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  1. Home
  3. Modeling The Influence Of Lime On The Unconfined Compressive Strength Of Reconstituted Graded Soil Using Advanced Machine Learning Approaches For Subgrade And Liner Applications.
  1. Home
  3. Modeling The Influence Of Lime On The Unconfined Compressive Strength Of Reconstituted Graded Soil Using Advanced Machine Learning Approaches For Subgrade And Liner Applications.

Related Experiment Video

Soil Lysimeter Excavation for Coupled Hydrological, Geochemical, and Microbiological Investigations
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Modeling the influence of lime on the unconfined compressive strength of reconstituted graded soil using advanced

Xinghuang Guo1, Cesar Garcia2, Alexis Ivan Andrade Valle3,4

  • 1China Design Group, Nanjing City, Jiangsu Province, China.

Plos One
|April 2, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

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Machine learning models predict soil compressive strength for subgrade and landfill design. Response Surface Methodology (RSM) showed superior accuracy over Artificial Neural Networks (ANN) and Evolutionary Polynomial Regression (EPR) for predicting unconfined compressive strength (UCS) in soil-lime mixtures.

Area of Science:

  • Soil Mechanics
  • Geotechnical Engineering
  • Machine Learning Applications

Background:

  • Traditional soil strength testing is equipment-intensive and costly.
  • Machine learning (ML) offers a cost-effective alternative for predicting soil behavior.
  • Soil-lime mixtures are crucial for subgrade and landfill liner applications.

Purpose of the Study:

  • To apply and compare ML techniques for predicting unconfined compressive strength (UCS) of soil-lime mixtures.
  • To evaluate the efficacy of Genetic Programming (GP), Artificial Neural Networks (ANN), Evolutionary Polynomial Regression (EPR), and Response Surface Methodology (RSM).
  • To assess model performance based on soil composition (Gravel, Sand, Silt, Clay, Lime) and curing time (7 and 28 days).

Main Methods:

  • Utilized input variables: Gravel (G), Sand (S), Silt (M), Clay (C), and Lime (L) content.
  • Developed predictive models using GP, ANN, EPR, and RSM.
  • Compared model accuracy using metrics like R2 and average error for 7- and 28-day curing periods.

    • Main Results:

    • ANN and EPR showed similar accuracy for 7-day UCS prediction; GP performed lower.
    • RSM achieved high accuracy (predicted R2 > 98% and 99% for 7 and 28 days, respectively).
    • All input factors, except lime content, showed near-equal importance, highlighting soil gradation's significance.

    Conclusions:

    • ML techniques, particularly RSM, are effective for predicting UCS in soil-lime mixtures.
    • The study validates the use of ML for sustainable subgrade and landfill liner design.
    • Findings offer valuable insights for engineering applications in construction and performance monitoring.