Abstract Summary
Within the structural health monitoring (SHM) field, consistent research efforts have been invested in developing automatic vibration-based indirect methodologies for inspecting existing heritage conditions. Current trends are mainly focused on output-only automatic operational modal analysis (AOMA), specifically throughout the stochastic subspace identification (SSI) technique among others. In the literature, a widespread workflow is implemented in a four-step solution: choice of the SSI control input parameters, computation of stabilization diagrams, stable poles' alignments detection, and their final clustering. However, the so far proposed solutions have not provided yet complete answers to some challenging and still open questions. For instance, an arbitrarily poor initial choice of the SSI control parameters may jeopardize the entire procedure. Therefore, in the current study, the authors present a novel intelligent-based AOMA framework in a machine learning perspective. Specifically, random-forest-driven Monte Carlo sampling of control parameters represents a promising intelligent way to automatically choose the proper SSI control parameters. Furthermore, the recurrent stable physical poles in the stabilization diagram among the Monte Carlo simulations deliver some special insights about mode shape confidence intervals. A numerical benchmark is herein analyzed illustrating some preliminary results and potentials of the proposed methodology.
