Associated Researcher at Hunan University, China
Jinzhao Li is a dynamic researcher 🧠 specializing in intelligent construction within civil engineering, currently serving as an Associate Researcher at Hunan University. With a keen focus on AI-infused methods and hydrodynamics, Li has contributed extensively to national infrastructure projects, including the Hong Kong-Zhuhai-Macao Bridge 🌉. His academic collaborations span globally renowned institutions such as TU Denmark 🇩🇰, Delft University 🇳🇱, and the University of Tokyo 🇯🇵. With over 50 research publications—including one in Nature Communications Engineering—more than 10 patents, and numerous funded projects, his work blends deep learning, computer vision, and fluid mechanics. Recognized with awards like the Silver Medal 🥈 in the Hunan Postdoctoral Innovation Competition and the China Railway Society Science and Technology Prize, Li exemplifies scientific excellence. His dedication to smart infrastructure and sustainable construction makes him a pivotal contributor to future-ready civil engineering. 🚀
Professional Profile
Scopus
🎓 Education
Jinzhao Li’s academic journey reflects a powerful blend of engineering rigor and global exposure 🌍. He earned his Bachelor’s degree in Traffic Engineering from Shandong University of Science and Technology (2007–2011), followed by a Ph.D. in Bridge and Tunnel Engineering at Beijing Jiaotong University (2011–2017), under an integrated Master–Ph.D. program. His doctoral work, under the mentorship of Professor Meilan Qi, provided the foundation for his specialization in bridge hydrodynamics and scour processes. To further internationalize his research acumen, he completed joint training at the Technical University of Denmark 🇩🇰, working closely with Professor David R. Fuhrman—an authority in applied ocean research. These cross-continental educational experiences deeply influenced his multi-disciplinary expertise in wave mechanics, AI applications in structural monitoring, and deep-learning physics modeling. 📚 His education solidified not only technical knowledge but also his global research mindset, setting the stage for a versatile, high-impact scientific career. 📖🧠
💼 Professional Experience
Jinzhao Li’s professional path traverses academia, research institutes, and high-impact engineering projects 🏗️. Starting as an Assistant Researcher at the Tianjin Research Institute of Water Transport Engineering (2018–2019), he contributed to national megaprojects like the Shenzhen-Zhongshan Passage and floating tunnel studies. His transition to academia led to a Lecturer role at Shandong University of Science and Technology (2019–2020), where he also served as Deputy Head of Department. Since 2020, he has been advancing frontier research as a Postdoctoral Fellow and now as an Associate Researcher at Hunan University. 🚀 Under collaborative mentorship with National Youth Thousand Talents Professor Xuan Kong, Li steers projects integrating AI, computer vision, and coastal engineering. His work reflects a harmonious blend of theory and fieldwork, evident in his involvement with hydrodynamic model testing, drone-based monitoring systems, and AI-driven structural health diagnostics. 📡 His professional versatility makes him a cornerstone in smart infrastructure R&D.
🧠 Research Interests
Jinzhao Li’s research interests are a fusion of artificial intelligence and civil engineering phenomena 💡. He pioneers in AI intelligent computing, integrating physics-driven deep learning to model real-world complexities such as wave-structure interaction and scour evolution. His work in computer vision-based flow measurement enables high-fidelity monitoring of structural dynamics, especially in disaster-prone flood zones 🌊. As part of his broader vision, he also delves into intelligent fluid dynamics, bridging fluid simulation with machine learning to advance structural resilience. His studies on bridge hydrodynamics and scouring have practical implications for coastal infrastructure safety, while his exploration of flood disaster monitoring employs drones and optical flow algorithms. 📹🔍 Blending neural networks with marine physics, Li pushes the boundary of what intelligent infrastructure can achieve. His focus aligns with smart, sustainable, and responsive design systems—a true intersection of digital intelligence and environmental engineering. 🌐
🏆 Awards and Honors
Jinzhao Li has earned prestigious accolades recognizing both his innovative spirit and technical prowess 🥇. Notably, he was selected for the “Hunan Province Outstanding Postdoctoral Innovation Talent Program”, affirming his place among China’s rising scientific leaders. He secured the Silver Award 🥈 in the First Hunan Postdoctoral Innovation and Entrepreneurship Competition, spotlighting his blend of applied and entrepreneurial science. His technical contributions were further recognized through the China Railway Society Science and Technology Second Prize, a high honor in engineering innovation. Internationally active, Li has served as Guest Editor for the SCI journal Sustainability and is a regular reviewer for top-tier SCI journals. His papers—some co-authored with world-class scientists—have garnered over 900 citations and an H-index of 16, confirming his scientific impact 📊. These honors echo his exceptional integration of AI, hydrodynamics, and vision-based civil engineering.
📚 Publications Top Note
1. Physics‑preserved graph learning of differential equations for structural dynamics
Authors: (Not specified in search snippet)
Year: 2025
Citations: 0 (appears recent)
Source: Mechanical Systems and Signal Processing
Summary:
This study introduces a novel graph-based learning framework that incorporates the underlying partial differential equations (PDEs) governing structural dynamics directly into the model. By encoding displacement, velocity, and energy dissipation processes via conservation laws within a graph neural network, the model can predict structural responses while adhering to physical laws. It aims to combine data-driven flexibility with physics-based constraints for improved interpretability and generalization under dynamic loads. The approach shows promising accuracy on simulated structural dynamic scenarios.
2. Vehicle Response‑Based Bridge Modal Identification Using Different Time‑Frequency Analysis Methods
Authors: (Not specified in snippet)
Year: 2025
Citations: 5
Source: International Journal of Structural Stability and Dynamics
Summary:
This paper proposes a method leveraging a moving vehicle’s response to identify bridge modal frequencies and mode shapes. It combines Empirical Mode Decomposition (EMD) with advanced time–frequency analysis (e.g. wavelets) to isolate bridge signature from vehicle–track–bridge interactions. Field and simulation results show that this hybrid approach enhances modal identification performance, improving accuracy even amid road surface noise and vehicle dynamics.
3. Full‑field modal identification of cables based on subpixel edge detection and dual matching tracking method
Authors: Jinxin Yi, Xuan Kong, et al.
Year: 2025
Citations: 0
Source: Mechanical Systems and Signal Processing
Summary:
This research introduces a computer vision‑based framework for extracting full‑field cable modal properties in cable-stayed bridges. By applying subpixel edge detection via LSD (Line Segment Detector) on video footage, followed by a dual-matching tracking algorithm, the method captures dense dynamic displacement data. It then derives modal frequencies and employs frequency differences to compute cable tension, avoiding preset tuning parameters. Verified with laboratory and field tests, the approach is robust and accurate.
✅ Conclusion
In summation, Jinzhao Li stands as a visionary in civil and computational engineering—a scientist bridging traditional hydrodynamics with cutting-edge artificial intelligence 🤖🌊. His career is marked by international collaborations, impactful research outputs, and real-world applications in infrastructure monitoring, disaster prediction, and intelligent design. From postdoctoral recognition in Hunan to Nature-Communications-level publications, his work exemplifies future-focused engineering with societal relevance. Whether optimizing bridge scour prediction through computer vision or leading drone-based flood warning systems, Li’s contributions embody the shift toward data-driven, smart construction ecosystems 🏗️📈. With more than 50 academic publications, 10+ patents, and a robust portfolio of funded research, he is a deserving candidate for elite research honors and fellowships. As AI and civil engineering continue to converge, Jinzhao Li is set to be a torchbearer of the next-generation engineering renaissance. 🌍🔬