Risk-informed optimization of the tuned mass-damper-inerter (TMDI) for the seismic protection of multi-storey building structures. Engineering Structures, 177, pp. 836-850.

No abstract

The goal of this work is to describe a framework to propagate uncertainties in piezoelectric energy harvesters (PEHs). These uncertainties are related to the incomplete knowledge of the model parameters. The framework presented could be employed to conduct prior robust stochastic predictions. The prior analysis assumes a known probability density function for the uncertain variables and propagates the uncertainties to the output voltage. The framework is particularized to evaluate the behavior of the frequency response functions (FRFs) in PEHs, while its implementation is illustrated by the use of different unimorph and bimorph PEHs subjected to different scenarios: free of uncertainties, common uncertainties, and uncertainties as a product of imperfect clamping. The common variability associated with the PEH parameters are tabulated and reported. A global sensitivity analysis is conducted to identify the Sobol indices. Results indicate that the elastic modulus, density, and thickness of the piezoelectric layer are the most relevant parameters of the output variability. The importance of including the model parameter uncertainties in the estimation of the FRFs is revealed. In this sense, the present framework constitutes a powerful tool in the robust design and prediction of PEH performance.

Sandwich structures are subjected to imperfect bonding or debonding caused by defects during the manufacturing process, by fatigue, or by impact loads.In this context, their safety and functionality can be improved with the implementation of vibration-based structural damage assessment methodologies.These methodologies involve the computation of second or higher order displacement derivatives, which are often obtained using the central difference method. Nevertheless, this method propagates and amplifies the measurement errors and noise. Therefore, a Gaussian process (GP) regression model to build smoothed (noise-free) curvature mode shapes from noisy experimental mode shape displacements is presented in this paper. The proposed baseline-free debonding assessment approach combines the gapped smoothing (GS) method, curvature mode shapes estimated using a GP regression, and the valleyemphasis method to automatically find damaged regions. Experimental results indicate that our approach performs better than the conventional GS method in the presence of experimental noise.