Background
I am Qing-Chen Tang, a native of Sichuan, China. For the past eight years, I have specialized 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 pursuing a Ph.D. in sensors and the Internet of Things (IoT) for better signal acquisition and processing. My project, DC5 – EDGE: Edge computing and dense low-cost sensing for early damage detection, aims to develop a reliable and cost-effective tool for bridge safety decision-making. This project will involve adopting edge computing technology into sensors for architecture optimization and using machine learning to train and validate classification models with both laboratory and field-testing data. Ultimately, I aim to combine and apply these technologies to structural health monitoring, contributing to bridge engineering by providing a new solution for bridge damage detection.