## 3 Methodology and Materials
### Dataset Preparation  
To build up an ANN model, accurate experimental data of UCS values of wet-mix shotcrete is a prerequisite. In those experiments, P-O 42.5R cement produced by the Jinchuan mine was used as the binder agent, and the minimum UCS of cement is 42.5 MPa at 28 days. Tap water was used as the mixing water. The aggregate consists of local mungbean stone and rod-mill sand. Local mungbean stone was employed as a coarse aggregate with a specific gravity of 2.7 and maximum size of 10 mm. Rod-mill sand was used as a fine aggregate with a fineness modulus of 3.05 and a specific gravity of 2.65, with maximum size of 4 mm. The gradation curves of fine and coarse aggregates are shown in Figure 3. The admixture of water reducer was Rheoplus 26R produced by BASF Company, Jining, China. Figure 2: Flowchart of the model. The wet-mix shotcrete mixtures were prepared according to the experimental designs listed in Table 1. The wet-mix shotcrete specimens were made according to the standard GB/T 50081-2002 [13; 14]. During the specimen preparation, all the ingredients of wet-mix shotcrete were first mixed fully by using concrete mixer, and then poured into the cube molds (edge length = 100 mm). The excess concrete slurry was removed by the sawing motion of the steel ruler. After 24 h, wet-mix shotcrete specimens were demolded and then cured in a standard curing box at 20 \(\pm\) 2 \({}^{\circ}\)C and 95% humidity for 7 and 28 days. When the target curing time was reached, the UCS tests were performed on wet-mix shotcrete specimens using WES\(-\)100 hydraulic universal testing machine according to the standard GB/T 50081-2002 [13; 14]. In order to ensure the accuracy of UCS values, three replicates were carried out for each experimental design and the mean UCS values were used for constructing the ANN model. In the end, a total of 96 UCS tests were carried out, leading to 32 samples in the dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Cement Content** & \multirow{2}{*}{**Water–Cement**} & **Water Reducer** & **Sand–Coarse** & \multicolumn{2}{c}{**UCS (MPa)**} \\  & **(kg\(\cdot\)m\({}^{-3}\))** & & **Ratio** & **Content (\%)** & **Aggregate Ratio** & **7 d** & **28 d** \\ \hline \(1\) & 420 & 0.42 & 0.65 & 6:4 & 24.3 & 28.3 \\ \(2\) & 420 & 0.45 & 0.80 & 7:3 & 26.3 & 28.9 \\ \(3\) & 420 & 0.48 & 0.95 & 8:2 & 22.9 & 25.5 \\ \(4\) & 420 & 0.51 & 1.10 & 9:1 & 21.0 & 23.4 \\ \(5\) & 440 & 0.42 & 0.80 & 9:1 & 30.3 & 33.3 \\ \(6\) & 440 & 0.45 & 0.95 & 6:4 & 26.3 & 30.9 \\ \(7\) & 440 & 0.48 & 1.10 & 7:3 & 26.9 & 30.5 \\ \(8\) & 440 & 0.51 & 0.65 & 8:2 & 24.5 & 27.8 \\ \(9\) & 460 & 0.42 & 0.95 & 8:2 & 32.5 & 35.5 \\ \(10\) & 460 & 0.45 & 1.10 & 9:1 & 25.6 & 28.7 \\ \(11\) & 460 & 0.48 & 0.65 & 6:4 & 29.0 & 33.0 \\ \(12\) & 460 & 0.51 & 0.80 & 7:3 & 27.4 & 31.5 \\ \(13\) & 480 & 0.42 & 1.10 & 7:3 & 33.5 & 39.4 \\ \(14\) & 480 & 0.45 & 0.65 & 8:2 & 33.6 & 37.5 \\ \(15\) & 480 & 0.48 & 0.80 & 9:1 & 28.3 & 33.2 \\ \(16\) & 480 & 0.51 & 0.95 & 6:4 & 29.5 & 32.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental designs and UCS tests results. Figure 3: Gradation curves for (**a**) fine aggregate and (**b**) coarse aggregate. According to the rules of ANN, the 32 sets of data need to be divided into two parts: the training set and the testing set. Among them, the training set is used for ANN training, and the testing set is used to evaluate the performance of ANN. From the 32 sets of data, 26 sets were randomly selected as the training set and 6 sets were selected as the testing set [15]. As show in Table 1, there is a huge difference in the orders of magnitude between inputs and outputs.