Identification Methods for Structural Health Monitoring and Residual Lifecycle Assessment

June 3, 2013 — June 7, 2013

Coordinators:

  • Eleni Chatzi (ETH Zürich, Zurich, Switzerland)
  • Costas Papadimitriou (University of Thessaly (UTH), Volos, Greece)

Structural Health Monitoring (SHM) is a term coined several years ago within the research community; however it has only recently received increased attention when it comes to practical implementation. Although certain pronounced failures in large civil structures could be the trigger for this turning point, it was more so the realization of dealing with an ageing infrastructure demographic that stressed the need for monitoring methods other than traditional visual inspection. Infrastructure operators in developed countries are currently more and more concerned with the number of structures approaching their design lifespan and are faced with decision making processes for the proper maintenance, repair and future use of structural systems.
For infrastructure systems, SHM aims at developing a long-term monitoring system able to provide information for evaluating structural integrity, durability and reliability throughout the structure life cycle and ensuring optimal maintenance planning and safe operation. This task poses challenges at different levels, from the selection of appropriate instrumentation to the actual design of a structural health evaluation system. More recently, the significant progress in sensor development and communication technologies required for handling the bulk of generated data, has allowed for the deployment of dense sensor arrays at a relatively low cost.
As a result, advanced computational methods are required in order to handle the large bulk of information as well as to achieve an accurate online system representation that can serve as input for extreme event detection and life cycle assessment.
The purpose of this course is firstly to provide an introduction to well known and established system identification methods on SHM and secondly to introduce more advanced, state-of-the-art tools, able to tackle the challenges associated with actual implementation. In addition, focus will be given on full scale applications and field deployments that illustrate the workings and effectiveness of the introduced monitoring schemes. The deployments will involve monitoring of large scale landmark structures during regular operation as well as large scale systems that have undergone damage due to seismic events. As part of the more established methods, introductory concepts will be provided on the general framework of time and frequency domain, parametric and non-parametric methods, input-output or output only techniques.
The more innovative tools will include Bayesian methods and advanced modal identification techniques. Damage detection methods will be addressed as well, with a focus on inverse problem formulation within the SHM framework. The course syllabus will also involve advanced computational tools for uncertainty quantification and structural reliability in an effort to provide the link between monitoring and structural integrity assessment. Overall this is a joint effort from a diverse group of lecturers working on different aspects of SHM, to report the current state-of-the-art in the field and form collaboration network and shared knowledge platform in an area where there is still considerable room for research.
The course is addressed to doctoral and postdoctoral students, young and senior researchers, working in the fields of civil, mechanical engineering or other related disciplines.

KEYWORDS:
Structural Health Monitoring, Vibration Response, Structural Identification, Bayesian Computational Techniques, Uncertainty Quantification, Structural Reliability Assessment.

See also