Compressive strength of masonry structures through metaheuristics optimization algorithms

This study presents a comparative analysis of three nature-inspired algorithms—Black Hole Algorithm (BHA), Earthworm Optimization Algorithm (EWA), and Future Search Algorithm (FSA)—for predicting the compressive strength of masonry structures. Each algorithm was integrated with a Multilayer Perceptron (MLP) model, using a structural dimension, rebound number, ultrasonic pulse velocity, and failure load dataset. The dataset was divided into training (70%) and testing (30%) subsets to evaluate model performance. Root Mean Square Error (RMSE) and the coefficient of determination (R²) were employed as statistical indices to measure accuracy. The BHA-MLP model achieved the best performance, with an RMSE of 0.04731 and an R² of 0.9995 for the training dataset and an RMSE of 0.06537 and an R² of 0.99877 for the testing dataset, securing the highest overall score. FSA-MLP ranked second, demonstrating strong predictive performance, followed by EWAMLP, which performed with lower accuracy but still showed valuable results. The study highlights the potential of using these nature-inspired optimization algorithms to enhance the predictive accuracy of compressive strength in masonry structures, offering insights for engineering and policymaking to improve structural safety and performance.