The SUBLIME consortium is proud to share that Ali Sabzi Khoshraftar, EngD researcher at the University of Twente’s Pervasive Systems Research Group, presented his latest research at the IEEE SmartNets 2025 conference. The conference took place from 22-24 July 2025 and hosted by Istinye University, Turkey, and brought together global experts in digital systems, monitoring technologies, and smart infrastructure. Ali’s paper, titled “Digital Twins for Bridge Assessment and Maintenance,” addresses a critical challenge faced by asset managers worldwide: how to efficiently and accurately monitor and maintain an aging stock of bridges in a safe, cost-effective, and environmentally conscious manner. Developed as part of Work Package 2 of the SUBLIME project, his work introduces a reference architecture for a predictive digital twin (DT) system tailored specifically for steel bridge maintenance. Read more on the M2i website
New study helps identify effective monitoring strategies for steel bridges
A new publication from the SUBLIME project examines how monitoring and inspection data can improve the assessment of fatigue-critical details in ageing steel bridges. Fatigue cracks can develop gradually under repeated traffic loads. Reliable information about these cracks is therefore essential for deciding whether a bridge can continue to be used safely and when maintenance is needed.
SUBLIME Annual Meeting Showcases Progress, Collaboration, and Real-World Impact
On 27 March, the SUBLIME consortium gathered for its annual user committee meeting, hosted by ProRail in a unique and inspiring setting at the Spoorwegmuseum in Utrecht. The meeting brought together academic researchers, industry partners, and infrastructure stakeholders to exchange insights, review progress, and strengthen collaboration across the project.
EngD Project Defence: Ali Sabzi Khoshraftar
The SUBLIME consortium is pleased to announce the successful defence of the Engineering Doctorate (EngD) project by Ali Sabzi Khoshraftar, who presented his work on the development and evaluation of predictive digital twins for steel bridge assessment and maintenance.