This study goes into the complex area of predicting soil liquefaction potential, a crucial issue in geotechnical engineering.
The work presents a novel viewpoint on forecasting soil liquefaction occurrences by combining traditional geotechnical concepts with cutting-edge machine-learning approaches. The research maintains authenticity and credibility in its conclusions by using real-world datasets from actual liquefaction incidents. While Pearson and Spearman correlation analyses clarify the complex interrelationships between the variables, a fundamental descriptive analysis offers a thorough statistical summary.
Three machine learning models—logistic regression, random forest, and SVM—that make use of cutting-edge computational methods constitute the basis of the analytical efforts.
Twenty different parameter sets were examined, and among them, sets 4, 12, 2, 8, 16, and 19 stood out as being particularly interesting.
Set 4 achieved an accuracy of 86 percent. According to the study’s definition of the “Gray zone”, a zone of caution should be followed when calculated probabilities are close to the 0.5 decision threshold.
The model’s reliability and the significance of several geotechnical elements are further highlighted through sensitivity analysis.
The research highlights complementary approaches’ usefulness in merging traditional geotechnical evaluations with cutting-edge data analytics, as recommended by literature and regulations.
To sum up, this study combines conventional geotechnical concepts with cutting-edge analytical methods, providing essential insights for upcoming geotechnical projects in the face of seismic difficulties.
