Zhenkun Li

Zhenkun Li is a Postdoctoral Researcher at Aalto University’s Department of Civil Engineering, specializing in structural health monitoring (SHM) and computational methods. His research focuses on indirect SHM techniques, leveraging vehicle responses and machine learning for bridge damage detection.

Research Interests: Structural Engineering, Bridge Health Monitoring, Machine Learning, Signal Processing, and Drive-By Inspection Methods. His work integrates convolutional neural networks, deep learning, and physics-guided models to analyze bridge dynamics via shared scooters, smartphones, and instrumented vehicles.

Publication Trends: Recent articles (2023–2025) emphasize data-driven SHM, explainable machine learning for asphalt modeling, and innovative frameworks for bridge frequency identification. Techniques include multisynchrosqueezing transforms, Mel-frequency cepstral coefficients, and crowdsensing for real-time damage detection.