Comparative Study on Hyperparameter Tuning for Predicting Concrete Compressive Strength
Abstract
1. Introduction
2. Methodology
2.1. Data Preprocessing
- Dataset 1 (DS1): This dataset was sourced from the study by Zheng et al. [33]. To reduce dimensionality, 8 out of the 16 original input variables were selected: cement strength (CCS), water (W), cement (C), slag (S), fly ash (F-ash), coarse aggregate (CA), fine aggregate (FA). The output variable is the 28-day compressive strength of concrete (28CS).
- Dataset 2 (DS2): This dataset was obtained from the study by Zhao et al. [34]. Variables not within the scope of this study (e.g., slump, tensile strength of cement) and data points without recorded compressive strength were excluded from the original dataset. The input variables are CCS, curing age (Age), maximum coarse aggregate size (Dmax), stone powder (SP), fine aggregate fineness modulus (FA-FM), water-cement ratio (w/c), W, and sand-aggregate ratio (S/a). The output variable is the compressive strength of concrete (CS).
- Dataset 3 (DS3): This dataset was compiled by the authors from various studies. The input variables include CCS, coarse aggregate-specific gravity (CA-SG), fine aggregate-specific gravity (FA-SG), C, W, w/c, CA, FA. The output variable is 28CS. To eliminate the size effect of the concrete specimen, compressive strengths for Ø100 × 200 mm and Ø150 × 300 mm cylindrical specimens, as well as a 100 mm cube specimen, were converted to equivalent compressive strength for a 150 mm cube specimen [35].
2.2. Model Training and Evaluation
2.3. SHapley Additive exPlanations
3. Results
3.1. Prediction Performance
3.1.1. Dataset 1
3.1.2. Dataset 2
3.1.3. Dataset 3
3.2. Post-Hoc Analysis
3.3. Comparison with Other Studies
4. Summary and Remarks
- In Dataset 1, the application of the search algorithm resulted in a significant improvement in the prediction accuracy of the ML model. The R2 value increased while both RMSE and MAE decreased, indicating an overall enhancement in performance. In addition, models incorporating the search algorithm demonstrated a reduced performance gap between the training and test sets, suggesting a lower risk of overfitting.
- In Dataset 2, although there was no significant change in the R2 metric, improvements in RMSE and MAE indicated a slight enhancement in the prediction performance. This suggests that the search algorithm contributed to reducing prediction errors.
- In Dataset 3, the application of the search algorithm resulted in neither significant improvements in prediction accuracy nor enhancements in overall performance. ML models based on this dataset exhibited a potential risk of overfitting and demonstrated a tendency toward lower prediction stability.
- SHAP provided valuable insights into feature importance and produced results that were generally consistent with empirical knowledge. However, it fell short in explaining the performance degradation observed in certain datasets. This limitation may stem from the SHAP approach of evaluating features independently, which does not capture complex feature interactions or multicollinearity. In addition, the potential presence of noise data and high-dimensional variables could obscure interpretability, particularly when working with small datasets. These challenges suggest that SHAP alone may not fully uncover the underlying causes of poor generalization or instability. To advance explainable AI in this context, future research should investigate the factors limiting the reliability of SHAP and explore complementary methods to improve model interpretability.
