## 4 Prediction of Mechanical Properties of UHPC Based on AOA-SVR
### Preparation of Sample Data  
We meticulously compiled a dataset comprising 1328 samples, which were effectively divided into a training set of 1055 samples and a test set of 273 samples. This partitioning strategy is designed to thoroughly train the model using the training set and subsequently validate the model's generalization ability and predictive accuracy with the test set.  
The high accuracy and flexibility of the AOA-SVR model not only provide a scientific basis for the proportion design of UHPC but also significantly enhance the efficiency and precision of this process. The dataset includes relevant test data collected by the research team, as well as trial configuration data from a civil engineering experimental testing institution, as detailed in Table 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Reference** & **Number of Data** & **Proportion of Data (\%)** \\ \hline
1 & [PERSON] et al. [17] & 471 & 35.47 \\
2 & [PERSON] [18] & 294 & 22.14 \\
3 & [PERSON], et al. [19] & 72 & 5.42 \\
4 & [PERSON], et al. [20] & 59 & 4.44 \\
5 & [PERSON], et al. [21] & 51 & 3.84 \\
6 & [PERSON], et al. [22] & 40 & 3.01 \\
7 & [PERSON], et al. [23] & 30 & 2.26 \\
8 & [PERSON], et al. [24] & 26 & 1.96 \\
9 & [PERSON], et al. [25] & 22 & 1.66 \\
10 & [PERSON], et al. [26] & 20 & 1.51 \\
11 & [PERSON], et al. [27] & 17 & 1.29 \\
12 & [PERSON], et al. [28] & 17 & 1.29 \\
13 & [PERSON], et al. [29] & 16 & 1.20 \\
14 & [PERSON], et al. [30] & 16 & 1.20 \\
15 & [PERSON], et al. [31] & 12 & 0.90 \\
16 & [PERSON], et al. [32] & 12 & 0.90 \\
17 & [PERSON], et al. [33] & 12 & 0.90 \\
18 & [PERSON], et al. [34] & 10 & 0.75 \\
19 & [PERSON], et al. [35] & 10 & 0.75 \\
20 & [PERSON], et al. [36] & 9 & 0.68 \\
21 & [PERSON], et al. [37] & 9 & 0.68 \\
22 & [PERSON], [PERSON], et al. [38] & 9 & 0.68 \\
23 & [PERSON], et al. [39] & 9 & 0.68 \\
24 & [PERSON], et al. [40] & 7 & 0.53 \\
25 & [PERSON], et al. [41] & 7 & 0.53 \\
26 & [PERSON], et al. [42] & 7 & 0.53 \\
27 & [PERSON], [PERSON], et al. [43] & 6 & 0.45 \\
28 & [PERSON], et al. [44] & 6 & 0.45 \\
29 & [PERSON], et al. [45] & 6 & 0.45 \\
30 & [PERSON], et al. [46] & 5 & 0.38 \\
31 & [PERSON], et al. [47] & 5 & 0.38 \\
32 & [PERSON], et al. [48] & 5 & 0.38 \\
33 & [PERSON], et al. [49] & 5 & 0.38 \\
34 & [PERSON], et al. [50] & 5 & 0.38 \\
35 & [PERSON], et al. [51] & 4 & 0.30 \\
36 & [PERSON], et al. [52] & 4 & 0.30 \\
37 & [PERSON], et al. [53] & 3 & 0.23 \\
38 & [PERSON], et al. [54] & 3 & 0.23 \\
39 & [PERSON] [55] & 3 & 0.23 \\
40 & [PERSON], et al. [56] & 2 & 0.15 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Database collections.  
The employment of machine learning techniques to forecast the compressive strength of ultra-high-performance concrete (UHPC) holds substantial importance. Through the creation and substantiation of predictive models, it becomes feasible to promptly and precisely estimate the compressive strength of UHPC, thereby diminishing the duration and financial expenditure associated with conventional experimental approaches. Moreover, such models facilitate the optimization of mix design, bringing it closer to the accurate determination of actual compressive strengths and thereby enhancing the integrity and safety of engineering structures.  
In this subsection, 1328 test data samples from 42 literature sources were employed to train and test the machine learning model for predicting the compressive strength of UHPC. Table 1 provides detailed information on the source references, the number of data points in each reference, and the percentage contribution of each reference to the total dataset. Table 6 lists the database used in this study, which includes seven input variables utilized to construct the machine learning model. These key input parameters are water content, cement content, steel fiber content, fly ash content, silica fume content, slag powder content, and superplasticizers content. Table 6 describes in detail the minimum, maximum, mean, median, and standard deviation of these input variables.