Fuzzy-driven strategy for fully automated modal analysis: Application to the SMART2013 shaking-table test campaign

A crucial step when identifying the modal signature of systems using growing order parametric methods consists in discriminating spurious modes from physical modes. In this paper, a three-stages clustering strategy is presented fuzzy framework for automating selection process context Input/Output and Output-Only identification. The novelty strong point approach lies first stage where, after computation single mode validation indicators, modified c-means procedure developed performing partition. It shown how membership function obtained cluster can be interpreted as new synthetic indicator helps with pole-splitting detection, outlier rejection generally improves final parameters estimation. methodology does not involve any user-specified threshold used produced by consisting fitting model to experimental data type. accelerations measured during SMART2013 shaking-table test campaign are processed data-driven state-space identification algorithms. automated tracking trapezoidal shaped reinforced-concrete specimen turn stochastic combined deterministic-stochastic algorithms, defining latter movement shaking table input. Variations then correlated damage actually observed on comparison between results made estimate interaction whole device.