Abstract
This research focuses on predicting Flexural Strength (Fck) in recycled aggregate-based concrete through a comprehensive approach integrating machine learning models and global sensitivity analysis. A dataset comprising 302 samples, including features like Cement, Sand, Natural Fine Aggregate, Recycled Fine Aggregate, Natural Coarse Aggregate, Recycled Coarse Aggregate, Water, W/C (Water/Cement Ratio), and Super Plasticizer, was gathered from existing literature. The study initially preprocesses the dataset, handling outliers through median values and scaling features to a range of 0 to 1. Five prominent machine learning models, namely Support Vector Regressor, Random Forest Regressor, Gradient Boosting Regressor, XGBoost, and Multi-Layer Perceptron, were selected as base models. These models were trained on the preprocessed dataset using fivefold cross-validation, and their performance was evaluated using metrics such as R2 Score, Root Mean Squared Error, Mean Absolute Error, and Mean Absolute Percentage Error. Subsequently, a stacking approach was employed, combining predictions from base models through a Linear Regression meta-model. The stacked model exhibited superior performance, achieving an R2 Score of 0.8826 and outperforming individual models. Visualizations, including radar plots and actual versus predictions plots, emphasized the enhanced accuracy and precision of the stacked model. Global sensitivity analysis highlighted the varying influences of input features, with recycled fine aggregate, water, and cement identified as critical contributors. The study concludes with insights into the relative importance of input features and suggests future directions for refinement and expansion, emphasizing the potential for more sustainable and resilient construction practices in recycled aggregate-based concrete.
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Data Availability
The data used in the current study is available from the corresponding author, upon reasonable request.
Abbreviations
- SVR:
-
Support vector regressor
- RFR:
-
Random forest regressor
- GBR:
-
Gradient boosting regressor
- XGBoost:
-
Extreme gradient boosting regressor
- MLP:
-
Multi-layer perceptron
- RMSE:
-
Root mean squared error
- MAE:
-
Mean absolute error
- MAPE:
-
Mean absolute percentage error
- C&D:
-
Construction and demolition
- RAC:
-
Recycled aggregate concrete
- ANN:
-
Artificial neural network
- BPNN:
-
Back propagation neural network
- MLR:
-
Multiple linear regression
- ICA:
-
Imperialist competitive algorithm
- NCA:
-
Natural coarse aggregates
- NFA:
-
Natural fine aggregates
- LR:
-
Linear regression
- FFNN:
-
Feedforward neural network
- SWR:
-
Stepwise linear regression
- PSO:
-
Particle swarm optimization
- GPR:
-
Gaussian process regression
- SVM:
-
Support vector machine
- KNN:
-
K-nearest neighbors
- RA:
-
Recycled aggregates
- RCA:
-
Recycled coarse aggregate
- NC:
-
Normal concrete
- SF:
-
Silica fume
- IQR:
-
Interquartile range
- Q3:
-
Third quartile
- Q1:
-
First quartile
- LB:
-
Lower bound
- UB:
-
Upper bound
- ST:
-
Sobol total
- S1:
-
First-order sensitivity
- OLS:
-
Ordinary least squares
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Singh, R., Tipu, R.K., Mir, A.A. et al. Predictive Modelling of Flexural Strength in Recycled Aggregate-Based Concrete: A Comprehensive Approach with Machine Learning and Global Sensitivity Analysis. Iran J Sci Technol Trans Civ Eng (2024). https://doi.org/10.1007/s40996-024-01502-w
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DOI: https://doi.org/10.1007/s40996-024-01502-w