Şahin G., Koçkar M. K.
ENVIRONMENTAL EARTH SCIENCES, cilt.85, sa.9, ss.1-25, 2026 (SCI-Expanded, Scopus)
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Yayın Türü:
Makale / Tam Makale
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Cilt numarası:
85
Sayı:
9
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Basım Tarihi:
2026
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Doi Numarası:
10.1007/s12665-026-12956-8
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Dergi Adı:
ENVIRONMENTAL EARTH SCIENCES
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Derginin Tarandığı İndeksler:
Scopus, Science Citation Index Expanded (SCI-EXPANDED), IBZ Online, BIOSIS, Compendex, Environment Index, Geobase, INSPEC
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Sayfa Sayıları:
ss.1-25
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Hacettepe Üniversitesi Adresli:
Evet
Özet
Abstract
Ankara Clay is characterized by overconsolidated, highly swelling, active, medium to high plastic and stiff consistency properties. Despite a wide array of research about the Ankara Clay available, a comprehensive and systematic evaluation of the soil properties, especially in terms of consolidation conditions through depth, is limited in the literature. In this context, a large data set of 5,500 borehole samples was collected across the Ankara Basin, and the variations of 20 different soil parameters from the surface up to 25–30 m in-depth were statistically determined. The correlations between soil parameters were investigated, and the predictive equations were developed for the preconsolidation pressure (
P
c
), compression index (
c
c
), swelling index (
c
s
), and volumetric compression coefficient (
m
v
). Investigations also revealed that the normally/lightly overconsolidated (NC-LOC) and moderately/highly overconsolidated (MOC-HOC) samples were frequently observed in specific strata based on depth. Accordingly, a detailed examination was carried out to investigate the influence of consolidation on the variability of the soil parameters. T-tests were performed between NC-LOC/MOC-HOC samples, and a significant difference (
p
< 0.05) in the mean values of 6 distinct soil parameters, particularly in the undrained shear strength (
C
u
) parameter, was identified. Correspondingly, four novel equations were proposed to predict the
C
u
under normally-consolidated and overconsolidated conditions. Furthermore, the accuracy of
C
u
predictions was validated using two alternative datasets. The results demonstrated that considering the consolidation condition as a criterion notably improved (%35–40) the
C
u
predictions.