Structural capacity deterioration is among the main causes of increasing failure probabilities of structural systems, thus maintenance interventions are a crucial task for their rational management. Several probabilistic approaches have been proposed during the last decades for the determination of cost-effective maintenance strategies based on selected performance indicators. However, benefits and drawbacks of each performance indicator with respect to the others should be further analyzed. The objective of this paper is to investigate probabilistic approaches based on the annual reliability index, annual risk, and lifetime distributions for life-cycle maintenance of structural systems. Maintenance schedules are obtained for representative series, parallel, and series-parallel systems considering total restoration of component resistances whenever a prescribed threshold, based on a selected performance indicator, is reached. Effects related to different structural configurations and correlation among failure modes are investigated. The superstructure of an existing bridge is used to illustrate the presented approaches.

Citation: . Life-cycle maintenance of deteriorating structures by multi-objective optimization involving reliability, risk, availability, hazard and cost. Structural Safety, 48, Additional Information:• This is the author's version of a work that was accepted for publication availability and hazard functions) are used in conjunction with total maintenance cost for evaluating Pareto fronts associated with optimal maintenance schedules of deteriorating structures. Essential maintenance actions are considered and optimization is performed by using genetic algorithms. The approach is illustrated on an existing deteriorating bridge superstructure.

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Nonlinear Energy Sinks (NESs) have recently received increasing attention from researchers because of their capability to passively absorb a significant amount of energy over a wide range of frequencies. In most studies, the dynamic response of the main structure coupled with one or more NESs is analysed for impulsive loading. In this paper, the performance of the NES attached to a Single Degree of Freedom (SDOF) system, under random Gaussian white noise base excitations, is investigated through several numerical simulations. In order to determine the optimal configuration for the device, four different objective functions are considered. Sensitivity analyses with respect to the intensity of the random loads, the mass ratio and the main parameters of the primary structure are presented. The authors propose an approximate design approach based on the use of the Statistical Linearization Technique, and an accurate empirical formulation linking the NES optimal parameters to the characteristic of the main structure and the random excitation. Numerical results are validated by Monte Carlo simulations. Finally, a numerical application for a 2-DOFs system equipped with a NES has been presented in order to investigate the applicability of the proposed empirical approach for Multi Degrees of Freedom structures.

Design codes typically define the seismic action in terms of pseudo-acceleration response spectra, encouraging the use of the modal superposition method for the evaluation of the structural response. For linear structural systems, provided that the ground shaking has been appropriately modelled, the full characterization of the response processes of interest can be achieved with the application of the random vibration theory. An analytical model for the power spectral density (PSD) functions consistent with seismic response spectra has been recently proposed. In this paper, taking advantage of this novel PSD model, closed-form approximate expressions of the spectral moments of the structural response are derived and numerically validated for single-and multi-degree of freedom systems. The proposed formulation is applied to the case of base isolated multi-story buildings, aimed at overcoming the difficulties associated with the non-classical nature of their damping. The paper shows how the proposed approach can be used for an accurate and computationally efficient evaluation of the probabilistic distribution of the structural response maxima.