DYNAMIC IDENTIFICATION OF THE INVERSO PINASCA BRIDGE

This thesis investigates the system identification of the Inverso Pinasca bridge, a structure that has been affected by natural phenomena, including water-induced scour, which have compromised the stability of its piers. The principal objective is to identify the bridge's modal parameters, which are crucial for assessing its structural integrity and identifying requisite interventions. By making use of advanced structural health monitoring (SHM) techniques, the study is concerned with the robustness of machine learning-based automated operational modal analysis (AOMA) algorithms in the presence of both stationary and non-stationary signals. A key challenge inherent to traditional system identification algorithms is their dependence on stationarity assumptions and the absence of explicit guidelines concerning signal duration. This thesis addresses these limitations by evaluating the efficacy of the AOMA algorithm in diverse signal contexts, integrating signal duration as a critical factor for precise modal identification. The analysis contrasts the performance of the algorithm with both stationary and non-stationary segments of a singular signal, offering insights into the algorithm's adaptability. Furthermore, a transmissibility analysis is conducted between the bridge deck and its piers, taking into account the presence of seismic isolators that support the deck on the pier caps. This analysis provides valuable insights into the dynamic interaction between the various components of the bridge, establishing a foundation for future applications such as modal updating and damage quantification due to scour.