Akademik Veri Yönetim Sistemi
STRUCTURAL CONTROL & HEALTH MONITORING, sa.1, 2025 (SCI-Expanded)
The ability to conduct automated operational modal analysis is essential for enabling real-time structural health monitoring without human intervention. Such automation remains a significant challenge due to the complexity of processing large datasets and the necessity of setting multiple user-defined thresholds. This study introduces a novel methodology for automated modal identification that leverages the enhanced frequency domain decomposition method. The key innovation of the proposed approach is the concept of the modal domain range, a parameter calculated for each frequency of the structure to distinguish physical modes from noise and false modes. The modal domain range is derived using the correlation of mode shapes, assessed through the modal assurance criterion method. High values within this range indicate dominant structural frequencies, enabling the autonomous identification of the structure's dynamic characteristics. To validate the proposed methodology, experimental data from the Z24 Bridge, a prestressed concrete structure, were analyzed. The dynamic parameters, including natural frequencies and mode shapes, were identified using the developed approach and compared with reference data from the literature. The results demonstrated that the methodology achieves remarkable precision. Moreover, the proposed method effectively reduces the impact of noise and environmental variations through a systematic filtering and grouping process. The findings highlight the robustness and adaptability of the methodology, demonstrating its capability for automated and accurate identification of modal parameters in civil engineering structures.