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Related Experiment Video

Updated: Oct 13, 2025

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
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On Random Subspace Optimization-Based Hybrid Computing Models Predicting the California Bearing Ratio of Soils.

Duong Kien Trong1, Binh Thai Pham1, Fazal E Jalal2

  • 1Faculty of Civil Engineering, University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Hanoi 100000, Vietnam.

Materials (Basel, Switzerland)
|November 13, 2021
PubMed
Summary

This study developed hybrid computing models to predict soil California Bearing Ratio (CBR), a key pavement indicator. The random subspace extra tree model demonstrated the highest accuracy in predicting soil CBR values.

Keywords:
California Bearing Ratioelastic modulusmetaheuristic algorithmsmodulus of subgrade reaction

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Area of Science:

  • Geotechnical Engineering
  • Pavement Engineering
  • Computational Intelligence

Background:

  • The California Bearing Ratio (CBR) is crucial for assessing pavement subgrade material strength.
  • Accurate CBR prediction is vital for efficient pavement design and construction.
  • Existing methods may require extensive laboratory testing, motivating the development of predictive models.

Purpose of the Study:

  • To develop and evaluate hybrid computing models for predicting the California Bearing Ratio (CBR) of soil.
  • To compare the performance of Reduced Error Pruning Trees (REPTs), Random Subsurface-based REPT (RSS-REPT), and RSS-based Extra Tree (RSS-ET) models.
  • To identify the most accurate model for soil CBR prediction using a comprehensive dataset.

Main Methods:

  • Development of three hybrid computing models: REPTs, RSS-REPT, and RSS-ET.
  • Compilation of an experimental database of 214 soil samples with diverse classifications (AASHTO M 145).
  • Utilized input parameters including particle size distribution, Atterberg limits, moisture content, and density for model training.

Main Results:

  • All developed models were trained and validated using a dataset of 214 soil samples.
  • Performance was assessed using metrics like coefficient of determination, relative error, MAE, and RMSE.
  • The RSS-based Extra Tree (RSS-ET) model achieved the highest prediction accuracy among the evaluated models.

Conclusions:

  • Hybrid computing models, particularly those employing random subspace optimization, are effective for predicting soil CBR.
  • The RSS-ET model offers a promising approach for accurate and efficient estimation of soil bearing capacity.
  • This research contributes to improved pavement engineering practices through advanced predictive modeling.