Summary The tuned mass‐damper‐inerter (TMDI) is a recently proposed linear passive dynamic vibration absorber for the seismic protection of buildings. It couples the classical tuned mass damper (TMD) with an inerter, a two‐terminal device resisting the relative acceleration of its terminals, in judicial topologies, achieving mass‐amplification and higher‐modes‐damping effects compared to the TMD. This paper considers an optimum TMDI design framework accommodating the above effects while accounting for parametric uncertainty to the host structure properties, modeled as a linear multi degree of freedom system, and to the seismic excitation, modeled as stationary colored noise. The inerter device constant, acting as a TMD mass amplifier, is treated as a design variable, whereas performance variables sensitive to high‐frequency structural response dynamics are used to account for the TMDI influence to the higher structural modes. Reliability criteria are adopted for quantifying the structural performance, expressed through the probability of occurrence of different failure modes related to the trespassing of acceptable thresholds for the adopted performance variables: floor accelerations, interstory drifts, and attached mass displacement. The design objective function is taken as a linear combination of these probabilities following current performance‐based seismic design trends. Analytical and simulation‐based tools are adopted for the efficient estimation of the underlying stochastic integral defining the structural performance under uncertainty. A 10‐story building under stationary Kanai‐Tajimi stochastic excitation is considered to illustrate the design framework for various TMDI topologies and attached mass values. It is shown that the TMDI achieves enhanced structural performance and robustness to building and excitation uncertainties compared to same mass/weight TMDs.

This paper revisits the implementation of surrogate modeling (metamodeling) techniques within seismic risk assessment, for applications that the seismic hazard is described through stochastic ground motion models. Emphasis is placed on how to efficiently address the aleatoric uncertainty in the ground motions, stemming in this case from the stochastic sequence utilized within the excitation model. Previous work has accommodated this uncertainty by approximating the statistics of the engineering demand parameters (EDPs), something that required a large number of replication simulations (for different stochastic sequences) for each training point that was used to inform the metamodel calibration. Using kriging (Gaussian Process regression) as surrogate model, an alternative formulation is discussed here, aiming to minimize the replications for each training point. This is achieved by approximating directly the EDP distribution. It is shown that accommodating heteroscedastic behavior with respect to the aleatoric uncertainty is absolutely critical for achieving an accurate approximation, and two different approaches are presented for establishing this objective. The first approach adopts a stochastic kriging formulation, utilizing a small number of replications for judicially selected inputs, leveraging a secondary surrogate model over the latter inputs to address the heteroscedastic behavior. The second approach uses no replications, establishing a heteroscedastic nugget formulation to accommodate the EDP distribution estimation. A functional relationship is introduced between the nugget and the excitation intensity features to approximate the heteroscedastic behavior. This relationship is explicitly optimized during the metamodel calibration.

SUMMARYAn efficient computational framework is presented for seismic risk assessment within a modeling approach that utilizes stochastic ground motion models to describe the seismic hazard. The framework is based on the use of a kriging surrogate model (metamodel) to provide an approximate relationship between the structural response and the structural and ground motion parameters that are considered as uncertain. The stochastic character of the excitation is addressed by assuming that under the influence of the white noise (used within the ground motion model) the response follows a lognormal distribution. Once the surrogate model is established, a task that involves the formulation of an initial database to inform the metamodel development, it is then directly used for all response evaluations required to estimate seismic risk. The model prediction error stemming from the metamodel is directly incorporated within the seismic risk quantification and assessment, whereas an adaptive approach is developed to refine the database that informs the metamodel development. The ability to efficiently obtain derivative information through the kriging metamodel and its utility for various tasks within the probabilistic seismic risk assessment is also discussed. As an illustrative example, the assessment of seismic risk for a benchmark four-story concrete office building is presented. The potential that ground motions include near-fault characteristics is explicitly addressed within the context of this example. The implementation of the framework for the same structure equipped with fluid viscous dampers is also demonstrated.

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SummaryStochastic ground motion models produce synthetic time-histories by modulating a white noise sequence through functions that address spectral and temporal properties of the excitation. The resultant ground motions can be then used in simulation-based seismic risk assessment applications. This is established by relating the parameters of the aforementioned functions to earthquake and site characteristics through predictive relationships. An important concern related to the use of these models is the fact that through current approaches in selecting these predictive relationships, compatibility to the seismic hazard is not guaranteed. This work offers a computationally efficient framework for the modification of stochastic ground motion models to match target intensity measures (IMs) for a specific site and structure of interest. This is set as an optimization problem with a dual objective. The first objective minimizes the discrepancy between the target IMs and the predictions established through the stochastic ground motion model for a chosen earthquake scenario. The second objective constraints the deviation from the model characteristics suggested by existing predictive relationships, guaranteeing that the resultant ground motions not only match the target IMs but are also compatible with regional trends. A framework leveraging kriging surrogate modeling is formulated for performing the resultant multi-objective optimization, and different computational aspects related to this optimization are discussed in detail. The illustrative implementation shows that the proposed framework can provide ground motions with high compatibility to target IMs with small only deviation from existing predictive relationships and discusses approaches for selecting a final compromise between these two competing objectives. | INTRODUCTIONThe growing interest in performance-based earthquake engineering 1,2 and in simulation-based, seismic risk assessment approaches 3,4 has increased in the past decades the relevance of ground motion modeling techniques. These techniques describe the entire time-series of seismic excitations, providing a characterization appropriate for dynamic time-history analysis. UndoubtedlyThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Taflanidis AA, Giaralis A and Patsialis D (2019) Multi-objective optimal design of inerter-based vibration absorbers for earthquake protection of multi-storey building structures.