Digital Twin and AI Research for Resilient Infrastructure

My research sits at the intersection of digital twins, AI-driven sensing, and structural engineering. The practical question that drives most of my work is straightforward: how do you know a bridge, a building, or a long-span steel structure is still safe when you cannot send an inspector every week? Over the past decade I have been developing methods that combine point-cloud reconstruction, physics-informed neural networks, and multi-agent reinforcement learning to answer that question in real time and at scale.

I hold a PhD from Universität der Bundeswehr München, where I investigated crack behaviour in reinforced-concrete slabs — work that required both structural mechanics and precision measurement, a combination that has shaped my research ever since. I went on to hold a permanent Associate Professor post at Oslo Metropolitan University (Norway), where I built the BIM Innovation Centre and supervised postgraduate students across Norway and China. I currently lead the Smart City and Sustainable Development Academy (SCSDA) as Founding Dean, a platform I established in 2019 to bridge European research capability with large-scale infrastructure deployment in China. In parallel I hold a part-time Visiting Professorship at Southeast University (QS 461), one of China's leading civil engineering institutions.

My most cited paper (340 citations, Energy and Buildings, 2022) describes a digital-twin predictive maintenance framework for air-handling units; a second line of work on nanosensors for bridge structural health monitoring has attracted 57 citations since 2023. Across 70 peer-reviewed papers, three provincial first-prize science awards, 20 invention patents, and 25 funded research grants, I have ensured that the methods I develop are tested on real infrastructure. Eight MEMS sensor products derived from this research have been deployed across more than 50 bridge and infrastructure projects.