Ioannis Gidaris

Performance assessment and optimization of fluid viscous dampers through life-cycle cost criteria and comparison to alternative design approaches

The performance assessment and optimal design of fluid viscous dampers through life-cycle cost criteria is discussed in this paper. A probabilistic, simulation-based framework is described for estimating the life-cycle cost and a stochastic search approach is developed to support an efficient optimization under different design scenarios (corresponding to different seismicity characteristics). Earthquake losses are estimated using an assembly-based vulnerability approach utilizing the nonlinear dynamic response of the structure whereas a point source stochastic ground motion model, extended here to address near-fault pulse effects, is adopted to describe the seismic hazard. Stochastic simulation is utilized for estimation of all the necessary probabilistic quantities, and for reducing the computational burden a surrogate modeling methodology is integrated within the framework. Two simplified design approaches are also examined, the first considering the optimization of the stationary response, utilizing statistical linearization to address nonlinear damper characteristics, and the second adopting an equivalent lateral force procedure that defines a targeted damping ratio for the structure. These designs are compared against the optimal life-cycle cost one, whereas a compatible comparison is facilitated by establishing an appropriate connection between the seismic input required for the simplified designs and the probabilistic earthquake hazard model. As an illustrative example, the retrofitting of a three-story reinforced concrete office building with nonlinear dampers is considered.

Multiobjective Design of Supplemental Seismic Protective Devices Utilizing Lifecycle Performance Criteria

AbstractThe cost-effective design of seismic protective devices considering multiple criteria related to their lifecycle performance is examined, focusing on applications to fluid viscous dampers. ...

Natural Hazard Probabilistic Risk Assessment Through Surrogate Modeling

Assessment of risk under natural hazards is associated with a significant computational burden when comprehensive numerical models and simulation-based methodologies are involved. Despite recent advances in computer and computational science that have contributed in reducing this burden and have undoubtedly increased the popularity of simulation-based frameworks for quantifying/estimating risk in such settings, in many instances, such as for real-time risk estimation, this burden is still considered as prohibitive. This chapter discusses the use of kriging surrogate modeling for addressing this challenge. Kriging establishes a computationally inexpensive input/output relationship based on a database of observations obtained through the initial (expensive) simulation model. The up-front cost for obtaining this database is of course high, but once the surrogate model is established, all future evaluations require small computational effort. For illustration, two different applications are considered, involving two different hazards: seismic risk assessment utilizing stochastic ground motion modeling and real-time hurricane risk estimation. Various implementation issues are discussed, such as (a) advantages of kriging over other surrogate models, (b) approaches for obtaining high efficiency when the output under consideration is high dimensional through integration of principal component analysis, and (c) the incorporation of the prediction error associated with the metamodel into the risk assessment.

Design of flow isolation systems through multi-objective criteria for the seismic-risk performance

This paper discusses a probabilistic framework for performance assessment and optimal design of floor isolation systems for the protection of acceleration sensitive contents. A multi-objective formulation is considered for the optimization problem with the two competing objectives corresponding to (i) maximization of the level of protection offered to the sensitive content (acceleration reduction) and (ii) minimization of the demand for appropriate clearance to avoid collision to surrounding objects (floor displacement reduction). Uncertainties are addressed by characterizing these objectives in terms of the associated seismic risk, whereas a surrogate modeling approach is developed to evaluate this risk and support the design optimization. As an illustrative example, the design of a polynomial friction pendulum isolator system is presented. The formulation is demonstrated to efficiently provide design solutions with different performance levels across the considered competing objectives, offering a range of options for selecting the final protection system.

Bayesian Updating of Deteriorating Bridge Infrastructures Through Monitoring Data

The knowledge about the deteriorating characteristics and future operation conditions in civil infrastructure systems is never complete. Any decision-making framework must include a rational approach for quantifying these uncertainties and their bearing on the decision making process, thought the entire life-cycle of operation. A probability logic approach provides a consistent foundation for this, employing probability models to characterize the relative likelihood of the different system properties. Health monitoring data, when available, may be used to update the probabilistic quantification related to these uncertainties as well the future assessment of the condition of the infrastructure system under consideration. This work presents a Bayesian framework for updating the assessment of bridge infrastructure systems through use of monitoring data. Focus is put on deteriorating characteristics for bridges, which can have a significant impact on the reliability of the system. Stochastic simulation techniques, primarily based on Markov Chain Monte Carlo simulation, are proposed for the Bayesian updating and various models classes are examined for the bridge system. The updating of the relative likelihood for each of the models through monitoring data is also considered. An illustrative example is presented that demonstrates the power of this approach for updating the assessment of an aging overpass concrete bridge and guiding maintenance decisions.Copyright