Sürdürülebilir Kalkınma Amaçları
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- MakaleAçık Erişim2026Scopus SJRQ2Machine Learning-Based Material Thickness and Type Classification via Scattering Parameters
Engin, Mustafa Alptekin; Cakir, Mehmet
The precise determination of material type and thickness is of major significance in non-destructive testing, quality assurance, and materials science, as it influences the functionality, reliability, and performance of materials in engineering applications. This study proposes a methodology for the classification of material thickness and type through the analysis of scattering parameters within the 8–12 GHz frequency range. A database was created, encompassing real, imaginary, and dB values of reflection and transmission parameters for nine real-world materials with thicknesses ranging from 1 to 10 mm. This study addressed two main classification tasks, namely material thickness and material type. A variety of training–testing splits were employed in conjunction with 10-fold cross-validation to facilitate a comparison of classifier performance. In the material type classification, incorporating multiple thickness levels of each material enabled the model to distinguish materials with similar reflection characteristics more accurately, thereby enhancing discrimination performance. The results showed that Fine KNN consistently achieved 100% accuracy in thickness classification, while Quadratic SVM achieved 100% accuracy in material type classification, even when utilising only three thickness levels. Furthermore, the 90% training–10% testing split yielded the highest performance in thickness classification, whereas the optimal data split for material type classification differed across classifiers. Overall, this study demonstrates that the combination of scattering parameters and machine learning serves as a reliable and efficient approach for non-destructive material characterization.
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İncele - MakaleYalnız künye2025Scopus SJRQ1Optimized model updating and site-specific seismic assessment of a historical masonry bridge
Tayfur, Bilal
This study comprehensively evaluates an 800-year-old masonry bridge using advanced non-destructive testing techniques, an innovative model update process, and site-specific ground motion analysis. Employing Operational Modal Analysis, Ultrasonic Pulse Velocity, and Schmidt Hammer tests, the research meticulously characterized the structural dynamic properties. The preliminary finite element model was rigorously refined utilizing the Tabu Search optimization algorithm by minimizing discrepancies between numerical and experimental modal parameters. Site-specific earthquake records, carefully selected and prepared in accordance with the Turkish Building Earthquake Code, were applied to conduct detailed time-history analyses across 11 distinct ground motion records. Despite experiencing stresses approaching critical thresholds, the bridge demonstrated remarkable structural integrity. While the findings indicate the bridge’s structural soundness under current seismic hazard analysis criteria, the results notably emphasize that the frequency content similarities between the structure and ground motion are more critical for seismic performance assessment than peak ground acceleration (PGA) values, underscoring the importance of continuous monitoring given the region’s complex tectonic characteristics.
AtıfScopus5WoS3OpenAlex4İncele