Abstract
This research extensively used different progressive machine learning (ML) techniques to predict the compressive strength (CS) and tensile strain (TSt) of engineered cementitious composites (ECC) with 14 input variables and six algorithms. Specifically, random forest (RF), support vector machine, extreme gradient boosting (XGBoost), light gradient boosting machine, categorical gradient boosting (CatBoost), and natural gradient boosting techniques were used in the present study, to understand mechanical properties of ECC meanwhile these properties are crucial for design codes and developing new reliable models for mixtures. The discrepancy between the ML technique and specific ECC expected outputs is novel in this study and will aid researchers in better understanding of ECC features. To estimate the CS and TSt of the ECC, 2535 and 1469 input data points, respectively, were incorporated based on the material ratio, W/B, and different properties of the fibers. In addition, hyperparameter optimization techniques have also been used in ML to improve over fitting and make the model more accurate and robust. Moreover, an error analysis was highlighted between the actual and predicted CS and TSt of the ECC with each ML technique. Also, the significance and influence of the variable inputs that affect the CS and TSt were explained using the Shapley additive explanation (SHAP) approach. Among all approaches, CatBoost and XGBoost predicted the CS and TSt of ECC with greater accuracy than other techniques in terms of the coefficient of determination (R2), mean square error, mean absolute error, root mean square error, and symmetric mean absolute percentage error. The training and testing R2 values of CatBoost and XGBoost for predicting the CS and TSt of ECC were 0.96, 0.89, 0.89, and 0.76, respectively. SHAP analysis revealed that W/B and fiber elongation were the most significant input variables for the CS and TSt of the ECC.
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The authors declare that the data supporting the findings of this study are available within the article.
Abbreviations
- ECC:
-
Engineered cementitious composite
- SCM:
-
Supplementary cementitious materials
- FRC:
-
Fiber reinforced concrete
- OPC:
-
Ordinary Portland cement
- GGBS:
-
Ground granulated blast-furnace slag
- HRWR/B:
-
High range water reducer/binder ratio
- ECS:
-
Experimental compressive strength
- SHCC:
-
Strain hardening cementitious composite
- PVA:
-
Polyvinyl alcohol fiber
- CS:
-
Compressive strength
- PE:
-
Polyethylene fiber
- GF:
-
Glass fiber
- TS:
-
Tensile strength
- PP:
-
Poly propylene fiber
- TSt:
-
Tensile strain
- LAS:
-
Locally available sand
- AR:
-
Aspect ratio
- Fy :
-
Fiber volume
- FA:
-
Fly ash
- SF:
-
Silica fume
- SS:
-
Silica sand
- NS:
-
Natural sand
- σ:
-
Tensile strength
- ρ:
-
Density
- E:
-
Modulus of elasticity
- Δ:
-
Elongation
- W/B:
-
Water binder ratio
- BF:
-
Basalt fiber
- ML:
-
Machine learning
- R2 :
-
Coefficient of determination
- MAPE:
-
Mean absolute percentage error
- WMAPE:
-
Weighted mean absolute percentage error
- SMAPE:
-
Symmetric mean absolute percentage error
- XGBoost:
-
Extreme gradient boosting
- Ngboost:
-
Natural gradient boosting
- LightGBM:
-
Light gradient boosting machine
- CatBoost:
-
Categorical gradient boosting
- SHAP:
-
Shapley additive explanations
- SL:
-
Supervised learning
- OSS:
-
One side sampling
- EFB:
-
Exclusive feature bundling
- MLR:
-
Multiple linear regression
- FR-CNN:
-
Faster region-based convolutional neural networks
- ANN:
-
Artificial neural network
- GP:
-
Genetic programming
- SVM:
-
Support vector machine
- RMSE:
-
Root mean square error
- VA:
-
Variance account
- MAE:
-
Mean absolute error
- MSE:
-
Mean square error
- RSR:
-
Standard deviation ratio
- RF:
-
Random forest
- Ref:
-
References
- ET:
-
Extra tree
- SVM:
-
Support vector machine
- HO:
-
Hyper optimization
- UL:
-
Unsupervised learning
- CNN:
-
Convolutional neural networks
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Acknowledgments
The authors wish to thank Dr. K Prakasan, Principal, PSG College of Technology, Coimbatore, for the facilities and support provided for carrying out this research at Advanced Concrete Research Laboratory and acknowledge Tongji University for providing digital library support through the research papers cited in this work.
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MNU: Writing an original draft, Software, and Visualization. NS: Conceptualization, Data curation, Methodology, Investigation, and Writing an original draft. SP: Writing-review & editing, Supervision. LZL: Writing-review & editing, Supervision.
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Uddin, M.N., Shanmugasundaram, N., Praveenkumar, S. et al. Prediction of compressive strength and tensile strain of engineered cementitious composite using machine learning. Int J Mech Mater Des (2024). https://doi.org/10.1007/s10999-023-09695-0
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DOI: https://doi.org/10.1007/s10999-023-09695-0