## 3 Model Construction and Prediction Results
### Deep Learning Model Construction  
In this study, the TensorFlow deep learning platform is used. The hyperparameters that need to be set when establishing the model mainly include the number of hidden layers, the number of neurons, the activation function, the optimizer, the learning rate, the loss function, epoch, and batch_size. For the selection of hidden layers and the number of neurons, too few may lead to underfitting, while too many can easily cause overfitting, resulting in poor generalization ability of the model. The activation function introduces nonlinear factors into the neural network. Common activation functions include sigmoid, tanh, relu, and Lrelu functions. In the process of deep learning back-propagation, the optimizer adjusts the parameters to minimize the loss function and approach the optimal solution. The commonly used optimizers include stochastic gradient descent (SGD), adaptive gradient (AdaGrad), adaptive moment estimation (Adam), root mean square propagation (RMSprop), etc. At present, there are many kinds of activation functions and optimizers, each of which has different characteristics. Selecting inappropriate activation functions and optimizers will have a significant negative impact on model performance. The learning rate determines the degree of change in the current weights during each update step. If it is too large, it may cause gradient oscillation and prevent the model from finding the global optimum; if it is too small, the convergence speed of the model becomes slow, resulting in longer training time. Regularization is used to prevent the model from overfitting. A commonly used method is Dropout regularization, which randomly deactivates a portion of neurons during each training iteration to reduce model complexity and enhance the diversity, sparsity, and generalization of the network. Usually, the model undergoes a certain number of epochs to converge, and an epoch indicates that all the training data have completed a forward computation and back-propagation process in the network. Too small an epoch will lead to insufficient model training, and too large an epoch will lead to overfitting of the model. Usually, the value of the epoch is set to a larger value, and an early stopping strategy is adopted, that is, when the loss of the model on the training set is no longer decreased (the degree of reduction is lower than the predefined threshold), \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature Parameter** & **Unit** & **Maximum** & **Minimum** & **Average** & **Standard Deviation** \\ \hline Cement content & kg/m\({}^{3}\) & 1277.40 & 192.00 & 639.79 & 151.05 \\ Fly ash content & kg/m\({}^{3}\) & 989.57 & 0.00 & 34.60 & 356.14 \\ Slag content & kg/m\({}^{3}\) & 768.00 & 0.00 & 40.45 & 0.00 \\ Silicon powder content & kg/m\({}^{3}\) & 429.53 & 0.00 & 99.97 & 0.00 \\ Nano-silica content & kg/m\({}^{3}\) & 43.70 & 0.00 & 5.39 & 12.15 \\ Limestone powder content & kg/m\({}^{3}\) & 1058.20 & 0.00 & 50.66 & 0.00 \\ Sand content & kg/m\({}^{3}\) & 1503.40 & 407.80 & 951.13 & 265.05 \\ Coarse aggregate content & kg/m\({}^{3}\) & 1298.61 & 0.00 & 371.01 & 545.00 \\ Quartz powder content & kg/m\({}^{3}\) & 259.00 & 0.00 & 14.26 & 0.00 \\ Water content & kg/m\({}^{3}\) & 286.00 & 90.00 & 173.35 & 16.60 \\ Dosage of high-range & kg/m\({}^{3}\) & 92.04 & 0.00 & 24.32 & 5.45 \\ water reducer & \% & 8.00 & 0.00 & 1.28 & 0.75 \\ Steel fiber diameter & mm & 1.00 & 0.00 & 0.22 & 0.28 \\ Steel fiber length & mm & 60.00 & 0.00 & 14.57 & 17.50 \\ Age & d & 500.00 & 1.00 & 46.51 & 117.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistical properties of the characteristic parameters. thereby avoiding the negative impact of excessive epochs on the model training process. Batch_size refers to the amount of data transmitted to the model for training in one iteration. If the batch_size is too small, the training speed will be slow and not easy to converge.