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<p>The present work introduces an accurate computational model by comparing Artificial Bee Colony (ABC), Genetic (GA), Grey Wolf (GWO), Harris Hawks (HHO), Particle Swarm (PSO), and Salp Swarm (SSO) optimized least squares support vector machine (LSSVM) models to estimate the bearing capacity of reinforced concrete (RC) piles (P<sub>U</sub>). Based on a substantial dataset, key geotechnical and structural parameters were identified as both highly influential and multicollinear. The Particle Swarm Optimization-based model demonstrated superior performance (variance accounted for = 95.49, root mean square error = 70.801, correlation = 0.9858, and mean absolute error = 51.521) compared to other hybrid variants, demonstrating enhanced robustness and generalization. Analyses revealed that most alternative models exhibited either underfitting or overfitting, primarily due to problematic multicollinearity among critical input features. This research presents a novel analysis of how feature multicollinearity affects model-fitting behavior in geotechnical predictive modeling.</p>

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Computational Aspects in Estimating the Bearing Capacity of RC Piles Using Hybrid Machine Learning Models

  • 期刊论文

  • 发表日期：

    2026年1月8日

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  • Jitendra Khatti,
  • Pijush Samui,
  • Denise-Penelope N. Kontoni,
  • Panagiotis G. Asteris

摘要

The present work introduces an accurate computational model by comparing Artificial Bee Colony (ABC), Genetic (GA), Grey Wolf (GWO), Harris Hawks (HHO), Particle Swarm (PSO), and Salp Swarm (SSO) optimized least squares support vector machine (LSSVM) models to estimate the bearing capacity of reinforced concrete (RC) piles (PU). Based on a substantial dataset, key geotechnical and structural parameters were identified as both highly influential and multicollinear. The Particle Swarm Optimization-based model demonstrated superior performance (variance accounted for = 95.49, root mean square error = 70.801, correlation = 0.9858, and mean absolute error = 51.521) compared to other hybrid variants, demonstrating enhanced robustness and generalization. Analyses revealed that most alternative models exhibited either underfitting or overfitting, primarily due to problematic multicollinearity among critical input features. This research presents a novel analysis of how feature multicollinearity affects model-fitting behavior in geotechnical predictive modeling.