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Room 307, 3/F, Mong Man Wai Building
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image of Dr. DANG Pengfei
Office: 
Room 307, 3/F, Mong Man Wai Building

DANG Penfgei 黨鵬飛

Honorary Postdoctoral Fellow
 
Education:
  • Ph.D., Disaster Prevention and Reduction Engineering and Protective Engineering, Institute of Engineering Mechanics, CEA, 2015 - 2021.
  • B.S., Civil Engineering, Northwestern Polytechnical University, 2007 - 2011.
 
Academic Employments:
  • 2021 - present, Postdoctoral Fellow, Assistant Researcher, Distinguished Associate Researcher, Guangzhou University, Guangzhou, China.
  • 2022 - present, Honorary Postdoctoral Fellow, Earthquake System Science Programme, The Chinese University of Hong Kong.
 
Research Fields and Current Research Interests:
  • Ground motion prediction equation
  • High-frequency ground motion simulation
  • Generalized inversion technique
  • Near-field motion characteristic and site effect
 
Representative Publications:
Please visit https://www.researchgate.net/profile/Pengfei-Dang-3/research?ev=prf_act for full publication list.
 
Honours and Awards:
  • First-class scholarship for postgraduate students in 2017 and 2020.
  • Second-class scholarship for postgraduate students in 2018.
 
Professional Activities
Past
  • 2022 CGU Annual Meeting: Oral presentation on Site amplification and rupture velocity in finite-fault modeling of ground motion during the 2017 Mw6.6 Jiuzhaigou, China earthquake.
  • Journal reviewer for Bulletin of Earthquake Engineering, Journal of Earthquake Engineering, Earth and Space Science, Shock and Vibration, Open Life Sciences, Scientia Iranica, Methods X, IWEG-2022, et al.
 
College Affiliation in CUHK
  • New Asia College
 
Selected Recent Publications
  1. Dang Pengfei*, Cui Jie, Liu Qifang, Ji Linjian. (2022). A method for predicting hybrid source model of near-field ground motion: Application to Yangbi earthquake in China. Seismological Research Letters, doi:10.1785/0220220135.
  2. Dang Pengfei*, Chong Wang, Wenhao Qi, Li Yadong. (2022). An updated stochastic finite fault modeling: Application to the Mw 6.0 earthquake in Jiashi, China. Soil Dynamics and Earthquake Engineering, 162, 107450.
  3. Dang Pengfei*, Cui Jie, Liu Qifang. (2022). Parameter estimation for predicting near-fault strong ground motion and its application to Lushan earthquake in China. Soil Dynamics and Earthquake Engineering, 156, 107223.
  4. Dang Pengfei*, Cui Jie, Liu Qifang, Xia Songlin. (2022). Slip-correlated high-frequency scaling factor for stochastic finite-fault modeling of ground motion. Bulletin of the Seismological Society of America, 112(3): 1472-1482.
  5. Dang Pengfei*, Liu Qifang*, Xia Songlin, Ma Wanjun. (2021). A stochastic method for simulating near-field seismograms: Application to the 2016 Tottori earthquake. Earth and Space Science, 8(11), e2021EA001939.
 
Review Article
  • Dang Pengfei, Liu Qifang*, Wang Chong, Xia Songlin. (2020). Review on the stochastic finite-fault ground motion simulation method. Earthquake Engineering and Engineering Dynamics, 40(6): 131-139. (In Chinese).
  • Dang Pengfei, Wang Wenbo, Li Yadong, Cui Jie*. (2022). Fluid Geological Process and Resource Environment Effect—Summary of the 8th Symposium on Fluid Geoscience, Mineral Resources and Environmental Disasters. Journal of Earth Science and Environment. (In Chinese). Accepted manuscript.
 
Research Grants
  • Study on the near-field ground motion simulation and the effect of seismic waves on the response of surface structures, 2011/11-2023/11, ¥200,000, Postdoctoral Science Foundation of Guangzhou City, China. (Dang et al.)
  • Research on parameter estimation of near-fault ground motion simulation and ground motion prediction model based on site characteristics, 2022/09-2023/09, ¥400,000, Postdoctoral Program of International Training Program for Young Talents of Guangdong Province, China. (Dang et al.)
  • Research on near-field high-frequency strong ground motion simulation method based on non-uniform slip distribution, 2023/01—2025/12, ¥300,000, Natural National Science Foundation for Young Scientists of China. (Dang et al.)
  • Deep learning and mining of site condition characteristics in ground pulsation, 2020/01—2023/12, ¥600,000, Natural National Science Foundation of China. (Shi et al.)