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Dataset | Sample Size | Feature | Unit | Min. | Max. | Mean. | STD. | Skew. | Kurt. | Missing |
---|---|---|---|---|---|---|---|---|---|---|
Dataset 1 | 610 | CCS | MPa | 42.9 | 53.2 | 47.9 | 3.8 | −0.149 | −1.52 | 0 |
W | kg/m3 | 143.2 | 166.4 | 153.2 | 6.4 | 0.231 | −1.19 | 0 | ||
C | kg/m3 | 135 | 335 | 231.5 | 43.9 | 0.066 | −0.99 | 0 | ||
S | kg/m3 | 50.0 | 115.0 | 81.7 | 18.0 | 0.391 | −0.97 | 0 | ||
F-ash | kg/m3 | 25.0 | 80.0 | 58.4 | 13.9 | 0.203 | −0.99 | 0 | ||
FA | kg/m3 | 719.1 | 946.4 | 870.1 | 66.4 | −1.075 | −0.19 | 0 | ||
CA | kg/m3 | 896.4 | 1004.7 | 955.2 | 30.4 | 0.508 | −0.84 | 0 | ||
28CS | MPa | 31.2 | 73.0 | 48.7 | 8.9 | 0.428 | −0.42 | 0 | ||
Dataset 2 | 388 | CCS | MPa | 35.5 | 63.4 | 48.0 | 4.7 | 0.401 | 0.79 | 0 |
Age | Day | 1 | 388 | 73 | 95 | 1.936 | 2.74 | 0 | ||
Dmax | mm | 12 | 80 | 30.1 | 13.6 | 2.727 | 8.06 | 10 | ||
SP | % | 0 | 20 | 8.92 | 5.45 | 0.353 | −0.75 | 31 | ||
FA-FM | n.a | 2.2 | 3.5 | 3.03 | 0.26 | −0.746 | 0.15 | 24 | ||
w/c | n.a | 0.30 | 1.01 | 0.47 | 0.10 | 1.027 | 2.87 | 0 | ||
W | kg/m3 | 104 | 291 | 169.9 | 21.2 | −0.662 | 4.16 | 0 | ||
s/a | % | 26 | 54 | 38.0 | 5.78 | 0.966 | 1.05 | 0 | ||
CS | MPa | 4.23 | 96.3 | 55.1 | 19.0 | −0.017 | −0.71 | 0 | ||
Dataset 3 | 371 | CCS | MPa | 32.1 | 67.5 | 49.9 | 8.620 | 0.280 | −0.639 | 0 |
CA-SG | n.a | 2.23 | 2.89 | 2.60 | 0.127 | −0.854 | 0.201 | 28 | ||
FA-SG | n.a | 2.24 | 2.71 | 2.59 | 0.116 | −1.169 | 0.392 | 116 | ||
C | kg/m3 | 250 | 601 | 390.0 | 69.8 | 0.507 | 0.372 | 0 | ||
W | kg/m3 | 108 | 320 | 181.2 | 28.8 | 0.891 | 2.758 | 0 | ||
w/c | n.a | 0.27 | 0.80 | 0.48 | 0.093 | 0.458 | 0.132 | 0 | ||
CA | kg/m3 | 680 | 1366 | 1098.9 | 140.6 | −0.521 | −0.140 | 0 | ||
FA | kg/m3 | 493 | 1160 | 717.7 | 111.6 | 0.879 | 1.052 | 0 | ||
28CS | MPa | 10 | 83.3 | 40.5 | 10.5 | 0.415 | 0.465 | 0 |
Algorithms | Parameter | Range | Selected | ||
---|---|---|---|---|---|
DS1 | DS2 | DS3 | |||
XGB | n_estimators | [100] | n.a | n.a | n.a |
random_state | [42] | ||||
Grid search | n_estimator | [50, 100, 150, 200, 250, 300] | 250 | 150 | 300 |
max_depth | [1, 3, 5, 7, 9, 11, 13, 15] | 1 | 3 | 3 | |
learning_rate | [0.01, 0.1, 0.2, 0.3, 0.4, 0.5] | 0.3 | 0.5 | 0.2 | |
Random search | n_estimator | Randint [50, 300] | 181 | 283 | 175 |
max_depth | Randint [1, 15] | 2 | 3 | 3 | |
learning_rate | Uniform [0.01, 0.49) | 0.230 | 0.119 | 0.455 | |
Bayesian optimization | n_estimator | Integer [50, 300] | 200 | 271 | 300 |
max_depth | Integer [1, 10] | 2 | 3 | 2 | |
learning_rate | Real [0.01, 0.5] prior = ‘log-uniform’) | 0.106 | 0.141 | 0.264 |
Dataset | Search Method | Test Set | Train Set | ||||
