A Hybrid Metaheuristic-Optimized Machine Learning Framework for Predicting Pile Capacity Using High Strain Dynamic Testing Data

Abstract

The precise estimation of spun pile efficiency parameters through High Strain Dynamic Testing (HSDT) is critical for structural safety and foundational integrity; however, it is frequently constrained by significant economic, temporal, and logistical limitations that restrict physical testing to a minor fraction of installed piles. To overcome these prohibitive barriers, this study proposes a high-fidelity hybrid predictive framework that synergizes advanced machine learning architectures with nature-inspired metaheuristic optimizers. Three distinct predictive models: Adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Regression (SVR), and Regression Tree (RT) were formulated. To resolve the inherent limitations of suboptimal convergence and hyperparameter trapping in standard configurations, the structural parameters of these models were optimized using the Sparrow Search Algorithm (SSA), Whale Optimization Algorithm (WOA), and Manta Ray Foraging Optimization (MRFO). Utilizing a high-quality, rigorously validated dataset of 150 Pile Driving Analyzer (PDA) records, the models were trained to forecast the maximum case method capacity (RMX) and maximum compressive force (FMX). To ensure model robustness and completely eliminate small-sample bias, a rigorous 10-fold bootstrapping cross-validation protocol was implemented, alongside a SHapley Additive exPlanations (SHAP) sensitivity analysis and Wilcoxon signed-rank testing for non-parametric statistical validation. The comparative benchmarking confirms the absolute superiority of the SSA-ANFIS framework, which achieved unprecedented predictive precision with a perfect correlation (R2=1.000) and minimal error profiles for both RMX (RMSE=0.123) and FMX (RMSE=0.506), statistically outperforming baseline models such as eXtreme Gradient Boosting (XGBoost) and the empirical Danish Driving Formula. For practical field deployment, the optimized architecture was embedded into a MATLAB-based Intelligent Geotechnical Decision Support System (IGDSS) and subjected to independent validation on a geologically distinct site, verifying its exceptional generalization capabilities. The integration of swarm intelligence with neuro-fuzzy logic presents a highly reliable, physically explainable, and cost-effective alternative to ubiquitous physical testing, advancing the paradigm of digital geotechnical engineering.