MS19 Machine Learning for Structural Health Monitoring and Reliability

Associate Prof. Heqing Mu: cthqmu@scut.edu.cn

Session Chairs:
Heqing Mu, Associate Professor, School of Civil Engineering and Transportation, State Key Laboratory of Subtropical Building Science, South China University of Technology, Guangzhou 510640, P.R. China. Email: cthqmu@scut.edu.cn
Stephen Wu, Assistant Professor, The Institute of Statistical Mathematics, Tokyo 190-8562, Japan. Email: stewu@ism.ac.jp
Kaveng Yuen, Distinguished Professor, State Key Laboratory on Internet of Things for Smart City, University of Macau, Macao SAR 999078, P.R. China; Chair Professor, School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510640, P.R. China. Email: kvyuen@um.edu.mo

Abstract of the special session:
Significant theoretical and practical efforts have been devoted for various problems in Structural Health Monitoring (SHM) and Reliability, but information extraction and inference from data are still considered to be challenging. Machine learning (ML) covers a set of methods that can automatically recognize, uncover and predict patterns of data under uncertainty. With the rapid evolution of ML, its development and application in SHM and reliability has begun to attract attention recently. The purpose of this special session is to provide opportunities for exchanging ideas on recent advances in ML, including modeling and fusing of heterogeneous data and making decision under uncertain model inference, in the fields of SHM and reliability. The topics may include but not limited to:

1. Data-driven modelling and inference in SHM and reliability
2. Advanced ML-based simulation in reliability
3. Heterogeneous data fusion in SHM
4. Computer vision in SHM and reliability