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Related Experiment Video

Updated: Jul 10, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Metaheuristic optimized hybrid machine learning framework for predicting soil compaction parameters.

Bayram Ateş1, Jun-Jiat Tiang2, Mohammad Azim Eirgash1

  • 1Department of Civil Engineering, Karadeniz Technical University, Trabzon, 61080, Turkey.

Scientific Reports
|July 8, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Rao-1 optimized hybrid model for predicting soil maximum dry density (MDD) and optimum moisture content (OMC). The enhanced framework significantly improves prediction accuracy and reduces errors in geotechnical engineering applications.

Keywords:
Machine learningMaximum dry densityOptimum moisture contentRao-1 algorithmSHAP analysisSoil compaction parametersStatistical analysis

Related Experiment Videos

Last Updated: Jul 10, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Area of Science:

  • Geotechnical Engineering
  • Data-driven Modeling
  • Artificial Intelligence in Civil Engineering

Background:

  • Accurate prediction of soil maximum dry density (MDD) and optimum moisture content (OMC) is crucial for effective compaction control and earthwork design.
  • Traditional laboratory compaction tests are time-consuming and resource-intensive, necessitating the development of efficient data-driven prediction models.
  • Existing prediction models often lack sufficient accuracy and interpretability for practical geotechnical applications.

Purpose of the Study:

  • To develop and validate a hybrid modeling framework integrating the Rao-1 metaheuristic optimization algorithm with Artificial Neural Network (ANN), Random Forest (RF), and Gradient Boosting (GB) models.
  • To enhance the predictive accuracy and reduce errors in estimating MDD and OMC using soil gradation properties and Atterberg limits.
  • To improve model interpretability by integrating SHapley Additive exPlanations (SHAP) and the Cosine Amplitude Method (CAM).

Main Methods:

  • A dataset of 397 soil samples was utilized, characterized by gradation properties and Atterberg limits.
  • The Rao-1 metaheuristic algorithm was employed to optimize hyperparameters and network weights for ANN, RF, and GB models.
  • SHAP and CAM were integrated for feature contribution analysis and model interpretability.

Main Results:

  • Rao-1 optimization consistently improved model performance across all algorithms and target variables (MDD and OMC).
  • For MDD prediction, optimized ANN achieved R2 of 0.9277 and RMSE of 0.2865; optimized RF and GB reached R2 of 0.9176 and 0.9213.
  • For OMC prediction, optimized ANN achieved R2 of 0.9245 and RMSE of 0.3071, with significant error reductions also observed for optimized RF and GB models.

Conclusions:

  • The proposed Rao-1-based hybrid framework significantly enhances predictive accuracy and error minimization for MDD and OMC compared to conventional models.
  • Rao-1 optimization demonstrates robustness and effectiveness in data-driven soil compaction modeling, offering a practical decision-support tool.
  • The study provides a valuable method for preliminary estimation of compaction parameters, complementing standardized laboratory testing.