Advanced Hybrid Computational–Statistical Approach for Predictive Analysis and Optimization of Deformation Indicator Behavior under Complex Loading Conditions

Abstract

The accurate prediction and optimization of deformation indicators in geotechnical and structural systems under complex loading conditions remain a critical challenge in modern computational mechanics. This study proposes an advanced hybrid computational–statistical framework that integrates finite element modeling, probabilistic analysis, and machine learning techniques for enhanced predictive performance. The research synthesizes deterministic numerical simulation methods with data-driven intelligence to address uncertainties in material behavior, loading variability, and soil–structure interaction effects. The framework leverages finite element analysis as implemented in PLAXIS-based environments alongside artificial neural network (ANN) architectures to capture nonlinear deformation patterns and improve generalization capability under heterogeneous geotechnical conditions.
The methodology builds upon established approaches in geotechnical variability modeling and probabilistic risk analysis, incorporating statistical learning techniques to enhance deformation prediction accuracy. Special emphasis is placed on resilient modulus behavior and stress–strain evolution in granular soil systems under dynamic and static loading scenarios. Comparative evaluation demonstrates that hybrid models outperform standalone numerical or statistical approaches in predictive stability and robustness.
The findings highlight the importance of integrating physics-based and data-driven models for reliable deformation forecasting. Furthermore, the study identifies key limitations in conventional ANN-based geotechnical prediction frameworks and proposes optimization strategies for improved convergence and reduced uncertainty propagation. The results provide significant implications for infrastructure design, geotechnical risk assessment, and intelligent modeling systems in engineering practice.