Data-driven clustering and dimensionality reduction for soil amplification analysis
Acta Geophysica, cilt.74, sa.4, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 74 Sayı: 4
- Basım Tarihi: 2026
- Doi Numarası: 10.1007/s11600-026-01962-w
- Dergi Adı: Acta Geophysica
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Compendex, Geobase, INSPEC, Academic Search Ultimate (EBSCO), Natural Science Collection (ProQuest), Earth, Atmospheric, & Aquatic Science Collection (ProQuest), Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
- Anahtar Kelimeler: Soil amplification, Clustering analysis, Principal component analysis-PCA, Principal component equation, Eigen decomposition
- Uşak Üniversitesi Adresli: Evet
Özet
This study investigates the key factors influencing soil amplification for ground motion across 8400 ground motion records for 100 different soil profiles. To achieve this, correlations of 21 different parameters related to the ground motion records with respect to amplification levels were analyzed. Next, clustering analysis was conducted, segmenting the data into three clusters. Additionally, principal component analysis (PCA) is applied to assess and more deeply investigate the data and clustering outcomes. The suitability of the dataset for factor analysis was confirmed using the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy. Subsequently, PCA revealed that the extracted principal components account for 83% of the total variance in the dataset. The findings showed that the most effective parameter in soil amplification was the input Peak Ground Acceleration (PGA) value highlighting the significance of intensity. However, intensity alone is not adequate to explain the entire separation among clusters, and frequency content is also required as a descriptive indicator. Among the parameters reflecting the frequency content of the acceleration record, the most decisive one for the clusters was determined to be average spectral period (TM1). These findings may provide valuable insights for future research on soil amplification.