Abstract: This work proposed the method of feature variable screening combined with feature variable correlation to optimize the constructed 28-day compressive strength database of lithium-slag concrete, and established random forest model and deep neural network model for testing the database, respectively, and compared the prediction results of the models with three indicators: correlation coefficient (R), root mean square error (RMSE) and mean relative error (MAE). The results show that the improved feature variable screening method can effectively improve the prediction effect of the model when predicting the 28-day compressive strength of lithium-slag concrete, and in addition, the prediction effect of the before-and-after random forest (RF) model with feature variable screening is significantly better than that of the deep neural network (DNN) model.
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