The short-course “Stochastic Dynamics Techniques for Civil Engineering Applications” is going to be given at the Natural Ocean Engineering Laboratory NOEL on July 19, 2017. The aims of the short-course focus on introducing the basic concepts and computational tools available for addressing problems in the field of Stochastic Engineering Dynamics. Examples are presented from a perspective of usefulness to civil, mechanical and marine engineering applications.

The course is organized in two parts:

Part I introduces the concepts of probability and random process theory for addressing problems pertaining to civil and marine engineering.

It proposes the following contents:

Introduction (0,5hrs): Motivation: Probability in civil engineering applications;

Probability and random process theory (1,5hrs): Random process theory (random variables, expectation of random variables, the Gaussian distribution, the concept of stochastic process, spectral decomposition);

Probability and random process theory (2 hrs): Monte Carlo simulations (overview on Monte Carlo simulations; spectral representation; numerical example: generation of a free surface displacement time history).

Part II discusses the fundamentals of random vibration and approximate methods for addressing applications of practical/engineering interest.

It proposes the following contents:

Introduction (0,5 hrs): Motivation: Historical elements of Random Vibration Theory;

Random Vibration (Stochastic Engineering Dynamics) (1,5 hrs): Linear Systems (Stochastic Input-Output Relationships, Response Determination of Single and Multi Degree of Freedom Lumped Parameter Systems: Time and Frequency Domains Approaches);

Random Vibration (Stochastic Engineering Dynamics) (2 hrs): Nonlinear Systems (Approximate Techniques for Stochastic Response Determination: Focus on Statistical Linearization).

Short-course “Stochastic Dynamics Techniques for Civil Engineering Applications” – July 19, 2017

Short-course “Stochastic Dynamics Techniques for Civil Engineering Applications” – July 19, 2017

19/06/2017

Title

Short-course “Stochastic Dynamics Techniques for Civil Engineering Applications” – July 19, 2017

by Prof. Ioannis A. Kougioumtzoglou, Columbia University, NY, USA; and Dr. Giovanni Malara, Mediterranea University of Reggio Calabria

Content

The short-course “Stochastic Dynamics Techniques for Civil Engineering Applications” is going to be given at the Natural Ocean Engineering Laboratory NOEL on July 19, 2017. The aims of the short-course focus on introducing the basic concepts and computational tools available for addressing problems in the field of Stochastic Engineering Dynamics. Examples are presented from a perspective of usefulness to civil, mechanical and marine engineering applications.

The course is organized in two parts:

Part I introduces the concepts of probability and random process theory for addressing problems pertaining to civil and marine engineering.

It proposes the following contents:

Introduction (0,5hrs): Motivation: Probability in civil engineering applications;

Probability and random process theory (1,5hrs): Random process theory (random variables, expectation of random variables, the Gaussian distribution, the concept of stochastic process, spectral decomposition);

Probability and random process theory (2 hrs): Monte Carlo simulations (overview on Monte Carlo simulations; spectral representation; numerical example: generation of a free surface displacement time history).

Part II discusses the fundamentals of random vibration and approximate methods for addressing applications of practical/engineering interest.

It proposes the following contents:

Introduction (0,5 hrs): Motivation: Historical elements of Random Vibration Theory;

Random Vibration (Stochastic Engineering Dynamics) (1,5 hrs): Linear Systems (Stochastic Input-Output Relationships, Response Determination of Single and Multi Degree of Freedom Lumped Parameter Systems: Time and Frequency Domains Approaches);

Random Vibration (Stochastic Engineering Dynamics) (2 hrs): Nonlinear Systems (Approximate Techniques for Stochastic Response Determination: Focus on Statistical Linearization).