Papers
1
Total Citations
2
H-Index
1
About
Haokun Li is a rising researcher at the forefront of structural health monitoring and digital twin technologies. His work centers on developing advanced computational frameworks that integrate multi-fidelity modeling, time-series analysis, and surrogate models to enable real-time, data-driven assessment of infrastructure integrity. Li’s most-cited paper, “Digital twin structural health monitoring driven by multi-fidelity time-series surrogate models” (2025), introduces a novel approach that fuses high- and low-fidelity simulation data to create efficient, accurate digital replicas of physical structures. This contribution addresses a critical challenge in civil engineering: balancing computational cost with predictive accuracy for continuous monitoring. Though early in his career, with 2 citations to date, the work has already garnered attention for its potential to revolutionize predictive maintenance and resilience planning in aging infrastructure. Li’s research promises to bridge the gap between theoretical simulation and practical, real-world deployment, making him a promising voice in the next generation of smart infrastructure and digital twin innovation.
Research Focus
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Top Papers
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