Pontificia Universidad Católica de Chile Pontificia Universidad Católica de Chile
Ruiz RO, Taflanidis AA, Lopez-Garcia D (2015): Seismic optimization of a novel tuned sloshing damper for the Chilean region based on life-cycle cost criteria. 12th International Conference on Applications of StatisticsProbability in Civil Engineering, electronic paper no. 146, Civil Engineering Risk and Reliability Association, Vancouver, Canada. (2015)

Seismic optimization of a novel tuned sloshing damper for the Chilean region based on life-cycle cost criteria

Tipo de publicación : Conferencia No A*

Abstract

The design of a new liquid damper device is considered in this paper based on life-cycle criteria. This new device, called Tuned Liquid Damper with Floating Roof (TLD-FR) maintains the advantages of traditional Tuned Liquid Dampers (low cost, easy tuning, alternative use of water) while establishing a linear and generally more robust/predictable damper behaviour through the introduction of a floating roof. This behaviour can be characterized by four dimensional parameters that represent the design variables for the system and are all related to the tank geometry. A probabilistic framework is established to perform the design optimization considering seismic risk criteria specific to the Chilean region. Quantification of this risk through time-history analysis is considered and the seismic hazard is described by a stochastic ground motion model that is calibrated to offer hazard-compatibility with ground motion prediction equations available for Chile. Two different criteria related to life-cycle performance are utilized in the design optimization. The first one, representing overall direct benefits, is the life-cycle cost of the system, composed of the upfront TLD-FR cost and the anticipated seismic losses over the lifetime of the structure. The second criterion, focusing on the performance of building contents, is the peak acceleration with a specific probability of exceedance over the lifetime of the structure. A multi-objective optimization is therefore established and stochastic simulation is used to estimate all required risk measures, whereas a Kriging metamodel is developed to support an efficient optimization process.