Explainable Levenberg Marquardt trained neural network paradigm for forecasting concrete compressive strength.

Machine Learning Prediction of Concrete Compressive Strength: Model Comparison, CatBoost Optimization, and SHAP Interpretation

A comparative framework evaluating nine regression algorithms using the UCI Concrete Compressive Strength dataset, jointly integrating correlation-corrected statistical validation, multi-model Bayesian optimization, and domain-informed feature engineering with SHAP interpretation, rarely combined in prior concrete-strength studies.

Comparative Evaluation of Intelligent Machine Learning Models for Prediction of Uniaxial Compressive Strength of Rocks

Comparative machine learning models for predicting the compressive strength of ultra-high-performance concrete

Ultra-high-performance concrete (UHPC) exhibits exceptional mechanical properties and durability. However, its compressive strength is highly dependent on complex mix design parameters. While traditional experimental techniques and regression-based models are commonly used to evaluate UHPC compressive strength, machine learning approaches offer an efficient alternative for capturing complex nonlinear relationships. This study develops a machine learning–based framework to predict the compressive strength of UHPC and compares the predictive performance of five advanced algorithms: Extremely Randomized Trees (ER), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), CatBoost, and Artificial Neural Network (ANN). A comprehensive experimental database was utilized for training and validation purposes. Among the evaluated models, CatBoost achieved the best predictive performance, with a coefficient of determination (R²) exceeding 0.90, a root mean square error (RMSE) of approximately 4.5 MPa, and a mean absolute error (MAE) of approximately 3.6 MPa. However, subgroup residual analysis showed that the prediction reliability was not uniform across the full strength range. In particular, mixtures with compressive strength ≥180 MPa exhibited larger errors and systematic underprediction, mainly due to the limited number of ultra-high-strength samples in the compiled database. Therefore, the model is more reliable within well-represented strength ranges, while predictions in the ultra-high-strength region should be interpreted with caution. SHAP-based analysis, feature dependency analysis, and both Individual Conditional Expectation (ICE) and Partial Dependence Plots (PDP) were employed. These explainable AI techniques identified key variables and quantified their contributions to the compressive strength of UHPC. The findings demonstrate that interpretable machine learning can support preliminary UHPC mixture assessment by combining predictive performance with physically meaningful insights.

Machine Learning-Based Prediction of Compressive Strength in Basalt Fiber Reinforced Concrete

Accurate prediction of the mechanical strength of Basalt Fiber Reinforced Concrete (BFRC) is critical for structural design, safety assessment, and the advancement of sustainable infrastructure in civil engineering. Traditional prediction methods often fail to capture the nonlinear relationships between BFRC mix proportions and resulting strength characteristics, leading to unreliable estimations. To address this limitation, this study proposes the Optimized Moment Balanced Machine (OMBM), an advanced machine learning model developed to improve the predictive accuracy of BFRC strength parameters. The model was trained and evaluated using key input features, including cement content, silica fume, fly ash, superplasticizer, water, aggregate composition, and fiber property parameters. The performance of the OMBM was benchmarked against four established machine learning models, such as Least Squares Support Vector Machine (LSSVM), Backpropagation Neural Network (BPNN), K-Nearest Neighbors (KNN), and Linear Regression (LR). Results from ten-fold cross-validation show that OMBM consistently outperforms the comparison models across five evaluation metrics. It achieved the lowest RMSE (2.411), MAE (1.788), and MAPE (4.08%), along with the highest values for correlation coefficient (R = 0.978), and coefficient of determination (R2 = 0.956). Furthermore, the OMBM achieved a Reference Index (RI) score of 1.000, which confirms its position as the leading predictive model within this comparative framework. These results confirm the robustness and reliability of the proposed OMBM model, making it a highly effective tool for accurate strength prediction of BFRC. This approach offers significant potential for the advancement of sustainable infrastructure by enabling more accurate and efficient use of concrete materials.

Explicit Modeling of Compressive Strength in Manufactured Sand Concrete Based on Integrated Machine Learning Approaches

To address the limitations of traditional BP neural networks in predicting manufactured sand concrete strength, specifically their susceptibility to local optima and “black-box” opacity, this study developed an integrated framework combining improved optimization algorithms with the Shapley Additive Explanations (SHAP) method. Using a dataset of 375 data points, genetic algorithm-back propagation (GA-BP) and GOOSE-BP prediction models were developed, with AutoFeat employed for explicit model construction based on a SHAP feature analysis. The results demonstrate that the GOOSE-BP model significantly outperformed traditional methods, achieving an R2 of 0.916 and reducing prediction errors by 47.5%. The SHAP analysis identified paste thickness and stone powder content as the primary determinants of strength. Key thresholds were established, including a water-to-binder ratio sensitivity range of 0.35–0.50, an optimal stone powder content of 80–110 kg/m3, and a recommended sand ratio of 0.38–0.45. By converting complex nonlinear mappings into interpretable explicit expressions, this study provides a robust scientific basis and a practical computational tool for predicting concrete strength, facilitating the deep integration of machine learning with civil engineering practice.

Interpretable machine learning for predicting splitting strength of asphalt concrete: insights from SHAP analysis

An interpretable machine-learning framework for predicting the splitting strength of asphalt concrete and supporting data-driven mixture design and a GUI platform integrating prediction and SHAP-based explanation was developed to improve the accessibility and practical applicability of the proposed framework.