Prediction of In-Situ Properties of Coastal Soils Using GWO-XGBoost Model

Abstract

In coastal port infrastructure, accurate prediction of soil profiles and Standard Penetration Test (SPT) N-values at intermediate borehole locations is critical for safe, economical and resilient foundation design, as across all three dimensions subsoil conditions can vary significantly over short distances since soil is heterogeneous, anisotropic, and unpredictable material. In order to predict in-situ properties at intermediate points, the application of Machine Learning models is necessary which would save time and cost required during the preliminary design and detailed planning phases. In order to maximize predictive accuracy and to navigate complex hyperparameter search spaces, a metaheuristic - optimized approach like the Grey Wolf Optimizer (GWO) - eXtreme Gradient Boosting (XGBoost) hybrid model was used, which is superior to classical interpolation techniques and conventional Machine Learning models such as Random Forest and XGBoost which suffer from limited generalization on sparse geotechnical datasets and suboptimal hyperparameter selection. A dataset of 385 borehole records from 72 geotechnically investigated locations, spanning depths of 0.5 m to 87 m of three major deep-sea port development sites, namely Machilipatnam, Ramayapatnam, and Durgarajpatnam, Andhra Pradesh, India, was compiled and processed using systematic data cleaning, geotechnical imputation, spatial feature engineering, and three normalization strategies Z-Score Standardization, Min-Max Scaling, and Robust Scaling in order to predict continuous SPT N-values and categorical soil profiles simultaneously at unsampled locations. The GWO algorithm optimized XGBoost hyperparameters including learning rate, maximum depth, estimator count, and L1/L2 regularization coefficients. The classical interpolation methods failed critically, with Inverse Distance Weighting (IDW) yielding R² = 0.1005 and Radial Basis Function (RBF) producing negative R² values. Optimized XGBoost improved performance to RMSE = 3.7072 and R² = 0.9315 and Random Forest achieved 87.32% soil classification accuracy and R² = 0.7601. The GWO–XGBoost model with Min-Max scaling confirmed strong generalization, attaining 100% primary soil type classification accuracy and R² = 0.9384, with a robust cross-validation score of 0.9095 ± 0.0195. The GWO–XGBoost framework offers a cost-effective tool with high-accuracy, for detailed subsurface characterization, which align with UN Sustainable Development Goals SDG 9 (Industry, Innovation and Infrastructure) focussing on target 9.1 (Resilient Infrastructure) as prediction would help in preventing failures due to complex environmental conditions and for designing resilient coastal infrastructure. Under SDG 9, target 9.4 (stainable Industrialization/Innovation) is addressed as Grey Wolf Optimization + XGBoost is a data-driven, innovative approach that improves sustainability and engineering efficiency compared to traditional field testing which is carbon-intensive.