Introduction to the theory and methods of random vibrations and probabilistic engineering mechanics. A wide range of engineering applications require simulation and estimation of loads and excitations that are random in nature as they are often associated with hazards such as earthquakes, winds, waves, etc. The module will provide both fundamentals and application examples. A practical tutorial is also planned.
The module will introduce the theory and the methods of random vibrations and probabilistic engineering mechanics. A wide range of engineering applications require simulation and estimation of loads and excitations that are random in nature as they are often associated with hazards such as earthquakes, winds, waves, etc. The module will provide both fundamentals and application examples. A practical tutorial is also planned.
To access the course you can use the Google Meet platform (PhD students can use the link in Google Classroom; external interested parties are invited to send an email to: firstname.lastname@example.org no later than July 2, 2020 with subject: RANDOM VIBRATIONS COURSE
PhD DISG students can get 2+2=4 credits.
External participants are welcome, but there are NO credits for these.
• Probability theory, random variables and random processes;
• Classification of structural loads as stationary vs. non-stationary, discussion of the fundamental properties of random processes (e.g. ergodicity);
• Frequency domain analysis: Power Spectral Density Function (PSD) and its applications to engineering problems;
• Autocorrelation and autocovariance function, relationship with PSD;
• Estimation of the structural dynamic response subjected to stationary random loads in the frequency domain;
• Estimation of the peak load and structural response due to stationary random loads;
• Introduction to engineering problems involving non-stationary loads and response.
The module will present an overview of the general aspects and practical applications of dynamic inverse methods. A complete review of the techniques available for the identification of linear time-invariant systems together with a selection of case studies illustrates features and drawbacks of available identification procedures in real world applications. The aim is to highlight the role played by identification methodologies in the comprehension, construction and management of full-scale civil infrastructure such as bridges and dams, but also of historical monuments and public buildings. New potentialities of uses of ICTs in structural engineering problems are summarized together with key issues related to the integration between measurements, data analysis and identification
• Dynamical models and Inverse problems: physical and non-physical parametric models, non-parametric models.
• Modal identification in the frequency domain (FDD, EFDD)
• Modal identification in the time domain (ARMA, IV, ERA, SSI)
• Modal identification in operational conditions, modal normalization with added masses;
• Model updating;
• Modal testing and permanent monitoring;
• Introduction to damage detection by vibration data;
• Model-driven and data-driven modelling.