刘攀平

Zhiyi Tang   唐志一

Ph.D., Lecturer

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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