A modified real-coded genetic algorithm to identify the parameters of large structural systems subject to the dynamic loads is presented in this article. The proposed algorithm utilizes several subpopulations and a migration operator with a ring topology is periodically performed to allow the interaction between them. For each subpopulation, a specialized medley of recent genetic operators (crossover and mutation) has been adopted and is briefly discussed. The final algorithm includes a novel operator based on the auto-adaptive asexual reproduction of the best individual in the current subpopulation. This latter is introduced to avoid a long stagnation at the start of the evolutionary process due to insufficient exploration as well as to attempt an improved local exploration around the current best solution at the end of the search. Moreover, a search space reduction technique is performed to improve, both convergence speed and final accuracy, allowing a genetic-based search within a reduced region of the initial feasible domain. This numerical technique has been used to identify two shear-type mechanical systems with 10 and 30 degrees-of-freedom, assuming as unknown parameters the mass, the stiffness, and the damping coefficients. The identification will be conducted starting from some noisy acceleration signals to verify, both the computational effectiveness and the accuracy of the proposed optimizer in presence of high noise-to-signal ratio. A critical and detailed analysis of the results is presented to investigate the inner work of the optimizer. Finally, its performances are examined and compared to the most recent results documented in the current literature to demonstrate the numerical competitiveness of the proposed strategy

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Wireless monitoring could greatly impact the fields of structural health assessment and infrastructure asset management. A common problem to be tackled in wireless networks is the electric power supply, which is typically provided by batteries replaced periodically. A promising remedy for this issue would be to harvest ambient energy. Within this framework, the present paper proposes to harvest ambient-induced vibrations of bridge structures using a new class of piezoelectric textiles. The considered case study is an existing cable-stayed bridge located in Italy along a high-speed road that connects Rome and Naples, for which a recent monitoring campaign has allowed to record the dynamic responses of deck and cables. Vibration measurements have been first elaborated to provide a comprehensive dynamic assessment of this infrastructure. In order to enhance the electric energy that can be converted from ambient vibrations, the considered energy harvester exploits a power generator built using arrays of electrospun piezoelectric nanofibers. A finite element analysis is performed to demonstrate that such power generator is able to provide higher energy levels from recorded dynamic loading time histories than a standard piezoelectric energy harvester. Its feasibility for bridge health monitoring applications is finally discussed.