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Traditionally, component optimization is defined by minimizing the structural cost under a prescribed reliability target for a single limit state.
An optimization problem is defined by minimizing the structural risk functional [117] of SVM as a function of the H matrix.
Strategies for the generation, preliminary design and structural optimization are proposed for the different patterns; in particular the optimization process is treated with mono-objective genetic algorithms, by minimizing the structural weight and imposing a constraint condition on the lateral stiffness of the building.
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Structural properties, the energy-of-formation (EOF) from the elements and the cohesive properties of the various phases have been established by minimizing the internal structural parameters.
Given a mass ratio between the damper and structure, the stiffness and damping coefficient of the tuned mass damper are derived by minimizing the response objective function of the primary structure, where the structural properties are known.
Basic methods detect the location and severity of structural damage by minimizing the difference between test and analytic FRFs, which is a type of model updating or optimization method; however, the preferred method proposed in this paper uses only a subset of vectors from the full set of FRFs for a few frequencies and calculates the stiffness matrix and reductions in explicit form.
Maximum Likelihood estimation is then used to solve for the parameters by minimizing the difference between the covariance matrix implied by the structural model and the covariance matrix of the data (Bollen).
It offers very good performance for pattern classification problems by minimizing the Vapnik-Chervonenkis (VC) dimension and achieving a minimal structural risk.
By minimizing the potential energy of the representative cell in the undeformed SWCNC, structural parameters and elastic properties of the domain are obtained.
In this paper, the structural nonlinear properties are simulated by using hysteresis material parameters of Bouc Wen model, and the optimal values of the hysteresis parameters are obtained by minimizing the objective function using the simulated annealing global optimization method.
Friction Minimize friction on the axle by minimizing the contact surface area.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com