Statistical Analysis of Soil Parameters Affecting the Bearing Capacity and Settlement Behaviour of Gravel Soils

Understanding the geotechnical behavior of soils is fundamental for the safe design and performance of engineering structures. This study investigates the bearing capacity and settlement behavior of gravel soils using extensive field and laboratory data collected from 27 boreholes in Düzce, northwest Türkiye. Key soil parameters, including excavation depth, groundwater levels, unit weight, water content, particle size distribution, internal friction angles, and cohesion coefficients, were analyzed. Correlation analysis revealed a significant relationship between bearing capacity and the No. 200 sieve value, while relationships with other parameters were less pronounced. Predictive models for bearing capacity and settlement were developed using multiple linear regression, quadratic, and cubic models. The cubic model demonstrated the highest accuracy, predicting bearing capacity with an R2 of 0.79 and settlement with an R2 of 0.904. These results underscore the potential of advanced statistical models to reliably estimate geotechnical properties based on measurable soil parameters. The findings emphasize the importance of combining field and laboratory analyses with robust statistical approaches to accurately predict soil behavior. This methodology provides a foundation for improving geotechnical design practices and may be extended to other soil types and regions, thereby enhancing the reliability and efficiency of soil parameter estimation for engineering applications.

Authors

Institutions

Publication Details

Journal
Applied Sciences
Published
2025-05-09
DOI
https://doi.org/10.3390/app15105271
Citations
1
Primary Topic
Dam Engineering and Safety
Type
article
Field-Weighted Citation Impact
0.95
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Statistical Analysis of Soil Parameters Affecting the Bearing Capacity and Settlement Behaviour of Gravel Soils

Ercan Özgan, Ayşe Bengü Sünbül Güner
1 citations
Applied Sciences
Dam Engineering and Safety
0.95
article

Statistical Analysis of Soil Parameters Affecting the Bearing Capacity and Settlement Behaviour of Gravel Soils

Ercan Özgan, Ayşe Bengü Sünbül Güner
article en
1 citations

Abstract

Understanding the geotechnical behavior of soils is fundamental for the safe design and performance of engineering structures. This study investigates the bearing capacity and settlement behavior of gravel soils using extensive field and laboratory data collected from 27 boreholes in Düzce, northwest Türkiye. Key soil parameters, including excavation depth, groundwater levels, unit weight, water content, particle size distribution, internal friction angles, and cohesion coefficients, were analyzed. Correlation analysis revealed a significant relationship between bearing capacity and the No. 200 sieve value, while relationships with other parameters were less pronounced. Predictive models for bearing capacity and settlement were developed using multiple linear regression, quadratic, and cubic models. The cubic model demonstrated the highest accuracy, predicting bearing capacity with an R2 of 0.79 and settlement with an R2 of 0.904. These results underscore the potential of advanced statistical models to reliably estimate geotechnical properties based on measurable soil parameters. The findings emphasize the importance of combining field and laboratory analyses with robust statistical approaches to accurately predict soil behavior. This methodology provides a foundation for improving geotechnical design practices and may be extended to other soil types and regions, thereby enhancing the reliability and efficiency of soil parameter estimation for engineering applications.

Applied SciencesVol. 15(10)
Düzce Üniversitesi (TR)
Openalex Percentile: Top 26%
Dam Engineering and Safety
0.95
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.