Zhiyi Tang   唐志一

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 2024 (more info). If you are interested, please send your CV to tang@kust.edu.cn

News

• Supported by Yunnan Provincial Science and Technology Department - Kunming University of Science and Technology Joint Project, January 2024.
• Supported by Yunnan Fundamental Research Project, March 2023.
• Selected as a Young Talent of Yunnan Province "Xing Dian Talent Program", November 2022.
• Our team was successfully granted as "Intelligent Infrastructure Operation and Maintenance Technology Innovation Team of Yunnan Provincial Department of Education", September 2022.
• Awarded the Excellent Doctoral Dissertation of HIT (31 over ~1000 yearly), July 2022.
• I joined Kunming University of Science and Technology (Fifth-level of high-level talent introduction program), December 2021.
• Our new research was selected as the Featured Cover! Link, November 2021.
• Got my Ph.D.. Hooray! July 2021.
• Volunteering in IPC-SHM 2020. Welcome to play and have fun! Info: Link#1, Link#2, June 2020.
• Awarded the National Scholarship of Ministry of Education (8 over ~300 yearly), October 2019.
• I am visiting Smart Informatix Laboratory at Purdue for one year, working with Dr. Mohammad Reza Jahanshahi, October 2019.
• Awarded the 1st Prize Scholarship of Ministry of Industry and Information Technology (teamwork, 10 persons or teams over ~30000 yearly), 4 March 2019.
• A mini dataset for image-based crack identification of long-span bridge girder is released, 15 October 2018.
• Volunteering in APESS-2018! Tsingdao & Harbin, China, 16 July - 5 August 2018.
• "Artificial Intelligence-based Data Anomaly Detection Method for Structural Health Monitoring" was selected as an excellent paper of WTC2018 (74 over 1829), Beijing, 20 June 2018.
• Completed "Deep Learning Specialization" (5 courses lectured by Andrew Ng), Cousera.org, 29 May 2018. What a wonderful journey!   Link of certification
• Best Performance Award - 2nd Prize (8 groups) in APESS-2017, Yokohama National University, Japan, 4 August 2017.

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