The inherent stochastic character of most of the physical quantities involved
in engineering models has led to an always increasing interest for probabilistic
analysis.
Many approaches to stochastic analysis have been proposed. However, it
is widely acknowledged that the only universal method available to solve accurately
any kind of stochastic mechanics problem is Monte Carlo Simulation.
One of the key parts in the implementation of this technique is the accurate and
efficient generation of samples of the random processes and fields involved in the
problem at hand. In the present thesis an original method for the simulation
of homogeneous, multi-dimensional, multi-variate, non-Gaussian random fields
is proposed. The algorithm has proved to be very accurate in matching both
the target spectrum and the marginal probability. The computational efficiency
and robustness are very good too, even when dealing with strongly non-Gaussian
distributions. What is more, the resulting samples posses all the relevant, welldefined
and desired properties of “translation fields”, including crossing rates
and distributions of extremes.
The topic of the second part of the thesis lies in the field of non-destructive
parametric structural identification. Its objective is to evaluate the mechanical
characteristics of constituent bars in existing truss structures, using static loads
and strain measurements. In the cases of missing data and of damages that
interest only a small portion of the bar, Genetic Algorithm have proved to be
an effective tool to solve the problem.
| Identifer | oai:union.ndltd.org:unibo.it/oai:amsdottorato.cib.unibo.it:960 |
| Date | 21 May 2008 |
| Creators | Bocchini, Paolo <1979> |
| Contributors | Viola, Erasmo |
| Publisher | Alma Mater Studiorum - Università di Bologna |
| Source Sets | Università di Bologna |
| Language | English |
| Detected Language | English |
| Type | Doctoral Thesis, PeerReviewed |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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