---|---|---|---|---|---|---|---|
R2 | RMSE | MAE | R2 | RMSE | MAE | ||
DS1 | NS | 0.858 | 3.647 | 3.228 | 0.953 | 1.838 | 1.345 |
RS | 0.909 | 2.918 | 2.583 | 0.938 | 2.128 | 1.846 | |
GS | 0.911 | 2.881 | 2.548 | 0.915 | 2.478 | 2.165 | |
BS | 0.911 | 2.874 | 2.556 | 0.930 | 2.249 | 1.973 | |
DS2 | NS | 0.950 | 4.022 | 2.659 | 1.000 | 0.401 | 0.136 |
RS | 0.960 | 3.611 | 2.422 | 0.997 | 1.153 | 0.862 | |
GS | 0.959 | 3.619 | 2.398 | 0.999 | 0.688 | 0.478 | |
BS | 0.960 | 3.600 | 2.360 | 0.997 | 1.071 | 0.792 | |
DS3 | NS | 0.800 | 4.613 | 3.556 | 1.000 | 0.171 | 0.030 |
RS | 0.777 | 4.873 | 3.527 | 0.999 | 0.245 | 0.130 | |
GS | 0.799 | 4.619 | 3.415 | 0.999 | 0.375 | 0.256 | |
BS | 0.774 | 4.901 | 3.676 | 0.992 | 0.926 | 0.712 |
Reference | R2 | RMSE | ||||||
---|---|---|---|---|---|---|---|---|
Basic | GS | RS | BS | Basic | GS | RS | BS | |
DS1 (this study)—XGB | 0.858 | 0.911 | 0.909 | 0.911 | 3.647 | 2.881 | 2.918 | 2.874 |
DS1 (this study)—RF | 0.879 | 0.892 | 0.893 | 0.894 | 3.354 | 3.172 | 3.159 | 3.152 |
DS1 (this study)—ANN | 0.794 | 0.913 | 0.914 | 0.911 | 4.384 | 2.847 | 2.831 | 2.877 |
DS2 (this study)—XGB | 0.950 | 0.959 | 0.960 | 0.960 | 4.022 | 3.619 | 3.611 | 3.600 |
DS2 (this study)—RF | 0.934 | 0.936 | 0.932 | 0.938 | 4.628 | 4.543 | 4.685 | 4.490 |
DS2 (this study)—ANN | 0.872 | 0.869 | 0.943 | 0.919 | 6.420 | 6.498 | 4.297 | 5.119 |
DS3 (this study)—XGB | 0.800 | 0.799 | 0.777 | 0.774 | 4.613 | 4.619 | 4.873 | 4.901 |
DS3 (this study)—RF | 0.763 | 0.760 | 0.763 | 0.760 | 5.024 | 5.052 | 5.017 | 5.055 |
DS3 (this study)—ANN | 0.588 | 0.696 | 0.688 | 0.742 | 6.620 | 5.687 | 5.765 | 5.234 |
Zhang et al. [47] | - | 0.795 | 0.795 | 0.807 | - | - | - | - |
Truong et al. [48] | 0.896 | 0.927 | 0.927 | 0.936 | 79.995 | 67.124 | 66.845 | 62.904 |
Lei et al. [49] | - | 0.885 | 0.893 | 0.918 | - | - | - | - |
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Kim, J.; Lee, D. Comparative Study on Hyperparameter Tuning for Predicting Concrete Compressive Strength. Buildings 2025, 15, 2173. https://doi.org/10.3390/buildings15132173
Kim J, Lee D. Comparative Study on Hyperparameter Tuning for Predicting Concrete Compressive Strength. Buildings. 2025; 15(13):2173. https://doi.org/10.3390/buildings15132173
Chicago/Turabian StyleKim, Jeonghyun, and Donwoo Lee. 2025. "Comparative Study on Hyperparameter Tuning for Predicting Concrete Compressive Strength" Buildings 15, no. 13: 2173. https://doi.org/10.3390/buildings15132173
APA StyleKim, J., & Lee, D. (2025). Comparative Study on Hyperparameter Tuning for Predicting Concrete Compressive Strength. Buildings, 15(13), 2173. https://doi.org/10.3390/buildings15132173