Abstract
Structural health monitoring of civil infrastructures has great practical importance for engineers, owners and stakeholders. Numerous researches have been carried out using long-term monitoring, for instance the Rio-Niterói Bridge in Brazil, the former Z24 Bridge in Switzerland, the Millau Bridge in France, among others. In fact, some structures are monitored 24/7 in order to supply dynamic measurements that can be used for the identification of structural problems such as the presence of cracks, excessive vibration, damage or even to perform a quite extensive structural evaluation concerning its reliability and life cycle. The outputs of such an analysis, commonly entitled modal identification, are the so-called modal parameters, i.e. natural frequencies, damping ratios and mode shapes. Therefore, the development and validation of tools for the automatic identification of modal parameters based on the structural responses during normal operation is fundamental, as the success of subsequent damage detection algorithms depends on the accuracy of the modal parameters estimates. The proposed methodology uses the data driven stochastic subspace identification method (SSI-DATA), which is then complemented by a novel procedure developed for the automatic analysis of the stabilization diagrams provided by the SSI-DATA method. The efficiency of the proposed approach is attested via experimental investigations on a simply supported beam tested in laboratory and on a motorway bridge.
Highlights
For the sake of clarity, one should clearly distinguish modal parameter estimation (MPE) from modal tracking. The former consists in the estimation of modal parameters from a single record of measured data and the latter corresponds to tracking the evolution of the modal parameters of a structure through repeated MPE
This paper focuses on the MPE process, which means that structural responses are evaluated independently
In 2012, Reynders et al [1] published a fully automated approach for the interpretation of a stabilization diagram. It was cleverly based on clustering techniques through three stages: a diagram pre-cleaning by means of a classification of all modes into two categories; a hierarchical clustering of the possible physical ones to group them together; and a final classification of the formed clusters into a physical or spurious condition
Summary
The Re( x ) is the symbol for the real part of x and λi denotes the continuous-time eigenvalue of A These 8 steps summarize the SSI-DATA routine which is, essentially, a fit of a state space model to the temporal output data by means of the geometric projection of the row space of the future measurements on the past measurements. It is necessary to point out that when one works with real structure response data, it is very hard to determine the system order through inspection of singular values like stated in the fourth step. This happens because there are no zeros at all, but an asymptotic smooth variation towards zero (S is full rank). One of the tools for this check is, exactly, the well-known stabilization diagram, where the modes estimates are plotted for various different system orders
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