Some road-controlling authorities (RCA) are not allocating enough funding for road renewal works. There is evidence of an increase in reactive maintenance activities to keep the road safe and maintainable.
The irony is that performance indicators currently used by RCAs are pretty deceptive. The nature of pavement deterioration, at the start of failure, only shows a minor decline, providing a false sense of no impact of deferring the maintenance and renewal works and hiding the risk of rapid pavement deterioration that can result in a significant cost increase in bringing the network back to the desired level of service. In addition, experience shows that less-than-normal funding for renewal works can gradually diminish the local asphalt industry, resulting in insufficient capacity to undertake the required volume of work even if the funding increases.
The challenge is identifying rapidly deteriorating sections early and intervening with the appropriate treatment at the right time. To understand the actual condition of the pavement, we have analysed detailed data from the Laser Crack Measurement System (LCMS2) together with captured images processed with in-house developed computer vision machine learning (ML) technology. In addition, we monitor changes in defective areas using ML analysis of video logged during periodically undertaken defect inspections. Combining this data with historical maintenance efforts provides insight into the pavement's performance.
By undertaking advanced predictive life cycle modelling, we can define the magnitude of impact and devise alternative maintenance strategies for managing the road network. Providing evidence of the effect of current reduced funding and optimal maintenance strategy can help the RCAs make informed decisions about their funding strategy for efficient and sustainable network management.
This paper shares our experience of the methodology followed to determine the impact of the current funding level.