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The design of complex structures should be based on advanced approaches able to take into account the behavior of the constructions during their entire life-cycle. Moreover, an effective design method should consider that the modern constructions are usually complex systems, characterized by strong interactions among the single components and with the design environment. A modern approach, capable of adequately considering these issues, is the so-called performance-based design (PBD). In order to profitably apply this design philosophy, an effective framework for the evaluation of the overall quality of the structure is needed; for this purpose, the concept of dependability can be effectively applied. In this context, structural health monitoring (SHM) assumes the essential role to improve the knowledge on the structural system and to allow reliable evaluations of the structural safety in operational conditions. SHM should be planned at the design phase and should be performed during the entire life-cycle of the structure. In order to deal with the large quantity of data coming from the continuous monitoring various processing techniques exist. In this work different approaches are discussed and in the last part two of them are applied on the same dataset. It is interesting to notice that, in addition to this first level of knowledge, structural health monitoring allows obtaining a further more general contribution to the design knowledge of the whole sector of structural engineering. Consequently, SHM leads to two levels of design knowledge gain: locally, on the specific structure, and globally, on the general class of similar structures.
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