Strain-Based Structural Health Monitoring Using Fiber Optic Sensors
SHMII-9 is proud to host Dr. Branko Glisic, Princeton University, as he delivers a short-course for civil engineers, researchers, practitioners, infrastructure managers and owners.
About the course
Structural health monitoring (SHM) is a process aimed at providing accurate and in-time information concerning structural health condition and performance. The information obtained from monitoring is generally used to increase the safety, plan and design maintenance activities, verify hypotheses, reduce uncertainty, and to widen the knowledge concerning the structure being monitored.
Recent developments in fiber optic sensing (FOS) technologies made possible global structural monitoring using long-gauge sensors and integrity monitoring using truly distributed sensors. These sensors combined in appropriate topologies and networks can provide for assessment of wide range of parameters relevant for structural behavior.
The aim of this course is to transfer the knowledge on SHM and FOS. Targeted groups are those who deal with or can take benefits from SHM: civil engineers, practitioners, consultants, contractors, infrastructure managers, owners, researchers and students.
Covered topics include:
brief introduction to SHM
overview of available FOS technologies
SHM methods based on FOS technologies
The topics are illustrated through numerous examples taken from practice.
About the lecturer
Prof. Branko Glisic, ISHMII’s Vice President for Education, has been engaged in R&D of structural health monitoring (SHM) methods and fiber-optic sensors (FOS) since 1996. Since February 2009, he has been employed at the Department of Civil and Environmental Engineering at Princeton University where he funded SHMlab. He was involved at different levels of responsibility in numerous SHM projects, EU, NSF, and USDOT-RITA-funded projects, and internal R&D projects. His expertise and current research interest include SHM methods and strategies, structural analysis, FOS and advanced sensory systems, and data management and analysis – system identification, damage detection, and data visualization.
Attendees are responsible for their own transportation and accommodation.