Correlations Between the Physical Properties and Consolidation Parameter of West Shore Clay

Heo, Yeol; Hwang, In-Sun; Kang, Chang Soo; Bae, Wooseok

doi:10.14481/jkges.2015.16.4.33

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…In addition, all independent variables were positively correlated with the dependent variable (Cc). The reliability of the simple regressions in Table 6 can be assessed using R 2 , and the results are similar to those of previous studies in similar geographic regions [8,9,27,28]. This was likely due to the analysis using relatively little data from a wide target area.…”

Section: Simple Regression Analysissupporting

confidence: 80%

“…However, standard consolidation tests are expensive and time-consuming. Therefore, many empirical formulas have been developed to calculate the compression index from relatively simple physical test results [6][7][8][9][10]. Many studies have been conducted to predict the compression index by applying various techniques, such as developing a model for predicting the compression index through single and multiple regression analysis using the natural water content, liquid limit, and initial void ratio [8].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, many empirical formulas have been developed to calculate the compression index from relatively simple physical test results [6][7][8][9][10]. Many studies have been conducted to predict the compression index by applying various techniques, such as developing a model for predicting the compression index through single and multiple regression analysis using the natural water content, liquid limit, and initial void ratio [8]. In addition, many studies have also been conducted on developing compression index prediction models based on machine learning algorithms [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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A Study on Developing a Model for Predicting the Compression Index of the South Coast Clay of Korea Using Statistical Analysis and Machine Learning Techniques

Lee,

Kang,

Kim

et al. 2024

Applied Sciences

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As large cities are continually being developed around coastal areas, structural damage due to the consolidation settlement of soft ground is becoming more of a problem. Estimating consolidation settlement requires calculating an accurate compressive index through consolidation tests. However, these tests are time-consuming, and there is a risk of the test results becoming compromised while preparing and testing the specimens. Therefore, predicting the compression index based on the results of relatively simple physical property tests enables more reliable and accurate predictions of consolidation settlement by calculating the compression index at multiple points. In this context, this study collected geotechnical data from the soft ground of Korea’s south coast. The collected data were used to construct a dataset for developing a compression index prediction model, and significant influencing factors were identified through Pearson correlation analysis. Simple and multiple linear regression analysis was performed using these factors to derive regression equations, and compression index prediction models were developed by applying machine learning algorithms. The results of deriving the significance of the influencing factors from the developed compression index prediction model showed that natural water content was the most significant factor in predicting the compression index. By collecting a significant amount of high-quality data and using the compression index prediction model and the model construction process proposed in this study, more accurate predictions of the compressive index will be possible in the future.

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Section: Simple Regression Analysissupporting

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Study on Developing a Model for Predicting the Compression Index of the South Coast Clay of Korea Using Statistical Analysis and Machine Learning Techniques

Lee,

Kang,

Kim

et al. 2024

Applied Sciences

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Explainable Ensemble Learning Approaches for Predicting the Compression Index of Clays

Ge,

Xia,

Shu

et al. 2024

JMSE

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Accurate prediction of the compression index (cc) is essential for geotechnical infrastructure design, especially in clay-rich coastal regions. Traditional methods for determining cc are often time-consuming and inconsistent due to regional variability. This study presents an explainable ensemble learning framework for predicting the cc of clays. Using a comprehensive dataset of 1080 global samples, four key geotechnical input variables—liquid limit (LL), plasticity index (PI), initial void ratio (e0), and natural water content w—were leveraged for accurate cc prediction. Missing data were addressed with K-Nearest Neighbors (KNN) imputation, effectively filling data gaps while preserving the dataset’s distribution characteristics. Ensemble learning techniques, including Random Forest (RF), Gradient Boosting Decision Trees (GBDT), Extreme Gradient Boosting (XGBoost), and a Stacking model, were applied. Among these, the Stacking model demonstrated the highest predictive performance with a Root Mean Squared Error (RMSE) of 0.061, a Mean Absolute Error (MAE) of 0.043, and a Coefficient of Determination (R2) value of 0.848 on the test set. Model interpretability was ensured through SHapley Additive exPlanations (SHAP), with e0 identified as the most influential predictor. The proposed framework significantly improves both prediction accuracy and interpretability, offering a valuable tool to enhance geotechnical design efficiency in coastal and clay-rich environments.

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Analysis of Geotechnical Characteristics of Coastal Clay Soil Found in Incheon and Gwangyang

Lee¹,

Baek²,

Ko³

et al. 2021

J. Korean Soc. Hazard Mitig

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Recently, there has been an increase in the number of projects involving the planning and construction of business complex facilities and land development in coastal regions. However, coastal regions are characterized by very soft clay soil. Consequently, such clay soil needs to be augmented for facilitating land procurement. Furthermore, to realize land improvement, the associated geotechnical characteristics need to be analyzed through laboratory tests by acquiring pristine samples of cohesive soil. According to the results obtained from previous studies, the characteristics of the cohesive soil found in Korea can vary depending on the region and depositional environment. The objective of this study is to examine the representative physical and mechanical characteristics of marine clay found in Namhae coast (Gwangyang) and Seohae coast (Incheon) by comparing and analyzing these characteristics. To this end, land examinations were conducted during planning and construction, and data were gathered from 445 sites in Incheon and 844 sites in Gwangyang for comparing the associated physical and mechanical characteristics. Subsequently, by conducting regression analysis, equations of correlation between liquid limit and natural water content, effective surface load and pre-consolidation load, compression index and liquid limit, and compression index and natural water content were deduced. The obtained results indicate that compared to the soil found in Incheon, the clay fraction, natural water content, liquid limit, plasticity index, liquidity index, initial void ratio, and compression index of the soil found in Gwangyang are higher.

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Correlations Between the Physical Properties and Consolidation Parameter of West Shore Clay

Cited by 3 publications

References 3 publications

A Study on Developing a Model for Predicting the Compression Index of the South Coast Clay of Korea Using Statistical Analysis and Machine Learning Techniques

A Study on Developing a Model for Predicting the Compression Index of the South Coast Clay of Korea Using Statistical Analysis and Machine Learning Techniques

Explainable Ensemble Learning Approaches for Predicting the Compression Index of Clays

Analysis of Geotechnical Characteristics of Coastal Clay Soil Found in Incheon and Gwangyang

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