# Exploiting Data Analytics and Deep Learning Systems to Support Pavement Maintenance Decisions
## 5 Results and Discussion
### Fastai Model Results  
The model was set up using the hyperparameters shown in Section 3.3 and it was run with 80 epochs (model steps) used to reduce variances. For the model, the first step was to utilize the learner within the FastAI library to identify the appropriate learning rate for the training. This was performed within the setup and was based on models carried out to determine appropriate learning rates for machine learning models [75]. This is visualized by the graph in Figure 13, showing the point corresponding to this learning rate to be utilized. The FastAI library has a built-in function that allows for programming a suggested value which can then be utilized directly within the model for more effective training. This process was done with the suggested training rate being highlighted as seen in Figure 13. From this process, a learning rate of approximately 0.02 was used for the model. Figure 12: Ranked feature importance within the dataset. This learning rate was subsequently utilized in the training process. During the training, the validation loss was monitored against the training loss to ensure there was no overfitting. Overfitting can typically be assumed to be taking place if the validation loss is significantly higher than the training loss. This was monitored within the setup with the graph shown in Figure 14. Within the figure, it can be seen that the values stabilized by the end of the training run and the validation losses were not significantly higher than the training losses. The RMSE values were also recorded during the process as shown in Figure 15.  
Figure 14: Loss observed during the training process. Figure 13: Utilization of Learning rate finder within FastAI library. Figure 15: Observed training metric during training. The values of RMSE were also stabilized by the end of the training. The RMSE, as identified in Section 3.4 was used as the metric for assessment of the accuracy of the model. After training was completed, the RMSE observed at the final epoch was 0.469. Using the values of RMSE, the NRMSE was computed using the average of the input dataset to normalize the value and make it interpretable on different scales and to provide clearer interpretation. This resulted in an NRMSE of 0.128, which therefore equates to an accuracy rate of 0.872, meaning the accuracy of the derived model is 87.2%. For comparison and validation of the feature selection, the model was run using all the features from Table 4 and this model achieved an NRMSE of 0.196 indicating an accuracy of 80% showcasing an increase of 7% in performance when using the feature selection, which could prove key in carrying out the correct maintenance at the correct time. It should also be noted that the increase could be more if more features are eliminated from a potentially larger database, eliminating those features that are not key to the dependent variable. Consequently, the model can accurately predict future maintenance sequences. In the model's application, new roads that have not undergone similar interventions would be put into the model to produce an idea of the timeline for intervention. The validation set used, which the model never saw during the training highlights the accuracy of the model. It must be stated that this is where deep learning has its strengths in its ability to search out deeper connections and patterns that are not easy to see and understand [76] and is why the general research has seen rapid growth and is considered more and more for engineering applications. Whilst it is important to mention that the model still shows an error, it is clear that the model is adequate and useful. This is in line with other studies, which have used different forms of artificial intelligence to predict maintenance intervention timelines and metrics.