Abstract

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.

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