Abstract：Based on the performance test datum of the second graded foundation concrete of the Minjiang Laomukong navigation and hydropower junction project, a hybrid neural network based on convolutional neural network and bidirectional long short-term memory network was established, and attention mechanism was introduced to obtain a model that predicts performance through mix proportion. Subsequently, non-dominated sorting genetic algorithm II(NSGA-II) was used to optimize the parameters of the prediction model, which reduced the prediction error of 28 day compressive strength from 38.54% before optimization to 0.26%, and reduced the prediction error of slump from 0.9% before optimization to 0.23%. Then, based on the optimized prediction model, the mix proportion of concrete was optimized using the NSGA-II algorithm with the maximum 28 day compressive strength and slump of concrete as the optimization objectives. The optimized 28 day compressive strength increased by 13.6% compared with the maximum value obtained from the experiment, and the slump also met the engineering requirements. Finally, experimental verification was conducted on the optimal mix proportion, and the results showed that the error between the theoretical 28 day compressive strength and the actual value of the optimal mix proportion was 2.57%, and the error of slump between the predicted value and the actual value was 0.79%, proving that the method proposed in this paper had good optimization effect
李建华, 刘雪平, 郭家舜. 基于卷积神经网络预测模型的混凝土配合比优化[J]. 水泥杂志, 2025, 0(04): 57-.
LI Jianhua, et al.. Optimization of concrete mix proportion based on convolutional neural network prediction model. Journal of Cement, 2025, 0(04): 57-.
2025, Vol. 0 
