Computer model for mechanical properties of lead-zinc tailings sand concrete

A computer prediction model for mechanical properties of lead-zinc tailings sand concrete was established using finite element analysis and machine learning methods. Basic data were obtained through mechanical property tests including compressive strength, elastic modulus, and stress-strain relationship on concrete specimens with different mix proportions. A BP neural network algorithm was used to construct the prediction model, with tailings sand replacement ratio, water-cement ratio, and age as input variables. The average relative error between model predictions and experimental data was within 5%, verifying the model's reliability. The research results provide theoretical basis and computational tools for engineering applications of lead-zinc tailings sand in concrete.