This study investigates the application of artificial neural networks (ANNs) for estimating the compressive strength of clay masonry walls based on the mechanical and geometrical properties of their constituent materials. A multilayer perceptron (MLP) neural network was developed using a hybrid dataset derived from Eurocode 6 empirical formulations and representative commercially available masonry units and mortars, enabling systematic generation of realistic input–output relationships. Input parameters included masonry unit dimensions and compressive strength, mortar compressive strength, masonry unit classification group, and mortar type. Different ANN topologies with ReLU, tanh, and logistic activation functions were analyzed, while training was performed using the Adam optimization algorithm. Model performance was evaluated using MSE, MAE, RMSE, R2, and 5-fold cross-validation. The proposed ANN model achieved high prediction accuracy, with R2 values approaching 0.98 for the optimal configuration. Sensitivity, SHAP, and partial dependence analyses confirmed that the constituent material strengths, together with the masonry unit classification and mortar type, are the most influential inputs, in agreement with the Eurocode 6 formulation. The developed model provides a practical tool for preliminary engineering assessment and rapid comparative analysis of masonry wall configurations, reducing reliance on repetitive empirical calculations. The model is based on a Eurocode 6 synthetic dataset and is intended for predictive approximation and engineering support rather than replacement of experimental testing. In this study, the ANN is explicitly framed as a surrogate model of the Eurocode 6 formulation rather than as a replacement for it: its added value lies in providing a fast, continuously differentiable, and interpretable approximation that enables large-scale parameter exploration, interpretability analysis, and deployment as a real-time decision-support web service. To confirm its robustness, the surrogate was benchmarked against Linear Regression, Random Forest, and XGBoost models on the identical dataset.
Results suggest that, within the present five-fold cross-validation setting and limited-sample dataset, RBF kernel ridge regression captures the nonlinear relationships more effectively than conventional linear models; however, broader generalization should be verified using larger datasets and additional validation.
Yuchen Lin· International Conference on...· 0 citations
Accurate prediction of concrete compressive strength is essential for effective mix design, quality control, and structural performance assessment. Conventional empirical models often exhibit limited accuracy due to the complex and nonlinear interactions among concrete constituents.
This study investigates the applicability of several machine learning models for predicting the compressive strength of concrete using a publicly available experimental dataset comprising 1030 concrete mixtures. Linear regression was adopted as a baseline model and compared with support vector regression, random forest regression, and artificial neural networks.
The performance of machine learning models was meticulously assessed using the coefficient of determination, root mean square error, and mean absolute error. Additionally, the models underwent five-fold cross-validation to evaluate their robustness and generalization capabilities. The results unambiguously demonstrate that machine learning models significantly outperform linear regression models.
Cross-validation results confirm the stability and reliability of the developed models. Feature importance analysis reveals that curing age and cement content are the most influential parameters affecting compressive strength, followed by water content, which is consistent with established concrete material behavior. The findings demonstrate that machine learning models, particularly random forest regression, can serve as effective supporting tools for preliminary concrete mix design and performance evaluation.
S. Rouabah· ITEGAM- Journal of Engineeri...· 0 citations
An integrated framework combining improved optimization algorithms with the Shapley Additive Explanations (SHAP) method is developed, providing a robust scientific basis and a practical computational tool for predicting concrete strength, facilitating the deep integration of machine learning with civil engineering practice.
Juanjuan Quan, Kunlin Liu, Hao Su et al.· Buildings· 1 citation
The proposed f-PICNN-CPOA model provides an effective and robust approach for predicting the shear strength of RC deep beams, and makes it a valuable decision-support tool for structural engineers, contributing to safer design practices and improved performance of reinforced concrete structures in the construction industry.
Saranyasri E, S. P· Journal of Vibration Enginee...· 0 citations
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.
Musthafa 'Abduh Fakhruddin, Sri Winarno, Acun Kardianawati· IDEALIS : InDonEsiA journaL...· 0 citations
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.
R. R. Khasani, Ferry Hermawan, Yuliana Usman· IOP Conference Series: Earth...· 0 citations
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