Research on the optimization of mortar mix proportion based on neural network models and genetic algorithm

Strength and durability of concrete are critical performance indicators for the safety and service life of building structures. These properties are significantly influenced by the material proportions and their microstructure. Traditional methods for designing concrete mix ratios have certain limitations when dealing with complex multivariable relationships. Therefore, intelligent mix optimization techniques have become a key focus of current research. This paper presents an optimization approach for mortar mix design based on a multi-output neural network model with a multi-head attention mechanism, combined with the genetic algorithm. Firstly, a neural network model based on the multi-head attention mechanism is developed to establish a nonlinear mapping relationship between material proportions and performance. The genetic algorithm is then applied to optimize the model's predictions, yielding the optimal mix design. Finally, by converting the optimized mix design data into element ion ratios parameters, the correlation between these microscopic factors and cementitious materials durability is analyzed. Results show that the neural network model effectively captures complex nonlinear relationships, with the predicted strength and durability closely aligning with experimental data. The mix ratio optimized by the genetic algorithm significantly improves the strength and durability of the mortar. Furthermore, the study of ion content provides new theoretical support for enhancing concrete durability. This research not only offers an innovative solution for the intelligent optimization of concrete mix design but also lays a theoretical foundation for concrete material design and performance enhancement.