Fig. 5: Prediction results of the DNN/MDL model for samples of different categories and freeze-thaw damage levels. | npj Materials Degradation

Fig. 5: Prediction results of the DNN/MDL model for samples of different categories and freeze-thaw damage levels.

From: Multimodal prediction model for concrete freeze-thaw damage based on natural language processing and deep neural network

Fig. 5

a−c represent the absolute errors, root mean square errors (RMSE), and determination coefficients (R2) for mass loss rate (MLR) prediction in different models on normal concrete (NC), fiber reinforced concrete (FRC), and recycled aggregate concrete (RAC) samples. d−f represent the absolute errors, RMSE, and R2 for RDME prediction in different models on NC, FRC, and RAC samples. g−i represent the absolute errors, RMSE, and R2 for MLR prediction in different models for samples with absolute mass change rates <2.5% and >2.5%. j−l represent the absolute errors, RMSE, and R2 for RDME prediction in different models for samples with RDME <0.8 and >0.8.

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