Integrated Seismic Risk Assessment of Asymmetric High-Rise Structures: Insights from Building Array, Distributed Acoustic Sensing, and Machine Learning-Based Hazard Modeling

To evaluate the high seismic risk and structural health monitoring (SHM) on the Tainan tableland, we equipped a P-alert seismometer array in an academic building of the Department of Resources Engineering, National Cheng Kung University (NCKU). With the R-1 rotational seismometer deployed on the 12th floor, the vertical and horizontal arrays help us to resolve the rotation kinematics in seismic events. The SHM system recorded 65 earthquakes from April 2024 to December 2025, including the 2024 Hualien earthquake and the 2025 Dapu earthquakes. These records enable the systematic analysis of the rotation rate comparison of asymmetric high-rise buildings. Rotation rates were estimated from horizontal accelerations using an array-derivative formulation and were validated against the direct measurements from the R-1 rotational seismometer. In this study, the rotation rates are consistent with two equipment, and the maximum torsion was taking place in the location far away from the elevator due to the building asymmetry. Moreover, the varied, position-dependent rotation rates can be determined by the P-alert horizontal array. To address the site effect of the Tainan metropolitan area, two earthquakes recorded by the NCKU Distributed Acoustic Sensing (NCKUDAS) were used to understand the amplification effect that foundations exert on buildings during earthquakes. To utilize these observations, we propose a machine-learning framework to test the vulnerability of building with the event magnitude. This integrated study provides a robust methodology for torsion-aware SHM and performance-based retrofitting decisions in seismically active regions.