# Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning
## 6 Conclusions and Future Work
It should be noted that the main goal of a PMS is to treat a pavement before serious structural damage has occurred, as the system tries to avoid excessive and corrective maintenance practices; However, given the current state of road networks worldwide and the budgetary issues as exposed by the study, it is simply not possible to ensure this is done on every road in every network. Budgets and time constraints have a major say in this being possible and therefore there must be alternatives within the PMS to ensure that the planning of maintenance and rehabilitation activities are kept optimal to ensure the most value of the available resources. To this end, it is critical that authorities have enough data to make critical decisions on which roads do they carry out maintenance and which should be prioritized. The methodologies and technologies discussed in the study help to bridge this gap providing low-cost but important data for the authority. Whilst it involves monitoring of sections that are already in many cases structurally damaged it does add significantly to the road asset database held by the authority. Additionally, the processes discussed within the paper also provide for hotspot indications of places where there is only minimal severity of distresses (level 1) and this can be monitored over time to give the road authorities data on the sections most likely to suffer significant structural failure in the near future. This allows for structural health monitoring of the pavement to be done and to be effective at a low cost. For assessments of the layers beneath the surface and the structural health of these layers, embedded technologies would have to be utilized. Future work will expand on this and try to provide more points of indication of differing levels of the structural damage to help further bolster this. This analysis is part of an ongoing study which is developing the automation pipeline for the optimization of the PMS. Future work will focus on a more thorough assessment of the pavement distresses with regards to their occurrence, impacts on safety and comfort and the level of traffic in the network. The development of a metric for describing these distresses based on these factors will further aid in the effectiveness of the developed model. This will then take another key step towards the automation of the pavement distress surveys and asset management system. Conceptualization, R.R. ; methodology, R.R., L.I. and G.D.M. ; software, R.R. ; validation, R.R. and G.D.M. ; formal analysis, R.R. and G.D.M. ; investigation, R.R. and G.G. ; resources, G.G. ; data curation, G.G. ; writing--original draft preparation, R.R. ; writing--review and editing, R.R., G.D.M., L.I. ; visualization, R.R. and G.D.M. ; supervision, R.R. and G.D.M. All authors have read and agreed to the published version of the manuscript. The research presented in this paper was carried out as part of the H2020-MSCA-ETN-2016. This project has received funding from the European Union's H2020 Program for research, technological development and demonstration under grant agreement number 721493. Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.