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Predicting the compressive strength of polymer-infused bricks: A machine learning approach with SHAP interpretability
Sathvik Sharath Chandra1, Rakesh Kumar1, Archudha Arjunasamy2
1Department of Civil Engineering, Dayananda Sagar College of Engineering, Bengaluru, 560111, India.
Scientific Reports
|March 8, 2025
Summary
This study developed eco-friendly bricks from waste polymers and used machine learning to predict their strength. Artificial Neural Networks (ANN) and Random Forest models showed high accuracy, with age and fly ash being key factors.
Area of Science:
- Sustainable Construction Materials
- Polymer Waste Management
- Machine Learning Applications
Background:
- Global waste production, especially non-biodegradable polymers, presents significant environmental challenges.
- Repurposing waste polymers into construction materials offers an innovative solution to mitigate environmental impact.
- Developing eco-friendly building materials is crucial for sustainable development.
Purpose of the Study:
- To develop and evaluate eco-friendly bricks using cement, fly ash, M sand, and polypropylene (PP) fibers from waste polymers.
- To predict the compressive strength of these polymer-infused bricks using machine learning models.
- To enhance the interpretability of machine learning models in predicting sustainable construction material properties.
Main Methods:
- Incorporation of waste polypropylene (PP) fibers into cementitious brick mixtures.
- Application of machine learning algorithms including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forest, and AdaBoost.
- Utilizing SHapley Additive exPlanations (SHAP) for model interpretability to identify key input variables.
Main Results:
- ANN and Random Forest models demonstrated superior accuracy in predicting compressive strength, closely matching experimental results.
- The ANN model achieved high R² values (0.99674 training, 0.99576 testing) and low RMSE (0.0151 training, 0.01915 testing).
- SHAP analysis identified 'age' and 'fly ash' as the most influential variables in compressive strength prediction.
Conclusions:
- Machine learning, particularly ANN, reliably predicts the compressive strength of eco-friendly polymer bricks.
- The study demonstrates the successful repurposing of polymer waste into valuable construction materials.
- SHAP analysis provides crucial insights into model behavior, enhancing transparency in predicting sustainable material performance.
Keywords:
AdaBoostArtificial neural networkMix proportionPolymer brickRandom forestSupport vector machineWaste materialsMore Related Videos
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