Group Discussion – Structural Health Monitoring

1. Group Discussion – Structural Health Monitoring Chairs: Chung Bang Yun Department of Civil & Environmental Engineering Korea Advanced Institute of Science and Technology (KAIST) KinchoH. Law Department of Civil & Environmental Engineering Stanford University Recorder: Kenneth J. Loh Department of Civil & Environmental Engineering University of California, Davis Number of participants in group discussion: 38+ Asian-Pacific Network of Centers for Research in Smart Structure Technology (ANCRiSST) Dalian, China July 25-26, 2011

2. 1. Smart Sensors & Sensing Technologies • Do we need to develop or improve upon current smart sensing technologies? • Yes, visual inspection still remains the current state-of-art • Several different categories where sensor development is needed: • Optical-based sensors or vision-based sensing • Include fusing fiber optic sensing with wireless capabilities • New materials and multifunctional materials • Multiple engineering functionalities: multi-modal sensing, energy scavenging, environmental monitoring, and actuation/interrogation, among others • Self-diagnosing and self-healing materials • European Union and select research groups (HIT, DLUT, UM) are very active • Need systems-approach for self-healing and self-diagnosis • Robotic sensing and associated infrastructure • Not only useful for SHM but also for security and gathering various information • Bio-inspired sensing still in its infancy • Chemical sensors for corrosion • Sensors for challenging environments • Deep-sea offshore oil platforms, wind turbines, built environment, etc. • Use of many not-so-smart or ordinary sensors for densely-distributed sensing

3. 2. Data Informatics and Data Mining • What information do we need to sense and how do we process the data? • Need new methodology for dealing with different types of sensors and data • Current research still dominated by vibration-based monitoring • Data fusion problem: global v. local and different types of sensor data • Issue of monitoring all members/locations v. critical locations/”hot spots” • Objective is to detect damage severity and location • Probability of Detection (POD): civil engineers need to start quantifying POD • Ex: how big of a crack can you detect, and what is the probability of detecting it? • Bio-informatics modeling and data modeling • Bio-inspired data interpretation and data mining • Biological creatures can autonomously differentiate between the variety of data collected • Spooling: fish can extract basic information from many neurons for decision making • Machine-learning and data-mining methods for analyzing large data sets • Imitate how doctors or professionals diagnose humans • Access to existing monitoring data or benchmark models • Prof. Li has collected information from damaged bridge structure and will make public • Jindo bridge monitoring data will potentially be made to the public late this year • Hong Kong Polytechnic University Guang Zhou TV Tower monitoring data on web • Talk to ANCRiSST

4. 3. Integrating SHM with Current Methods • How does one integrate SHM with current methods? • Life cycle cost and life cycle assessment • Need to integrate SHM with life cycle cost and assessment to achieve desired life cycle performance • Build resilient infrastructure while extending their service life • How will climate change affect civil infrastructure • Need collaboration across disciplines and research groups • Reliability-based SHM • Most cases, deterioration is gradual (fatigue, corrosion, loading, etc.) • Find probability of failure and predict remaining service life and cost of maintenance • Useful for future code development • Risk management • We know what vibration means, but the general public may not • Need ways of communicating to the general public for emergency management • Autonomous decision making strategies and communication systems (tsunami) • Accurate information is critical • Appropriate business models for each particular industry • Understand the needs of owners, government agencies, and stakeholders • Successful example is dams where it is heavily regulated with SHM

5. 4. Education • Structural health monitoring is a multi-disciplinary field of study • Some students may only focus on one small aspect of SHM • Is the current engineering curricula adequate for training our future students • Need online learning or e-learning approaches • Sharing of SHM course content and teaching materials • Asian-Pacific Summer School (APSS) on Smart Structures Technologies • Course contents and all teaching materials from previous years available online • After 6 APSS summer schools, a group of researchers will get together to write a textbook

6. 5.1. List of Attendees and Affiliations • China: • Hui Li (HIT) • GuofuQiao (HIT) • Zhichun Zhang (HIT) • Wentao Wang (HIT) • Dongsheng Li (DLUT) • Xiongwei Hu (DLUT) • YuequanBao (HIT) • Minghua Huang (HIT) • Binbin Li (DLUT) • Wensong Zhou (HIT) • Tong Guo (Southeast Univ) • Xu-feng Guan (HIT) • Ying Lei (Xiamen Univ.) • ChunguangLan (HIT) • Yan Yu (DLUT) • JianghuaRan (DLUT) • Yanhong Wang (DLUT) • Xuefeng Zhao (DLUT) • Xijun Ye (South China Univ.) • Tianfeng Zhu (South China Univ.) • Yong Huang (HIT) • Jilin Hou (DLUT) • Dongwang Tao • Xin Chun Guan (HIT) • YimingGu

7. 5.2. List of Attendees and Affiliations • United States: • Kincho Law (Stanford) • Ken Loh (UC Davis) • Dryver Huston (Univ. of Vermont) • Miao Yu (UM College Park) • Stephen Wu (Cal. Tech.) • Vanessa Heckman (Cal. Tech.) • Robin Kim (UIUC) • Yongchao Yang (Rich Univ.) • Korea: • Chung Bang Yun (KAIST) • Hyun Myimg (KAIST) • Japan: • Akira Mita (Keio Univ.) • ToshiOshima (Kitami Inst. of Tech.) • United Kingdom: • Hua-Peng Chen (Greenwich Univ.)