Zafer Yilmaz

This study examines the potential significance of the Initial Sampling Instant Error (ISIE), a commonly overlooked source of measurement error, and its implications on seismic shaking intensity estimates. Advances in seismic instrumentation have greatly improved our understanding of earthquakes over the past five decades, enabling the development of safer structural designs and contributing to life-saving engineering practices. Among these advances, Ground Motion Prediction Equations (GMPEs) stand out as a major tool for estimating ground motion intensity measures, such as Peak Ground Acceleration (PGA), using general earthquake parameters related to the source, propagation path, and local site conditions. The accuracy of GMPEs depends critically on the recorded PGA data from prior earthquakes, which are subject to inherent measurement errors. These errors are due to limitations in sampling frequency, aliasing, quantization, sensor noise, clock jitter, and the timing of the initial sample. Although many of these errors have been extensively investigated and mitigated, ISIE is often dismissed as negligible. ISIE arises from discrepancies between the digitized PGA value and the theoretical continuous-time PGA obtained via sinc interpolation over the same frequency range. Despite its random nature, ISIE is influenced by earthquake characteristics (source, path, site) and the sampling rate. In this study, we analyze an extensive dataset of ground motion records to quantify ISIE and examine its dependence on these parameters. A preliminary analysis of 9,934 records from the PEER NGA West2 database indicates that ISIE can lead to an underestimation of PGA [1]. To better understand the factors influencing this error, we use machine learning methods to model each parameter's contribution.

The interest in damage identification methods has increased significantly in recent years due to the rising demand for structural health monitoring of structures. This study presents an enhanced version and validation of a recently introduced method for damage detection, localization and quantifying damage using vibration data. The method is validated through a building application, a scaled steel frame model built in the laboratory. The validation is carried out using eight different damage scenarios in numerical and experimental studies. These studies are based on finite element analysis and ambient vibration tests. A newly introduced filtering approach that utilizes MAC rejection levels in Modal Participation Ratio derivation is provided to replace the user-controlled bandpass filter to obtain more reliable vibration data in experimental investigations. The results showed that the proposed procedure is more capable of correctly detecting, localizing and quantifying damage to a building, considering the real-life conditions.