Refined FDD modal dynamic identification from earthquake responses with Soil-Structure Interaction

Non-parametric structural system identification via a . refined Frequency Domain Decomposition (rFDD) algorithm is developed here to evaluate current modal properties of civil engineering buildings subjected to earthquake excitation. Innovatively, the algorithm is expanded to operate under Soil-Structure Interaction (SSI) conditions, in order to deal with both flexible- and fixed-base conditions. At first, the modal dynamic identification technique is explored analytically, by dealing with a variable stiffness and damping foundation system. The standard fixed-base condition (no SSI) may simulate well the response to ambient loadings or weak seismic excitations; rather, a flexible-base model shall better deal with more realistic earthquake-induced structural responses embedding SSI effects. The developed analyses are performed on earthquake-induced synthetic response signals, which are computed from a benchmark linear frame structure under several earthquake excitations and at variable foundation properties. The rFDD expansion originally operates within an OMAX (Operational Modal Analysis with eXogenous input) environment on the base-excited buildings. The OMAX condition comes from the adoption of base excitation records too, which are employed as exogenous input. This aims at detecting also the underlying fixed-base conditions and to quantify the amount of SSI to which the building under analysis is subjected to. The present identification method demonstrates its full effectiveness in detecting both flexible- and fixed-base modal parameters identified based on earthquake excitation, also in the presence of close modes and heavy damping (in terms of identification challenge). This work proves a necessary condition for the effectiveness of the present rFDD-OMAX algorithm as a robust method for inspecting current flexible- and fixed-base strong ground motion modal parameters. This shall help in identifying possible variations of structural features and SSI effects along experienced seismic histories, thus providing an effective tool towards potential Earthquake Engineering and Structural Health Monitoring purposes.