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September 10, 2025Open Access

Application of Heuristic Optimization Approaches to Evaluate Compaction Parameters

MRMahmudur RahmanKing Fahd University of Petroleum and Minerals

Key Points

The study finds a strong correlation between optimal moisture content and maximum dry density with significant soil parameters.
Using meta-heuristic optimization models, the genetic algorithm showed the highest prediction accuracy for optimal moisture content.
The particle swarm optimization model effectively predicted maximum dry density with solid results.
Employing these optimization approaches may also mitigate environmental impacts from soil extraction and modification.

Abstract

In geotechnical engineering and construction, the optimal moisture content (OMC) and maximum dry density (MDD) is crucial for determining the ideal conditions for soil strength and stability in infrastructure. Traditional laboratory techniques for calculating OMC and MDD are both costly and time-consuming. Machine learning offers a potential alternative for traditional empirical approaches by making it easier to create complex prediction models and algorithms that can improve the accuracy and efficacy of forecasts of compaction parameters. Machine learning-specifically Meta-heuristic optimization (MHO)-approaches are high-level problem-solving strategies that seek optimal or near-optimal solutions to difficult optimization problems, which are frequently non-linear, multi-modal, or non-differentiable. The Genetic Algorithm (GA), Generalized Population-Based Adaptive Search (GPAS), and Particle Swarm Optimization (PSO) are three powerful meta-heuristic optimization algorithms that are commonly employed to solve complex optimization issues. The goal of this project is to develop a framework that uses meta-heuristic optimization techniques to estimate OMC and MDD. Using MHO models, the study shows a substantial correlation between OMC and MDD, respectively, with significant soil factors such as specific gravity, Atterberg limits, and grain size distribution parameters. This study depicts three distinct models for the prediction of OMC and MDD named GA, PSO, and GPAS models. Among the models, the GA model demonstrated the highest accuracy in predicting OMC (R2 = 0.9999, MSE = 0.0001), while the PSO model was most effective for MDD prediction (R2 = 0.9660, MSE = 0.1871). These findings highlight the accuracy and dependability of the GA technique, which presents a viable method for precisely forecasting the MDD and OMC of soil stabilization mixtures in a range of engineering applications. Additionally, it reduces the negative effects that soil extraction and modification have on the environment.

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