The short courses Structural Health Monitoring Using Statistical Pattern Recognition and Verification and Validation (V&V) of Computational Models are offered before the conference. Further details and course registration are available on the course organizer’s website www.la-dynamics.com.
Structural Health Monitoring Using Statistical Pattern Recognition will introduce engineers to the field of damage assessment (detection, location, severity) in structures as determined from changes in their measured dynamic response. In addition to the historical motivation and development of the methods, the course will cover the theory, application, and computerized implementation of this technology with hands-on software exercises. Many real-world examples and results will be presented from the fields of aerospace, civil, and mechanical engineering. The application of statistical pattern recognition techniques will be emphasized throughout the course.
This course is designed for those who seek a thorough understanding of the analytical techniques for SHM as well as an appreciation for practical implementation issues. The instructors will assume a basic knowledge of structural mechanics, dynamics and mathematics obtained in a bachelor’s aerospace, civil or mechanical engineering curriculum.
Course Material Provided: notebook, CD with color copy of notes, software, data sets and reference book: Structural Health Monitoring: A Machine Learning Perspective
This short-course on the Verification and Validation (V&V) of computational models teaches techniques to quantify prediction uncertainty which includes the broad classes of, first, numerical uncertainty caused by truncation effects in the discretization of partial differential equations and, second, parametric uncertainty caused by the variability of model parameters. It focuses on applications in structural mechanics and structural dynamics. The quantification includes the propagation and assessment of how much uncertainty is present in the simulation of an application of interest (“what are the sources, how much uncertainty is present?”). It includes understanding which effects control the uncertainty (“is it predominantly the mesh discretization, parameter variability, or other phenomena?”) and what can be done to reduce the overall uncertainty (“should the mesh be refined, should small-scale experiments be performed, should model parameters be calibrated and how?”).
The short-course is intended for graduate students, researchers, practicing engineers and project managers seeking to understand, or implement, V&V techniques for their applications. The goal is to provide a sufficient understanding of key techniques such that attendees are able to discuss them with their peers, read the pertinent literature implement and apply them to their applications. Graduate students and researchers will be pointed towards essential techniques without having to endure months of literature review. Practicing engineers will understand how to integrate them into a logical process for their applications. Project managers will be exposed to way to define quality controls for the numerical simulations that their projects and customers rely on.
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