Interpretable Machine Learning-Based Prediction Model for Concrete Cover Separation of FRP-Strengthened RC Beams
Abstract
:1. Introduction
2. Workflow
3. Dataset Construction
3.1. Parameter Selection Criteria
- (1)
- The failure mode of all beams is CCS, and there are no other modes.
- (2)
- The geometrical characteristics and parameters of the beams are described in detail.
- (3)
- The FRP sheets were not pre-stressed.
3.2. Inputs and Outputs
3.3. Description of the Dataset
4. Machine Learning Models
4.1. Linear Regression
4.2. Support Vector Regression
4.3. Backpropagation Neural Network
4.4. Decision Tree
4.5. Random Forest
4.6. XGBoost
4.7. Shapley Additive Explanation
5. Results and Discussion
5.1. Machine Learning Model Construction
5.2. Performance Criteria
5.3. Machine Learning Model Evaluation
5.4. Existing Model Evaluation
6. Parametric Study
7. Conclusions
- (1)
- Of all the machine learning models, XGBoost is the best at predicting CCS, with a better distribution of deviations on both the training and test sets. In addition, the XGBoost model also has the maximum goodness-of-fit, the minimum standard deviation, and the minimum root mean square error on both the training and test sets.
- (2)
- The models proposed by AS and TR55 overestimated the shear force during CCS, while the models of ACI, fib, and most researchers are conservative. In addition, the R2 and CV of these models are not satisfactory. Compared to the above models, XGBoost has a higher R2 (0.95) and a lower CV (16%).
- (3)
- The parameters that have a greater influence on V* are the contribution of the concrete to the shear force, the yield strength of the reinforcement, the concrete strength, and the contribution of the hoop reinforcement where V* is approximately proportional to the contribution of the concrete to the shear force and approximately inversely proportional to the yield strength of the reinforcement and the concrete strength.
- (4)
- In this study, the parameters affecting CCS failure were statistically analyzed based on SHAP. However, mechanism-based analyses are scarce and further research is needed in the future.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Parameter | ds (A1) | B (A2) | f′c (A3) | Ast (A4) | fsy (A5) | Af (A6) | Ef (A7) | M*/V* (A8) | Vus (A9) | Vuc (A10) |
---|---|---|---|---|---|---|---|---|---|---|
Min. | 69 | 100 | 19 | 57 | 350 | 13 | 10 | 0 | 3 | 4 |
Max. | 375 | 400 | 80 | 1272 | 611 | 912 | 271 | 550 | 491 | 182 |
Average | 176 | 139 | 42 | 224 | 481 | 120 | 185 | 128 | 89 | 34 |
Standard deviation | 47% | 35% | 53% | 18% | 79% | 13% | 68% | 23% | 18% | 19% |
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Zheng, S.; Hu, T.; Yu, Y. Interpretable Machine Learning-Based Prediction Model for Concrete Cover Separation of FRP-Strengthened RC Beams. Materials 2024, 17, 1957. https://doi.org/10.3390/ma17091957
Zheng S, Hu T, Yu Y. Interpretable Machine Learning-Based Prediction Model for Concrete Cover Separation of FRP-Strengthened RC Beams. Materials. 2024; 17(9):1957. https://doi.org/10.3390/ma17091957
Chicago/Turabian StyleZheng, Sheng, Tianyu Hu, and Yong Yu. 2024. "Interpretable Machine Learning-Based Prediction Model for Concrete Cover Separation of FRP-Strengthened RC Beams" Materials 17, no. 9: 1957. https://doi.org/10.3390/ma17091957