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Compressive strength prediction of high-strength oil palm shell lightweight aggregate concrete using machine learning
Saeed Ghanbari1, Amir Ali Shahmansouri1, Habib Akbarzadeh Bengar2
1Department of Civil Engineering, University of Mazandaran, Babolsar, Iran.
Summary
Agricultural waste in concrete reduces environmental impact. Machine learning models accurately predict the compressive strength of concrete made with oil palm shell aggregate, with gene expression programming showing superior performance.
Area of Science:
- Civil Engineering
- Materials Science
- Environmental Science
Background:
- Sustainable development necessitates eco-friendly construction materials.
- Agricultural byproducts, like oil palm shell, offer potential as sustainable aggregates in concrete.
- Predicting concrete properties is crucial for its practical application.
Purpose of the Study:
- To develop predictive models for the compressive strength of high-strength lightweight aggregate concrete using oil palm shell.
- To compare the performance of various machine learning and regression techniques for this prediction task.
- To assess the robustness and accuracy of these models for potential integration into construction codes.
Main Methods:
- Utilized data from 229 concrete samples incorporating oil palm shell as lightweight aggregate.
- Employed Gene Expression Programming (GEP), Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), and Multiple Linear Regression (MLR) for model development.
- Included nine input parameters: water-to-binder ratio, cement, fly ash, silica fume, fine aggregate, natural coarse aggregate, oil palm shell content, superplasticizer, and specimen age.
- Performed uncertainty analysis using Monte Carlo Simulation (MCS).
Main Results:
- All developed models effectively predicted the compressive strength of concrete containing oil palm shell aggregate.
- The Gene Expression Programming (GEP) model demonstrated superior performance compared to ANFIS, ANN, and MLR models.
- Uncertainty analysis confirmed the reliability of the prediction results.
Conclusions:
- Machine learning models, particularly GEP, are highly effective for predicting the compressive strength of eco-friendly concrete utilizing agricultural waste.
- The developed models offer a robust and accurate tool for the design and application of sustainable concrete.
- These findings support the wider adoption of agricultural waste-based concrete in the construction industry and future codes of practice.
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