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In recent years, Structural Health Monitoring (SHM) based on Operational Modal Analysis (OMA) and damage detection tools has become a popular non-destructive solution to assess the real-time integrity of any kind of structure. This technique is especially well-suited for the condition-based conservation of historical structures, where minimal invasiveness must be ensured owing to their high cultural and architectural value. Optimal Sensor Placement (OSP) techniques represent a valuable tool for efficiently designing the sensor layout in a SHM system in order to achieve an effective modal identification with a reduced number of sensors and, consequently, an improved cost efficiency. In this light, this paper proposes a design methodology of sensor networks based on OSP techniques suitable for historical structures. To do so, a preliminary extensive OMA campaign is conducted in order to construct a reliable finite element (FE) model by fitting the identified modal properties. Afterwards, an optimal sensor arrangement with a limited number of sensors is obtained by applying different model-based OSP techniques. In order to improve the robustness of the solution, material uncertainties are included in the model and the optimal sensor placement is conducted within a statistical framework. This methodology is presented and evaluated with a case study of a Spanish secular building: the Monastery of San Jerónimo de Buenavista in Seville (Spain). In particular, this paper presents the results of the preliminary ambient vibration test and the modal identification of the monastery, the updating process of the FE model, as well as a critical review of the different OSP techniques within a framework of material parameter uncertainty. The presented analysis demonstrate that OSP techniques based on the rank optimization of the kinetic energy matrix of the structure yield robust sensor layout.

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<p>Using a multi-objective optimization algorithm avoid the use of weighting factors to balance the different residuals in a finite element model updating procedure under the maximum likelihood method. By using this approach, the fittest model is not unique and a set of solutions that form a curve, so-called Pareto optimal front, is obtained. Within this paper, first a review of the state of the art on the criteria used to determine the most adequate model among all the solutions of the Pareto front is presented. Subsequently, a case study of a real footbridge is considered. A finite element model of the footbridge is updated based on its experimental modal parameters. The Non- Dominated Sorting Genetic Algorithm is used to obtain the Pareto front. Since all the solutions in the Pareto front are non-dominated, the selection of the best candidate requires a reasonable criterion. Herein, different procedures to select the best updated model are discussed.</p>

Vibration-based energy harvesting represents a clean power technology that can be of interest for application in civil engineering structures. This study focuses on energy harvesting using cantilever piezoelectric devices excited by operational and ambient bridge vibration. The optimal design and analysis of energy harvesters is usually performed using the mean and standard deviation of a response quantity of interest (i.e. voltage) under broadband Gaussian white noise excitation. In this paper, a novel holistic approach to the problem is proposed through the statistics of the voltage of piezoelectric energy harvesters under real measured bridge vibration base excitation. A new semi-analytic expression of the expected power is developed. The solution is based on the closed-form of the frequency response function between the harvester output voltage and the base excitation, and the experimentally measured spectral density of the latter. A study on the influence of the electromechanical coupling of the problem equations is first conducted. Then, a sensitivity analysis of the piezoelectric energy harvester parameters is performed. The critical analysis is developed through a case study of the measured long-term vibrations of a bowstring-arch highway bridge. Both operational and ambient vibration records are considered in the feasibility study. The results show the potential of the semi-analytic expression to evaluate the harvested power of piezoelectric harvesters under operational structural vibration. This is a promising approach to confidently develop future analyses on the power requirements of wireless sensor networks for SHM.