Background
I am Qing-Chen Tang, a native of Sichuan, China. Since 2017, I have been specializing in the dynamics of complex train-bridge systems, vibration control of bridge structures, and finite element analysis of steel-concrete composite bridges. I graduated from Beijing Jiaotong University in 2023 with honors, earning 1st-level scholarships and an Excellent Master Thesis award. I then worked as an assistant researcher at the Rail Transit Electrification and Automation Engineering Technology Research Center (CNERC-Rail) at Hong Kong Polytechnic University. There, I studied robust control of thin-walled structures with multiple tuned mass dampers and collaborated with the Mass Transit Railway Corporation (MTR) to develop and test novel rail dampers for noise and vibration mitigation. Using machine learning, I evaluated the performance of these dampers in terms of human comfort.
Motivation
I am passionate about advancing sensor technologies and the Internet of Things (IoT) for enhanced signal acquisition and processing, particularly in structural health monitoring (SHM). My PhD project (DC5 – EDGE) aims to develop a reliable and cost-effective tool to support its safety decision-making for early bridge damage detection. In this project, I will integrate edge computing technology into dense sensor networks, enabling data processing and preliminary analysis directly at the edge, near sensor nodes. This approach optimizes the sensing networking architecture, reduces data transmission, and improves real-time monitoring capabilities. Additionally, I will also conduct both laboratory and field tests, and then utilize those data to train and validate machine learning models, which will ultimately be deployed on microcontrollers for autonomous, edge-based bridge damage detection. I believe my research will provide novel insights and drive the advancement of SHM with intelligent, automated sensing systems.