The long-term preservation of our infrastructure requires not only intelligent sensor technology and highly developed monitoring procedures, but also innovative digital tools for analyzing, evaluating and utilizing the results. This includes mathematical and, in particular, probabilistic methods for damage detection and tracking as well as for calculating service life and maintenance cycles. In our four-day training course from 20.-23.03.2023, you will be introduced to various issues and solution approaches, especially for damage detection with statistical pattern recognition, and will be comprehensively explained by the most competent lecturers.

The course on Structural Health Monitoring Using Statistical Pattern Recognition will introduce doctoral students, researchers and 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. In addition, actual research topics will be discussed and there is time for interaction with the lecturers. Paper notebook, electronic color copy of notes, software, data sets.

This training 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.

Preregistration can be made via the link below to register as a participant, please choose “Register as attendee”.  During the registration process you have to create an account. With this account you can login later, using the login box on the left side, to check your documents (invoice & ticket) or to change your personal data.

The registration fees are

  • 2600 € for professionals
  • 1600 € for PhD students

If you have any questions, please contact



The registration for the conference is supported by Converia, a conference management application.