196 C. Rainieri et al. and dedicated books are available in the literature about vibration-based SHM [1–3]. The monitoring process consists in the observation of the structure over long periods of time. Appropriate sensors and measurement systems continuously acquire records of relevant physical and mechanical parameters; damage sensitive features are then extracted from the collected data and analyzed to assess the health state of the structure. From a general point of view, damage is defined as any change of the structure that adversely affects its performance [3]. This change can be in the form of stiffness change (for instance, cracking), mass change, connectivity change (for instance, looseness in a bolted joint) or boundary condition change (for instance, bridge scour). An effective SHM system should be able to automatically detect damage at an early stage [1]. Five damage detection levels have been defined [4]: • Level 1: identification of damage existence; • Level 2: localization of damage; • Level 3: identification of the type of damage; • Level 4: quantification of damage severity; • Level 5: prediction of the remaining service life of the structure (prognosis). Modal based damage detection starts by recognizing that the modal parameters depend on physical parameters (mass, stiffness and damping). Assuming that damage yields a change in the physical properties of the structure, this is reflected by a change in the modal properties. Thus, it is theoretically possible to identify damage from the analysis of the variations of the modal parameters, and a number of damage sensitive features have been defined in terms of modal parameters. Damage sensitive features can be defined in terms of natural frequencies and mode shapes. Natural frequency variations provide the easiest way to detect the presence of damage, because they can be accurately estimated even in the presence of a few sensors. However, the information they provide is limited to Level 1 damage detection. Thus, other features have been defined in terms of mode shapes and mode shape curvatures, because mode shapes can provide information also for damage location. However, they are typically estimated with lower accuracy with respect to natural frequencies. In the present paper, the opportunities and limitations of automated output-only modal identification and modal-based damage detection for bridges are briefly reviewed in view of fast assessment of structures in the early earthquake aftershock. Some results of a recent comprehensive research program, funded by the Italian Ministry of Research through the Project PON-FESR 2007–2013 (PON01_02366, STRIT) “Tools and Technologies for the Management of the Transportation Infrastructures” and focused on the assessment and monitoring of as-built and retrofitted RC bridges, are illustrated. The objective of the experimental tests was twofold. The dynamic response of the structure has been experimentally characterized for different support conditions (simply supported, with seismic isolation devices). After this first stage of analysis, an input ground motion has been applied by means of a couple of asynchronous shaking tables [5]. As a result of the seismic input, the structure without seismic isolation devices was damaged. The effect of damage on the modal properties and the possibility to localize damage by means of selected damage features are discussed on the basis of the obtained experimental results. 20.2 Modal-Based SHM and Application to Bridges Several applications of modal-based damage detection to bridges are reported in the literature (see, for instance, [3, 6–10]). Even if a comprehensive review is out of the scope of the paper, attention is herein focused on the application to bridges of continuous monitoring of the modal parameters and modal-based damage detection; in particular, advantages and limitations of these techniques are reviewed in view of fast assessment of structures in the early earthquake aftershock. The recent increase in the number of applications of modal based SHM to bridges takes advantage of the recent development of several algorithms for automated identification [11, 12] and tracking [13] of modal parameters based on Operational Modal Analysis (OMA) methods. Damage detection techniques based on changes of the modal parameters of the monitored structure over time are currently well established [14]. Thus, the continuous monitoring of modal parameters has a large potential in performance and health assessment of bridges in the early earthquake aftershock [1, 15, 16]. However, the effectiveness of modal based damage detection techniques depends on the appropriate selection of damage features and metrics [3]. About the selection of damage features, an ideal one is such that it is sensitive to damage and insensitive to the influence of environmental and operational factors. Unfortunately, one of the main drawbacks of modal based damage detection is the sensitivity of modal property estimates to environmental and operational conditions. These can cause changes in the estimates of the same order of magnitude of those induced by damage. As a consequence, the estimates have to be depurated from the effects of environmental factors in order to effectively detect damage. This issue has been addressed in several papers [10, 17, 18], and a number of techniques for compensation of environmental and operational effects on the estimated modal properties have been proposed. Some require measurements of the environmental and operational factors that influence the
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