Optimization of Tuned Mass Dampers to Improve the Earthquake Resistance of High Buildings

Optimization of Tuned Mass Dampers to Improve the Earthquake Resistance of High Buildings

Rolf Steinbuch (Reutlingen University, Germany)
Copyright: © 2015 |Pages: 38
DOI: 10.4018/978-1-4666-7336-6.ch018
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Abstract

To prevent high buildings in endangered zones suffering from seismic attack, TMD are applied successfully. In many applications the dampers are placed along the height of the edifice to reduce the damage during the earthquake. The dimensioning of TMD is a multidimensional optimisation problem with many local maxima. To find the absolute best or a very good design, advanced optimization strategies have to be applied. Bionic optimization proposes different methods to deal with such tasks but requires many repeated studies of the buildings and dampers design. To improve the speed of the analysis, the authors propose a reduced model of the building including the dampers. A series of consecutive generations shows a growing capacity to reduce the impact of an earthquake on the building. The proposals found help to dimension the dampers. A detailed analysis of the building under earthquake loading may yield an efficient design.
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Earthquake And Design For Earthquake Loading

Often we regard an earthquake loading of a building as a base excitation of the building (Towhata, 2008; Bozorgnia & Bertero 2004). As the mass of the surrounding ground is essentially larger than the building’s mass, the excitation may be considered as displacement controlled. The interface between the ground and the building has to follow these displacements.

Key Terms in this Chapter

Artificial Neural Net Optimisation: Adapting sets of artificial neurons to given test data to learn about the shape of the test data surfaces.

Particle Swarm Optimization: Copying the behaviour of animal swarms to find optima.

Tuned Mass Dampers: Masses connected to a vibrating system to reduce the amplitudes of the excited system.

Bionic Strategies: Optimization strategies that try to reproduce optimization schemes found in the nature.

Hybrid Strategies: Optimization by switching between different strategies.

Gradient Search Strategies: Strategies to find local optima by stepping along the gradient of the objective.

Isolation: Uncoupling of dynamic structures.

Restrictions: Data that decide whether designs comply with the requirements of the problem.

Goal or Objective: Function of the free parameters of a system which has to be maximized or minimized.

Ferns Optimisation: Optimization by sequences of mutation of initial designs.

Optimization: Process to improve a given objective by variation of free parameters.

Response Surfaces: Low degree polynomial surfaces to approximate test data.

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