D</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>Rail flaw detection is of paramount importance for the safe and reliable operation of rail networks. The Intelligent Rail Defect Detection and Reporting in Real-time via Electromagnetics (i-R3D2) project is aimed at developing an integrated rail defect detection system capable of complementing existing non-destructive techniques. The project will employ advanced data management and predictive analytics to provide rail maintenance personnel with timely rail defect information to enhance rail maintenance effectiveness as well as improve efficiency and safety. The project is led by Avonwood Development Ltd, in partnership with Avanti Communications Ltd and Birmingham University.</ns2:abstractText></ns2:project>