## 5 Results and Discussion
### Data Splitting Ratio Analysis in SVM--dot Model  
One of the most important decisions to be made when developing a prediction model is to make full use of existing experimental data to optimize the prediction model. The most common technique is to divide the data into two--typically referred to as the sets of training and testing. The training set is used to build models and is used as the substratum feature set to estimate parameters, compare models, and all other activities necessary to develop a complete model. The test collection is applied to determine the final and objective evaluation only at the end of these activities. Before this point, it is important that the test set is not used. Looking at the results of the test sets would skew the results because the test data were part of the process of model creation. Improper splitting of the dataset will result in the output of the model being excessively high. Nonetheless, various sophisticated methods of sampling were used to address this problem. In addition, an important aspect of data splitting is how well the training, testing, and validation datasets describe the feature space when the number of points in the whole dataset is large during when any division ratio would work, but when the dataset is limited, division ratio may play a crucial role [82; 83; 84]. The SVM--dot model from Section 5.1, is selected for further analysis. When a specific test set was not available, the split validation operator was implemented to predict the model's fit to a hypothetical test set. The split validation operator can also train on one set of data and check on another set of specific test data. The purpose of splitting data into two different categories in this prediction model was to avoid over- and under-fitting and only optimizing the training dataset accuracy. Hence, there is a need for a model that performs well on a dataset that it has never seen (test data), which is called generalization. In this case, the training data and test data are contributed randomly by seven specimens (not from the same specimen) with different detailing, load-carrying capacity, different maximum drift ratio, etc. The test data and training data from every specimen are independent from each other as they have different designs. This procedure has assured the pattern of input-output data during the testing is different than the pattern used during the training. Figure 11 shows three different results based on linear sampling for the split ratio between training and testing of 70:30, 75:25, and 80:20 from the prediction model. Results show that the split ratio of 70:30 with the lowest error of predicted crack widths (25%) near the reference line. It is, thus, a better split ratio for the SVM--dot than 75:25 (30%) and 80:20 (48%) of split ratios. Most of the plotted predicted points for the 80:20 split ratio were far below the equity/measured line, due to unbalanced data from a smaller number of datasets, thus, providing high variance in prediction, which can significantly change testing accuracy. In other words, significant under-fitting in the 80:20 split ratio may cause redundancy in experimental output data. Past studies have shown that the proportion chosen in the analysis for fewer numbers of datasets (\(\leq\)1000 data) was 70% for the training set and 30% for the test. The idea is that more training data is preferred because it makes the classification and regression models better, whilst more test data makes the error estimate less accurate [85; 86; 87]. For this dataset, the 70:30 split is within this experimental range and is a reasonable choice. The trade-off is simple as less the testing data, bigger the variance performance of the model algorithm, while more the training data, smaller would be the variance in parameter estimates. From this 70:30 split ratio graph, further optimizations are analyzed and discussed based on the number of input parameters selected in the following sub-section.