# Exploiting Data Analytics and Deep Learning Systems to Support Pavement Maintenance Decisions
## 5 Results and Discussion
### Fastai Model Results
This is validated in recent studies which have achieved accuracies of: 87% when predicting the infrastructure's condition using a neural network with embedding [77], 86% for predicting road surface milling and overlays interventions [78], 85% when using combinations of gradient boosting trees to predict the PCI [79] and 87% predicting global road performance indicators [29]. Other studies have achieved higher accuracies when using the LTP database [80] and other studies showing that the majority of research using ANN for predictions is done using this database [41]. This is expected given the size and resources used to create that database but does not assist the authorities without similar resources. Given the level of accuracy achieved from this study using the available data and resources, the result represents a substantial advance for the targeted types of road agencies. Additionally, in scenarios where there are no models in place because of the lack of data, this would represent a substantial upgrade to their planning processes. With respect to time, the error would represent less than 1.5 months of time of a gap between when the model predicts maintenance should occur and when it does occur. Considering that plans are made on a yearly scale, this error can, therefore, be considered acceptable as opposed to the alternative where the activity is scheduled in a different year or not at all for a particular road without the model. For a graphical representation of the possible scheduling, a test dataset of 220 road instances was considered and a hypothetical prediction of the required maintenance intervention was plotted for the next 10-year period, grouped by the respective circumscriptions and is shown in Figure 16. This is a prediction plot so the prediction points appear between the year groups as shown in the figure. This figure highlights the time points where the model has predicted that maintenance should occur and should be programmed over the next period of years. Within the figure, it is shown which circumscriptions are critical at which points in time. Using this, a plan of activities can easily be drawn up for the next period and budgets can be planned with a better idea of how much interventions are likely required in a particular year. Additionally, to provide a clearer picture of criticalities, the plot was further grouped to generate a heatmap of points in Figure 16 to understand the important periods and circumscriptions. This was generated using the seaborn library and is given in Figure 17.  
Figure 16: Jointplot of predicted future maintenance interventions grouped by circumscription. Figure 12:0 Heatmap of Jointplot of predicted interventions grouped by circumscription. As expected from the discussions, the plot shows that the period where more action is required are the mid to later year periods but it also importantly highlights the circumscriptions where more intervention is needed, which is critical for planning and budget allocations. Whilst there is room for improvement in the model, it must be emphasized that given the size of the dataset and the resources available, it is a significantly useful result that could help better plan interventions within the municipality. It is also a pathway towards planning interventions without relying on excessive and expensive databases.