12 citations · 13 across the 3 of their papers we have counts for
4 papers
SDIGLM: Leveraging Large Language Models and Multi-Modal Chain of Thought for Structural Damage Identification
Yunkai Zhang, Shiyin Wei, Yong Huang +3
Existing computer vision(CV)-based structural damage identification models demonstrate notable accuracy in categorizing and localizing damage. However, these models present several…
A robust deep learning-based damage identification approach for SHM considering missing data
Fan Deng, Xiaoming Tao, Pengxiang Wei +1
Data-driven method for Structural Health Monitoring (SHM), that mine the hidden structural performance from the correlations among monitored time series data, has received widely c…
A change-point detection method for detecting and locating the abrupt changes in distributions of damage-sensitive features of SHM data, with application to structural condition assessment
Xinyi Lei, Zhicheng Chen, Hui Li +1
Diagnosing the changes of structural behaviors using monitoring data is an important objective of structural health monitoring (SHM). The changes in structural behaviors are usuall…
General solutions for nonlinear differential equations: a rule-based self-learning approach using deep reinforcement learning
Shiyin Wei, Xiaowei Jin, Hui Li
A universal rule-based self-learning approach using deep reinforcement learning (DRL) is proposed for the first time to solve nonlinear ordinary differential equations and partial…