Papers
1
Total Citations
2
H-Index
1
About
Lingkang Li is a rising researcher in structural health monitoring and digital twin technologies, with a focus on integrating multi-fidelity modeling and time-series analysis for infrastructure resilience. Their most-cited work, "Digital twin structural health monitoring driven by multi-fidelity time-series surrogate models" (2025), introduces a novel framework that leverages surrogate models to bridge high- and low-fidelity data, enabling real-time, accurate damage detection and lifecycle prediction for complex structures. This contribution addresses a critical gap in digital twin applications—balancing computational efficiency with predictive fidelity—and has already garnered early citations for its practical implications in civil and mechanical engineering. Li’s research advances the field by combining machine learning, physics-informed modeling, and sensor data fusion, offering scalable solutions for aging infrastructure. While early in their career, their work signals a promising trajectory in smart monitoring systems, with potential to reduce maintenance costs and enhance safety in bridges, buildings, and aerospace structures. Li’s innovative approach to multi-fidelity surrogates positions them as a key contributor to the next generation of autonomous, data-driven structural health management.
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Top Papers
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