### Comparison And Evaluation Of Two Approaches Of Uncertainty Modeling In Dynamcal Systems.

#### Abstract

When mechanical systems are modeled, uncertainties should be taken into account for improving

the predictability of the model. In this work a two d.o.f. (degrees of freedom) dynamical system

is used to compare two strategies to model uncertainties in structural dynamics. Uncertainties are considered

present only on the spring stiffnesses. In the first approach, uncertainties are inserted into each

spring stiffness. A probabilistic model is constructed for each random variable associated to each spring

stiffness. In the second approach, uncertainties are considered in a global way, that is, a probability

model is constructed for the stiffness matrix. In both approaches, the probability density functions are

deduced from the Maximum Entropy Principle, using only the available information. The simple example

used is helpful to assure a better understanding of the two approaches. The event space generated by

each strategy will be shown and it will be discussed how good they are to predict data uncertainties and

model uncertainties.

the predictability of the model. In this work a two d.o.f. (degrees of freedom) dynamical system

is used to compare two strategies to model uncertainties in structural dynamics. Uncertainties are considered

present only on the spring stiffnesses. In the first approach, uncertainties are inserted into each

spring stiffness. A probabilistic model is constructed for each random variable associated to each spring

stiffness. In the second approach, uncertainties are considered in a global way, that is, a probability

model is constructed for the stiffness matrix. In both approaches, the probability density functions are

deduced from the Maximum Entropy Principle, using only the available information. The simple example

used is helpful to assure a better understanding of the two approaches. The event space generated by

each strategy will be shown and it will be discussed how good they are to predict data uncertainties and

model uncertainties.

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Güemes 3450

S3000GLN Santa Fe, Argentina

Phone: 54-342-4511594 / 4511595 Int. 1006

Fax: 54-342-4511169

E-mail: amca(at)santafe-conicet.gov.ar

**Asociación Argentina de Mecánica Computacional**Güemes 3450

S3000GLN Santa Fe, Argentina

Phone: 54-342-4511594 / 4511595 Int. 1006

Fax: 54-342-4511169

E-mail: amca(at)santafe-conicet.gov.ar

**ISSN 2591-3522**