Estimating the strength of soil stabilized with cement and lime at optimal compaction using ensemble-based multiple machine learning
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View abstract on PubMed
Summary
This summary is machine-generated.This study enhanced cohesive soil strength using cement and lime, with machine learning models accurately predicting unconfined compressive strength (UCS). Gradient Boosting and K-Nearest Neighbors achieved 95% accuracy, highlighting key factors like maximum dry density and consistency limits.
Area Of Science
- Geotechnical Engineering
- Environmental Geotechnics
- Materials Science
Background
- Cohesive soils require stabilization for pavement subgrades and landfill liners when unconfined compressive strength (UCS) is below 200 kN/m².
- Improving mechanical properties is crucial for structural integrity and environmental protection in geotechnical applications.
- Machine learning offers advanced analytical tools for predicting soil behavior and optimizing material properties.
Purpose Of The Study
- To comparatively assess machine learning models for predicting the unconfined compressive strength (UCS) of cohesive soil stabilized with cement and lime.
- To identify the most effective ensemble-based machine learning techniques for soil stabilization analysis.
- To determine the key factors influencing the UCS of reconstituted cohesive soils.
Main Methods
- Utilized ensemble-based machine learning classification (Gradient Boosting, CN2, Naïve Bayes, SVM, SGD, K-NN, Decision Tree, Random Forest) and symbolic regression (ANN, RSM).
- Trained and tested models on 190 experimental data points, considering inputs: cement, lime, liquid limit, plasticity index, optimum moisture content, and maximum dry density.
- Performed correlation matrix and sensitivity analysis to identify influential parameters on UCS.
Main Results
- Gradient Boosting (GB) and K-Nearest Neighbors (K-NN) models achieved the highest accuracy (95%).
- CN2, Support Vector Machine (SVM), and Decision Tree (Tree) models showed approximately 90% accuracy.
- Maximum dry density (MDD), consistency limits (LL, PI), and cement content significantly influenced UCS, while optimum moisture content (OMC) had a negligible impact.
Conclusions
- Ensemble machine learning models, particularly GB and K-NN, are highly effective for predicting the UCS of stabilized cohesive soils.
- Optimal soil stabilization can be achieved by focusing on MDD, consistency limits, and cement content.
- The findings provide a valuable framework for field applications in designing stable geotechnical structures with reconstituted soils.
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