刘攀平

Zhiyi Tang   唐志一

Ph.D., Lecturer

Kunming University of Science and Technology

621

School of Civil Engineering and Mechanics, KUST, Kunming

Github

About me

My research studies structural health monitoring (SHM) for civil infrastructures. I am interested in developing methods that learn structural behavior/performance as inverse problems. Also, I pay close attention to improve monitoring systems’ reliability.
I graduated from HIT, working with Prof. Yuequan Bao. I completed my bachelor’s degree and master’s degree there as well, advised by Prof. Hui Li. Now, I am working at Kunming University of Science and Technology, where is in my hometown!
We are looking for passionate new Master students, PhD students (me as a joint supervisor) to join the team in Fall 2023 (more info).

Papers

  • An Interpretable Deep Learning Method for Identifying Extreme Events under Faulty Data Interference (Appl Sci-Basel)   Link
  • Data Anomaly Detection for Structural Health Monitoring by Multi-View Representation Based on Local Binary Patterns (Measurement)   Preprint   Link
  • A data-driven multi-scale constitutive model of concrete material based on polynomial chaos expansion and stochastic damage model (Constr Build Mater)   Link
  • Machine-learning-based methods for output-only structural modal identification (Struct Control Hlth)  Preprint   Link
  • Group sparsity-aware convolutional neural network for continuous missing data recovery of structural health monitoring (Struct Health Monit)   Link
  • Deep reinforcement learning-based sampling method for structural reliability assessment (Reliab Eng Syst Safe)   Link
  • Clarifying and quantifying the geometric correlation for probability distributions of inter-sensor monitoring data: A functional data analytic methodology (Mech Syst Signal Pr)   Link
  • The State of the Art of Data Science and Engineering in Structural Health Monitoring (Engineering)   Link
  • Compressive-sensing data reconstruction for structural health monitoring: a machine-learning approach (Struct Health Monit)   Preprint   Link
  • Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring (Struct Control Hlth)   Link
  • Computer vision and deep learning–based data anomaly detection method for structural health monitoring (Struct Health Monit)   Link
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