Hu Qin



       Phone: +86-18827655437


     Academic Areas: Structural Engineering

     Research Interests:Structural model updating; structural dynamic analysis; structural health monitoring

Academic Degrees

PhD in Structural Engineering, 2010-2015, the City University of Hong Kong (CityU);
Bachelor in Structural Engineering, 2006-2010, Huazhong University of Science and Technology (HUST).

Professional Experience

School of Civil Engineering and Mechanics, HUST, Lecturer (2016-present);
Dept. Architecture & Civil Engineering, the City University of Hong Kong (CityU), Research Associate (2015-2016).

Selected Publications

1. Hu Q, Lam H F, Zhu H P, Alabi S A. Bayesian ballast damage detection utilizing a modified evolutionary algorithm. Smart Structures Systems 2018, 21(4):435-448.
2. Lam H F, Yang J H, Hu Q (corresponding author), Ng C T. Railway ballast damage detection by Markov chain Monte Carlo-based Bayesian method. Structural Health Monitoring 2017, (4): 147592171771710.
3. Hu Q, Lam H F and Alabi S A. Use of measured vibration of in-situ sleeper for detecting underlying railway ballast damage. International Journal of Structural Stability Dynamics 2015, 15(8): 1540026(1-14).
4. Lam H F, Hu Q and Wong M T. The Bayesian methodology for the detection of railway ballast damage under a concrete sleeper. Engineering Structures 2014, 81: 289-301.
5. Zhu H P, Hu Q, Lam H F. The Markov Chain Monte Carlo-based Bayesian model updating method in the damage detection of railway ballast under a concrete sleeper. EASEC15, 2017.
6. Lam H F, Hu Q and Alabi S A. Detection of damages on railway sleeper by Bayesian model class selection. EASEC14, 2016.
7. Hu Q and Lam H F. Bayesian methodology in railway ballast damage detection based on a continuous modeling method. HKSTAM19, 2015
8. Hu Q and Lam H F. Identification of the degradation of railway ballast under a concrete sleeper. ASEM13, 2013.

Courses Taught

Design of Concrete Structure and Masonry Structure
Descriptive Geometry and Civil Engineering Graphics


1. Bayesian-based Damage Detection of the Cement Asphalt Layer of the Slab Track: The National Natural Science Foundation of China Youth Fund Project (51708242).
2. The Damage Identification of the Cement Asphalt Layer of the CRTSI Slab Track based on Bayesian model class selection and model updating method: the Fundamental Research Funds for the Central Universities (2017KFYXJJ137).